Knitro user options

Knitro has a great number and variety of user option settings and although it tries to choose the best settings by default, often significant performance improvements can be realized by choosing some non-default option settings.

Index

User options are defined in the knitro.h and summarized in the following index. To see a more detailed description of an individual option and its possible values click on the option name. The importance of each option is related to its category (General, Derivatives, etc…), 1 being the most important parameters.

General options

Option name	Importance	Purpose
`algorithm`	1	Indicates which algorithm to use to solve the problem
`blasoption`	2	Specifies the BLAS/LAPACK function library to use for basic vector and matrix computations
`blasoptionlib`	3	Specifies a dynamic library name that contains object code for BLAS/LAPACK functions
`bndrange`	3	Specifies max limits on the magnitude of constraint and variable bounds
`cg_maxit`	2	Determines the maximum allowable number of inner conjugate gradient (CG) iterations
`cg_pmem`	3	Specifies number of nonzero elements per hessian column when computing preconditioner
`cg_precond`	2	Specifies whether or not to apply preconditioning during CG iterations in barrier algorithms
`cg_stoptol`	3	Relative stopping tolerance for CG subproblems
`convex`	1	Identify convex models and apply specializations often beneficial for convex models
`cpuplatform`	2	Specifies the target instruction set architecture for the machine on which Knitro is running
`datacheck`	2	Specifies whether to perform more extensive data checks
`delta`	3	Specifies the initial trust region radius scaling factor
`eval_cost`	2	Specifies the relative cost of performing callback evaluations
`eval_fcga`	3	Specifies that gradients are provided together with functions in one callback
`honorbnds`	1	Indicates whether or not to enforce satisfaction of simple variable bounds
`initpenalty`	3	Initial penalty value used in Knitro merit function
`initpt_strategy`	2	Specifies the initial point strategy used for the continuous algorithms
`initptfile`	3	Specifies a file from which to read the initial point
`linesearch_maxtrials`	3	Indicates the maximum allowable number of trial points during the linesearch
`linesearch`	2	Indicates which linesearch strategy to use for the Interior/Direct or SQP algorithm
`linsolver`	1	Indicates which linear solver to use to solve linear systems arising in Knitro algorithms
`linsolver_maxitref`	3	Specifies the maximum number of iterative refinement steps when solving the linear system
`linsolver_nodeamalg`	3	Controls node amalgamation for MA57, MA86 and MA97 linear system solvers
`linsolver_ooc`	3	Indicates whether to use Intel MKL PARDISO out-of-core solve of linear systems
`linsolver_ordering`	2	Specifies ordering method for linear system solvers
`linsolver_pivottol`	3	Specifies the initial pivot threshold used in factorization routines
`linsolver_scaling`	2	Enables scaling for linear system solvers
`ncvx_qcqp_init`	2	Initialization strategy for non-convex QPs and QCQPs
`objrange`	3	Specifies the extreme limits of the objective function for purposes of determining unboundedness
`restarts`	2	Specifies whether to enable automatic restarts
`restarts_maxit`	3	Maximum number of iterations before restarting when restarts are enabled
`scale`	1	Specifies whether to perform problem scaling
`scale_vars`	2	Specifies the strategy for variable scaling
`soc`	3	Specifies whether or not to try second order corrections (SOC)
`strat_warm_start`	2	Specifies whether or not to invoke a warm-start strategy

Derivatives options

Option name	Importance	Purpose
`bfgs_scaling`	2	Specifies the initial scaling for the BFGS or L-BFGS Hessian approximation
`derivcheck`	1	Determine whether or not to perform a derivative check on the model
`derivcheck_terminate`	3	Determine whether or not to terminate after the derivative check
`derivcheck_tol`	3	Specifies the relative tolerance used for detecting derivative errors
`derivcheck_type`	3	Specifies whether to use forward or central finite differencing for the derivative checker
`findiff_estnoise`	2	Enable noise estimation procedure when computing finite-difference gradients
`findiff_relstepsize`	2	Specifies a relative stepsize when computing finite-difference gradients
`gradopt`	1	Specifies how to compute the gradients of the objective and constraint functions
`hessian_no_f`	3	Determines whether or not to allow Knitro to request Hessian evaluations without the objective component included.
`hessopt`	1	Specifies how to compute the (approximate) Hessian of the Lagrangian
`lmsize`	2	Specifies the number of limited memory pairs stored when approximating the Hessian

Termination options

Option name	Importance	Purpose
`feastol`	1	Specifies the final relative stopping tolerance for the feasibility error
`feastol_abs`	1	Specifies the final absolute stopping tolerance for the feasibility error
`findiff_terminate`	2	Specifies the termination criteria when using finite-difference gradients
`fstopval`	2	Used to implement a custom stopping condition based on the objective function value
`ftol`	2	The optimization process will terminate if feasible and the relative change in the objective function is less than ftol
`ftol_iters`	3	Number of consecutive feasible iterations where the relative change in the objective function is less than ftol before Knitro stops
`infeastol`	2	Specifies the (relative) tolerance used for declaring infeasibility of a model
`infeastol_iters`	3	Stop if number of consecutive infeasible iterations where the relative change in the feasibility error is less than infeasftol reaches this value
`maxfevals`	2	Specifies the maximum number of function evaluations before termination.
`maxit`	1	Specifies the maximum number of iterations before termination
`maxtime`	1	Specifies, in seconds, the maximum allowable real time before termination
`opttol`	1	Specifies the final relative stopping tolerance for the KKT (optimality) error
`opttol_abs`	1	Specifies the final absolute stopping tolerance for the KKT (optimality) error
`soltype`	2	Specifies the type of solution returned by Knitro
`xtol`	1	The optimization process will terminate if the relative change of the solution point estimate is less than xtol
`xtol_iters`	3	Number of consecutive iterations where change of the solution point estimate is less than xtol before Knitro stops

Presolver options

Option name	Importance	Purpose
`presolve`	1	Determine whether or not to use the Knitro presolver
`presolve_initpt`	2	Controls whether Knitro presolver can shift user-supplied initial point
`presolve_level`	2	Knitro presolve level to enable
`presolve_passes`	2	Number of passes through the Knitro presolver
`presolve_tol`	3	Determines the tolerance used by the Knitro presolver
`presolveop_redundant`	2	Presolve operation to control detection and removal of redundant constraints
`presolveop_substitution`	2	Controls variable substitution in the Knitro presolver
`presolveop_substitution_tol`	3	Tolerance for applying substitutions
`presolveop_tighten`	2	Determine whether bounds are tightened by the Knitro presolver

Barrier options

Option name	Importance	Purpose
`bar_conic_enable`	1	Enable special treatments for conic constraints in the Interior/Direct algorithm
`bar_directinterval`	1	Controls the maximum number of consecutive conjugate gradient (CG) steps
`bar_feasible`	1	Specifies whether special emphasis is placed on getting and staying feasible
`bar_feasmodetol`	3	Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible
`bar_globalize`	2	Specifies the globalization strategy used
`bar_initmu`	2	Specifies the initial value for the barrier parameter $\mu$ used
`bar_initpi_mpec`	3	Specifies the initial value for the MPEC penalty parameter $\pi$
`bar_initpt`	2	Indicates initial point strategy for x, slacks and multipliers
`bar_linsys`	2	Indicates linear system form to use for Interior/Direct algorithm
`bar_linsys_storage`	2	Indicates linear system storage approach to use for Interior/Direct algorithm
`bar_maxcorrectors`	2	Specifies the maximum number of corrector steps allowed for primal-dual steps
`bar_maxcrossit`	3	Specifies the maximum number of crossover iterations before termination
`bar_maxmu`	3	Maximum allowed barrier parameter value
`bar_maxrefactor`	3	Indicates the maximum number of refactorizations of the KKT system per iteration
`bar_mpec_heuristic`	3	Enables a heuristic approach when solving MPEC models
`bar_murule`	1	Indicates which strategy to use for modifying the barrier parameter $\mu$
`bar_penaltycons`	2	Indicates whether a penalty approach is applied to the constraints
`bar_penaltyrule`	3	Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate
`bar_refinement`	3	Specifies whether to try to refine the barrier solution for better precision
`bar_relaxcons`	2	Indicates whether a relaxation approach is applied to the constraints
`bar_slackboundpush`	3	Indicates minimum amount by which initial slack variables are pushed inside the bounds
`bar_switchobj`	3	Indicates objective function used when the barrier algorithms switch to a pure feasibility phase
`bar_switchrule`	3	Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase
`bar_watchdog`	3	Specifies whether to enable watchdog heuristic

Active-set options

Option name	Importance	Purpose
`act_lpalg`	3	Indicates which algorithm to use for linear programming (LP) subproblems
`act_lpfeastol`	3	Feasibility tolerance for the linear programming solver in the Knitro Active Set or SQP algorithms
`act_lppenalty`	1	Indicate whether to use penalty formulation for linear programming subproblems
`act_lppresolve`	3	Controls presolve for linear programming subproblems (only when using Cplex or Xpress)
`act_lpsolver`	1	Indicates which linear programming solver the Knitro Active Set or SQP algorithms use
`act_parametric`	2	Solve parametric linear programming subproblems instead of standard LPs
`act_qpalg`	1	Indicates which algorithm to use to solve quadratic programming (QP) subproblems
`act_qppenalty`	2	Indicate whether to use penalty formulation for quadratic programming subproblems in SQP
`cplexlibname`	3	Name of the Xpress library when act_lpsolver=KN_ACT_LPSOLVER_CPLEX
`xpresslibname`	3	Name of the Xpress library when act_lpsolver=KN_ACT_LPSOLVER_XPRESS

MIP options

Option name	Importance	Purpose
`mip_branchrule`	1	Specifies which branching rule to use for MIP branch-and-bound procedure
`mip_clique`	2	Specifies rules for adding clique cuts
`mip_cut_flowcover`	2	Specifies rules for adding flow cover cuts
`mip_cut_probing`	2	Specifies rules for adding probing cuts
`mip_cutfactor`	2	Specifies a limit on the number of cuts added to a node subproblem
`mip_cutoff`	3	Specifies the MIP objective cutoff value
`mip_cutting_plane`	2	Specifies how to apply the cutting plane procedure
`mip_debug`	2	Specifies debugging level for MIP solution
`mip_gomory`	1	Specifies rules for adding Gomory mixed-integer cuts
`mip_gub_branch`	3	Specifies whether or not to branch on generalized upper bounds (GUBs)
`mip_heuristic_diving`	1	Specifies whether to enable the diving heuristic
`mip_heuristic_feaspump`	1	Specifies whether to enable the feasibility pump heuristic
`mip_heuristic_lns`	2	Specifies whether to enable large neighborhood search (LNS) heuristics
`mip_heuristic_localsearch`	1	Specifies whether to enable the local search heuristic
`mip_heuristic_maxit`	2	Specifies the maximum number of iterations to allow for MIP heuristics
`mip_heuristic_misqp`	3	Specifies whether to enable the MISQP heuristic
`mip_heuristic_mpec`	1	Specifies whether to enable the MPEC heuristic
`mip_heuristic_strategy`	1	Specifies the global strategy and effort for MIP heuristics
`mip_heuristic_terminate`	2	Specifies the condition for terminating the MIP heuristic
`mip_implications`	2	Specifies whether or not to add constraints to the MIP derived from logical implications
`mip_integer_tol`	3	Specifies the threshold for deciding whether or not a variable is determined to be an integer
`mip_intvar_strategy`	2	Specifies how to handle integer variables
`mip_knapsack`	2	Specifies rules for adding MIP knapsack cuts
`mip_liftproject`	2	Adds lift and project cuts for MIP
`mip_lpalg`	2	Specifies which algorithm to use for any linear programming (LP) subproblem solves
`mip_maxnodes`	2	Specifies the maximum number of nodes explored (0 means no limit)
`mip_maxsolves`	3	Specifies the maximum number of subproblem solves allowed (0 means no limit)
`mip_method`	1	Specifies which MIP method to use
`mip_mir`	2	Specifies rules for adding mixed integer rounding cuts
`mip_multistart`	3	Enable multi-start at the branch-and-bound level
`mip_nodealg`	1	Specifies which algorithm to use for standard node subproblem solves in MIP
`mip_numthreads`	1	Specifies the number of threads for the MIP branch-and-bound method
`mip_opt_gap_abs`	1	The absolute optimality gap stop tolerance for MIP
`mip_opt_gap_rel`	1	The relative optimality gap stop tolerance for MIP
`mip_outinterval`	1	Specifies node printing interval for mip_outlevel when mip_outlevel > 0
`mip_outlevel`	1	Specifies how much MIP information to print
`mip_outsub`	3	Specifies MIP subproblem solve debug output control
`mip_pseudoinit`	3	Specifies the method used to initialize pseudo-costs
`mip_relaxable`	2	Specifies whether integer variables are relaxable
`mip_restart`	2	Specifies whether to enable the MIP restart procedure
`mip_rootalg`	2	Specifies which algorithm to use for the root node solve in MIP
`mip_rounding`	2	Specifies the MIP rounding rule to apply
`mip_selectdir`	2	Specifies the MIP node selection direction rule for choosing the next node in the branch-and-bound tree
`mip_selectrule`	1	Specifies the MIP select rule for choosing the next node in the branch-and-bound tree
`mip_strong_candlim`	3	Specifies the maximum number of candidates to explore for MIP strong branching
`mip_strong_level`	3	Specifies the maximum number of tree levels on which to perform MIP strong branching
`mip_strong_maxit`	3	Specifies the maximum number of iterations to allow for MIP strong branching solves
`mip_sub_maxtime`	3	Specifies the maximum allowable real time in seconds for the MIP node subproblems
`mip_terminate`	1	Specifies conditions for terminating the MIP algorithm
`mip_zerohalf`	2	Specifies rules for adding zero-half cuts

Multi-algorithm options

Option name	Importance	Purpose
`ma_outsub`	2	Enable writing algorithm output to files for the multi-algorithm procedure
`ma_sub_maxtime`	3	Specifies the maximum allowable real time before termination for the multi-algorithm subproblems
`ma_terminate`	1	Define the termination condition for the multi-algorithm procedure

Multi-start options

Option name	Importance	Purpose
`ms_enable`	1	Indicates whether Knitro will solve from multiple start points to find a better local minimum
`ms_initpt_cluster`	2	Specifies the clustering strategy used to select initial points for multi-start
`ms_maxbndrange`	2	Specifies the maximum range that an unbounded variable can take when determining new start points
`ms_maxsolves`	1	Specifies how many start points to try in multi-start
`ms_num_to_save`	2	Specifies the number of distinct feasible points to save in a file named
`ms_numthreads`	1	Specify the number of threads to use for multi-start
`ms_outsub`	2	Enable writing algorithm output to files for the parallel multi-start procedure
`ms_savetol`	2	Specifies the tolerance for deciding if two feasible points are distinct
`ms_seed`	2	Seed value used to generate random initial points in multi-start
`ms_startptrange`	1	Specifies the maximum range that each variable can take when determining new start points
`ms_sub_maxtime`	3	Specifies, in seconds, the maximum allowable real time before termination for the multi-start subproblems
`ms_terminate`	1	Specifies the condition for terminating multi-start
`ms_terminaterule_tol`	3	The tolerance in (0,1] for the rule-based termination of multi-start

Parallelism options

Option name	Importance	Purpose
`blas_numthreads`	2	Specifies the number of threads to use for BLAS operations
`concurrent_evals`	1	Determines whether or not function and derivative evaluations can take place concurrently in parallel
`conic_numthreads`	2	Specifies the number of threads to use for operations in the conic solver
`findiff_numthreads`	2	Specifies the number of threads to use for finite-difference gradients
`linsolver_numthreads`	2	Specifies the number of threads to use for linear system solve operations
`mip_numthreads`	1	Specifies the number of threads for the MIP branch-and-bound method
`numthreads`	1	Specifies the number of threads to use for altogether for any parallel computing features

Output options

Option name	Importance	Purpose
`debug`	2	Controls the level of debugging output
`newpoint`	2	Specifies additional action to take after every iteration in a solve of a continuous problem
`out_csvinfo`	3	Specifies whether to create knitro_solve.csv information file
`out_csvname`	3	Specify non-default filename when using `out_csvinfo`
`out_hints`	2	Print diagnostic hints (e.g. on user option settings) after solving
`outappend`	2	Specifies whether output should be started in a new file, or appended to existing files
`outdir`	2	Specifies a single directory as the location to write all output files
`outlev`	1	Controls the level of output produced by Knitro
`outmode`	1	Specifies where to direct the output from Knitro
`outname`	2	Specify filename (default knitro.log) when directing output to a file via `outmode`

Tuner options

Option name	Importance	Purpose
`tuner`	1	Indicates whether to invoke the Knitro-Tuner
`tuner_optionsfile`	1	Can be used to specify the location of a Tuner options file
`tuner_outsub`	2	Enable writing additional Tuner subproblem solve output to files for the Knitro-Tuner procedure
`tuner_sub_maxtime`	3	Specifies the maximum allowable real time before terminating for Knitro-Tuner subproblems
`tuner_terminate`	1	Define the termination condition for the Knitro-Tuner procedure

General options

type algorithm

type alg

type KN_PARAM_ALG

#define KN_PARAM_ALGORITHM            1003
#define KN_PARAM_ALG                  1003
#  define KN_ALG_AUTOMATIC               0
#  define KN_ALG_AUTO                    0
#  define KN_ALG_BAR_DIRECT              1
#  define KN_ALG_BAR_CG                  2
#  define KN_ALG_ACT_CG                  3
#  define KN_ALG_ACT_SQP                 4
#  define KN_ALG_MULTI                   5

Indicates which algorithm to use to solve the problem

0 (auto) let Knitro automatically choose an algorithm, based on the problem characteristics.
1 (direct) use the Interior/Direct algorithm.
2 (cg) use the Interior/CG algorithm.
3 (active) use the Active Set algorithm.
4 (sqp) use the SQP algorithm.
5 (multi) run all algorithms, perhaps in parallel (see Algorithms).

Default value: 0

type blasoption

type KN_PARAM_BLASOPTION

#define KN_PARAM_BLASOPTION           1042
#  define KN_BLASOPTION_AUTO            -1
#  define KN_BLASOPTION_KNITRO           0
#  define KN_BLASOPTION_INTEL            1
#  define KN_BLASOPTION_DYNAMIC          2
#  define KN_BLASOPTION_BLIS             3
#  define KN_BLASOPTION_APPLE            4

Specifies the BLAS/LAPACK function library to use for basic vector and matrix computations.

-1 (auto) Let Knitro automatically choose which BLAS to use.
0 (knitro) Use Knitro built-in functions.
1 (intel) Use Intel Math Kernel Library (MKL) functions on available platforms.
2 (dynamic) Use the dynamic library specified with option blasoptionlib.
3 (blis) Use BLIS functions on available platforms (currently not available on Windows OS).
4 (apple) Use Apple Accelerate (only available on Mac with M1 processor).

Default value: -1

Note

BLAS and LAPACK functions from Intel Math Kernel Library (MKL) are provided with the Knitro distribution. The number of threads to use for the MKL BLAS are specified with blas_numthreads. On platforms where Intel MKL and Apple Accelerate are not available, the Knitro built-in functions are used by default.

BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage) functions are used throughout Knitro for fundamental vector and matrix calculations. The CPU time spent in these operations can be measured by setting option debug = 1 and examining the output file kdbg_profile*.txt. Some optimization problems are observed to spend very little CPU time in BLAS/LAPACK operations, while others spend more than 50%. Be aware that the different function implementations can return slightly different answers due to roundoff errors in double precision arithmetic. Thus, changing the value of blasoption sometimes alters the iterates generated by Knitro, or even the final solution point.

The Knitro option uses built-in BLAS/LAPACK functions based on standard netlib routines (www.netlib.org). The intel option uses MKL functions written especially for x86 and x86_64 processor architectures. On a machine running an Intel processor, testing indicates that the MKL functions can significantly reduce the CPU time in BLAS/LAPACK operations. The dynamic option allows users to load any library that implements the functions declared in the file include/blas_lapack.h. Specify the library name with option blasoptionlib.

type blasoptionlib

type KN_PARAM_BLASOPTIONLIB

#define KN_PARAM_BLASOPTIONLIB        1045

Specifies a dynamic library name that contains object code for BLAS/LAPACK functions.

The library must implement all the functions declared in the file include/blas_lapack.h.

Note

This option has no effect unless blasoption = 2.

type bndrange

type KN_PARAM_BNDRANGE

#define KN_PARAM_BNDRANGE             1112

Specifies max limits on the magnitude of constraint and variable bounds. Any constraint or variable bounds whose magnitude is greater than or equal to bndrange will be treated as infinite by Knitro. Using very large, finite bounds is discouraged (and is generally an indication of a poorly scaled model).

Default value: 1.0e20

type cg_maxit

type KN_PARAM_CG_MAXIT

#define KN_PARAM_CG_MAXIT             1013

Determines the maximum allowable number of inner conjugate gradient (CG) iterations per Knitro minor iteration.

-1

Let Knitro automatically determine a value.
0 Knitro will set a maximum value based on the problem size.
n At most n>0 CG iterations may be performed during one minor iteration of Knitro.

Default value: -1

type cg_pmem

type KN_PARAM_CG_PMEM

#define KN_PARAM_CG_PMEM              1103

Specifies the amount of nonzero elements per column of the Hessian of the Lagrangian which are retained when computing the incomplete Cholesky preconditioner.

n At most n>0 nonzero elements per column.

Default value: 10

type cg_precond

type KN_PARAM_CG_PRECOND

#define KN_PARAM_CG_PRECOND           1041
#  define KN_CG_PRECOND_NONE             0
#  define KN_CG_PRECOND_CHOL             1

Specifies whether an incomplete Cholesky preconditioner is applied during CG iterations in barrier algorithms.

0 (no) Not applied.
1 (chol) Preconditioner is applied.

Default value: 0

type cg_stoptol

type KN_PARAM_CG_STOPTOL

#define KN_PARAM_CG_STOPTOL           1099

Specifies the relative stopping tolerance used for the conjugate gradient (CG) subproblem solves.

Default value: 1.0e-2

type convex

type KN_PARAM_CONVEX

#define KN_PARAM_CONVEX               1114
#  define KN_CONVEX_AUTO                -1
#  define KN_CONVEX_NO                   0
#  define KN_CONVEX_YES                  1

Declare the problem as convex by setting KN_CONVEX_YES or non-convex by setting KN_CONVEX_NO. Otherwise, Knitro will try to determine this automatically, but may only be able to do so for simple model forms such as QPs or QCQPs. If your model is specified as (or automatically determined to be) convex, this will cause Knitro to apply specializations and tunings that are often beneficial for convex models to speed up the solution. Currently this option is only active for the Interior/Direct algorithm, but may be applied to other algorithms in the future.

Default value: -1

type cpuplatform

type KN_PARAM_CPUPLATFORM

#define KN_PARAM_CPUPLATFORM          1120
#  define KN_CPUPLATFORM_AUTO           -1
#  define KN_CPUPLATFORM_COMPATIBLE      1
#  define KN_CPUPLATFORM_SSE2            2
#  define KN_CPUPLATFORM_AVX             3
#  define KN_CPUPLATFORM_AVX2            4
#  define KN_CPUPLATFORM_AVX512          5 /* EXPERIMENTAL */

This option can be used to specify the target instruction set architecture for the machine on which Knitro is running. This can be used, for example (especially using the setting KN_CPUPLATFORM_COMPATIBLE), to try to produce more consistent Knitro performance across various architectures (at the expense of, perhaps, slower performance on some platforms). This option is currently only used for the Intel Math Kernal Library (MKL) functions used inside Knitro.

Default value: -1

type KN_PARAM_DATACHECK

#define KN_PARAM_DATACHECK            1087
#  define KN_DATACHECK_NO                0
#  define KN_DATACHECK_YES               1

Specifies whether to perform more extensive data checks to look for errors in the problem input to Knitro (in particular, this option looks for errors in the sparse Jacobian and/or sparse Hessian structure). The datacheck may have a non-trivial cost for large problems. It is turned on by default, but can be turned off for improved speed.

Default value: 1

type delta

type KN_PARAM_DELTA

#define KN_PARAM_DELTA                1020

Specifies the initial trust region radius scaling factor used to determine the initial trust region size.

Default value: 1.0e0

type eval_cost

type KN_PARAM_EVAL_COST

#define KN_PARAM_EVAL_COST            1159
#  define KN_EVAL_COST_UNSPECIFIED       0
#  define KN_EVAL_COST_INEXPENSIVE       1
#  define KN_EVAL_COST_EXPENSIVE         2

Use this option to tell Knitro the relative cost of performing callback (e.g. function, gradient and Hessian) evaluations. Knitro will use this informaton to better tune its algorithms.

Default value: 0

type eval_fcga

type KN_PARAM_EVAL_FCGA

#define KN_PARAM_EVAL_FCGA            1116
#  define KN_EVAL_FCGA_NO                0
#  define KN_EVAL_FCGA_YES               1

Use this option to tell Knitro that you are providing the first derivatives (i.e. gradients) in the same callback routine used for your function evaluations.

Default value: 0

type honorbnds

type KN_PARAM_HONORBNDS

#define KN_PARAM_HONORBNDS            1002
#  define KN_HONORBNDS_AUTO             -1
#  define KN_HONORBNDS_NO                0
#  define KN_HONORBNDS_ALWAYS            1
#  define KN_HONORBNDS_INITPT            2

Indicates whether or not to enforce satisfaction of simple variable bounds throughout the optimization. The API function KN_set_var_honorbnds() can be used to set this option for each variable individually. This option and the bar_feasible option may be useful in applications where functions are undefined outside the region defined by inequalities.

-1 (auto) Knitro automatically determine the best setting.
0 (no) Knitro does not require that the bounds on the variables be satisfied at intermediate iterates.
1 (always) Knitro enforces that the initial point and all subsequent solution estimates satisfy the bounds on the variables.
2 (initpt) Knitro enforces that the initial point satisfies the bounds on the variables.

Default value: -1

Note

Note that setting honorbnds = 1 (always) or 2 (initpt) or using the default auto option may cause Knitro to shift the value of a user-provided initial point so that it lies sufficiently inside the (possibly presolved) bounds. Setting honorbnds = 0 (no) will prevent Knitro from shifting a user-provided initial point.

type initpenalty

type KN_PARAM_INITPENALTY

#define KN_PARAM_INITPENALTY          1097

Specifies the initial penalty parameter used in the Knitro merit functions. The Knitro merit functions are used to balance improvements in the objective function versus improvements in feasibility. A larger initial penalty value places more weight initially on feasibility in the merit function.

Default value: 1.0e1

type initpt_strategy

type KN_PARAM_INITPT_STRATEGY

#define KN_PARAM_INITPT_STRATEGY      1158
#  define KN_INITPT_STRATEGY_AUTO       -1
#  define KN_INITPT_STRATEGY_BASIC       1
#  define KN_INITPT_STRATEGY_ADVANCED    2

Specifies the initial point strategy used for the continuous algorithms. Using a more advanced initial point strategy may produce a better initial point at the cost of more computation.

-1 (auto) Automatically determine the initial point strategy.
1 (basic) Try a basic initial point strategy.
2 (advanced) Try a more advanced initial point strategy.

Default value: -1

type initptfile

type KN_PARAM_INITPTFILE

#define KN_PARAM_INITPTFILE           1167

Specifies a file from which to read the initial point used for the Knitro algorithms. Setting to ‘NULL’ means that no initial point is read from a file.

Default value: NULL

type linesearch

type KN_PARAM_LINESEARCH

#define KN_PARAM_LINESEARCH           1095
#  define KN_LINESEARCH_AUTO             0
#  define KN_LINESEARCH_BACKTRACK        1
#  define KN_LINESEARCH_INTERPOLATE      2
#  define KN_LINESEARCH_WEAKWOLFE        3

Indicates which linesearch strategy to use for the Interior/Direct or SQP algorithm to search for a new acceptable iterate. This option has no effect on the Interior/CG or Active Set algorithm.

0 (auto) Let Knitro automatically choose the strategy.
1 (backtrack) Use a simple backtracking scheme.
2 (interpolate) Use a cubic interpolation scheme.
3 (weakwolfe) Use a linesearch that satisfies the weak Wolfe conditions (unconstrained only).

Default value: 0

type linesearch_maxtrials

type KN_PARAM_LINESEARCH_MAXTRIALS

#define KN_PARAM_LINESEARCH_MAXTRIALS 1044

Indicates the maximum allowable number of trial points during the linesearch of the Interior/Direct or SQP algorithm before treating the linesearch step as a failure and generating a new step.

This option has no effect on the Interior/CG or Active Set algorithm.

Default value: 3

type linsolver

type KN_PARAM_LINSOLVER

#define KN_PARAM_LINSOLVER            1057
#  define KN_LINSOLVER_AUTO              0
#  define KN_LINSOLVER_INTERNAL          1
#  define KN_LINSOLVER_HYBRID            2
#  define KN_LINSOLVER_DENSEQR           3
#  define KN_LINSOLVER_MA27              4
#  define KN_LINSOLVER_MA57              5
#  define KN_LINSOLVER_MKLPARDISO        6
#  define KN_LINSOLVER_MA97              7
#  define KN_LINSOLVER_MA86              8

Indicates which linear solver to use to solve linear systems arising in Knitro algorithms.

0 (auto) Let Knitro automatically choose the linear solver.
1 (internal) Not currently used; reserved for future use. Same as auto for now.
2 (hybrid) Use a hybrid approach where the solver chosen depends on the particular linear system which needs to be solved.
3 (qr) Use a dense QR method. This approach uses LAPACK QR routines. Since it uses a dense method, it is only efficient for small problems. It may often be the most efficient method for small problems with dense Jacobians or Hessian matrices.
4 (ma27) Use the HSL MA27 sparse symmetric indefinite solver.
5 (ma57) Use the HSL MA57 sparse symmetric indefinite solver.
6 (mklpardiso) Use the Intel MKL PARDISO (parallel, deterministic) sparse symmetric indefinite solver.
7 (ma97) Use the HSL MA97 (parallel, deterministic) sparse symmetric indefinite solver.
8 (ma86) Use the HSL MA86 (parallel, non-deterministic) sparse symmetric indefinite solver.

Default value: 0

Note

The QR linear solver, the HSL MA57/MA86/MA97 linear solvers and the Intel MKL PARDISO solver all make frequent use of Basic Linear Algebra Subroutines (BLAS) for internal linear algebra operations. If using any of these it is highly recommended to use optimized BLAS for your particular machine. This can result in dramatic speedup. Please read the notes under the blasoption user option in this section for more details about the BLAS options in Knitro and how to make sure that the Intel MKL BLAS or other user-specified BLAS can be used by Knitro. You may also achieve speedups using multi-threaded BLAS with these solvers by setting numthreads>1 or blas_numthreads>1 when using the solvers.

Additionally, the HSL solvers MA86 and MA97 and the Intel MKL PARDISO solver are specifically designed to exploit parallelism (beyond BLAS parallelism) to achieve speedups on large problems. You may try setting numthreads>1 or linsolver_numthreads>1 (with blas_numthreads =1) when using these solvers, to obtain greater speedups.

type linsolver_maxitref

type KN_PARAM_LINSOLVER_MAXITREF

#define KN_PARAM_LINSOLVER_MAXITREF   1130

Indicates the maximum allowable number of iterative refinement steps applied when a linear system is solved inside Knitro. Iterative refinement steps may be applied when there are significant errors (e.g. large residuals) in the linear system solves. Applying more iterative refinement steps may improve the numerical accuracy of the linear solves at extra cost.

Default value: 2

type linsolver_nodeamalg

type KN_PARAM_LINSOLVER_NODEAMALG

#define KN_PARAM_LINSOLVER_NODEAMALG  1145

Controls the node amalgamation setting for the MA57, MA86 and MA97 linear solvers. A value of 0 indicates that the default value should be used for the given linear solver, while a positive value sets the node amalgamation parameter for the linear solver to that specific value.

Default value: 0

type linsolver_ooc

type KN_PARAM_LINSOLVER_OOC

#define KN_PARAM_LINSOLVER_OOC        1076
#  define KN_LINSOLVER_OOC_NO            0
#  define KN_LINSOLVER_OOC_MAYBE         1
#  define KN_LINSOLVER_OOC_YES           2

Indicates whether to use Intel MKL PARDISO out-of-core solve of linear systems when linsolver = mklpardiso.

This option is only active when linsolver = mklpardiso.

0 (no) Do not use Intel MKL PARDISO out-of-core option.
1 (maybe) Maybe solve out-of-core depending on how much space is needed.
2 (yes) Solve linear systems out-of-core when using Intel MKL PARDISO.

Default value: 0

Note

See the Intel MKL PARDISO documentation for more details on how this option works.

type linsolver_ordering

type KN_PARAM_LINSOLVER_ORDERING

#define KN_PARAM_LINSOLVER_ORDERING   1144
#define   KN_LINSOLVER_ORDERING_AUTO    -1
#define   KN_LINSOLVER_ORDERING_BEST     0
#define   KN_LINSOLVER_ORDERING_AMD      1
#define   KN_LINSOLVER_ORDERING_METIS    2

Sets the ordering method used for the linear system solver.

-1 (auto) Let Knitro automatically choose the ordering strategy.
0 (best) Choose the best between AMD and METIS (try both).
1 (amd) Use AMD ordering (minimum degree for MKL PARDISO).
2 (metis) Use METIS ordering.

Default value: -1

type linsolver_pivottol

type KN_PARAM_LINSOLVER_PIVOTTOL

#define KN_PARAM_LINSOLVER_PIVOTTOL   1029

Specifies the initial pivot threshold used in factorization routines.

The value should be in the range [0, …, 0.5] with higher values resulting in more pivoting (more stable factorizations). Values less than 0 will be set to 0 and values larger than 0.5 will be set to 0.5. If linsolver_pivottol is non-positive, initially no pivoting will be performed. Smaller values may improve the speed of the code but higher values are recommended for more stability (for example, if the problem appears to be very ill-conditioned).

Default value: 1.0e-8

type linsolver_scaling

type KN_PARAM_LINSOLVER_SCALING

#define KN_PARAM_LINSOLVER_SCALING    1156
#  define KN_LINSOLVER_SCALING_NONE      0
#  define KN_LINSOLVER_SCALING_ALWAYS    1
#  define KN_LINSOLVER_SCALING_DYNAMIC   2

Enables scaling for the linear system solver. Applying scaling may allow for more accuracy in the linear system solves, but will generally make the linear system solves more expensive.

0 (none) Do not apply scaling in the linear system solves.
1 (always) Always apply scaling in the linear system solves.
2 (dynamic) Dynamically apply scaling in the linear system solves.

Default value: 0

type ncvx_qcqp_init

type KN_PARAM_NCVX_QCQP_INIT

#define KN_PARAM_NCVX_QCQP_INIT       1139
#  define KN_NCVX_QCQP_INIT_AUTO        -1
#  define KN_NCVX_QCQP_INIT_NONE         0
#  define KN_NCVX_QCQP_INIT_LINEAR       1
#  define KN_NCVX_QCQP_INIT_HYBRID       2
#  define KN_NCVX_QCQP_INIT_PENALTY      3
#  define KN_NCVX_QCQP_INIT_CVXQUAD      4

Specifies the initialization strategy used for non-convex QPs and QCQPs. In particular, these strategies may be more likely to cause Knitro to find global or better local solutions on non-convex quadratic programs (QPs) or non-convex quadratically constrained quadratic programs (QCQPs).

-1 (auto) Knitro will automatically determine the strategy.
0 (none) No special initialization strategy is used.
1 (linear) Initialize by solving a linear relaxation.
2 (hybrid) Initialize by solving a hybrid formulation.
3 (penalty) Initialize by solving a penalty formulation.
4 (cvxquad) Initialize by solving a convex quadratic relaxation.

Default value: -1

type objrange

type KN_PARAM_OBJRANGE

#define KN_PARAM_OBJRANGE             1026

Specifies the extreme limits of the objective function for purposes of determining unboundedness.

If the magnitude of the objective function becomes greater than objrange for a feasible iterate, then the problem is determined to be unbounded and Knitro proceeds no further.

Default value: 1.0e20

type restarts

type KN_PARAM_RESTARTS

#define KN_PARAM_RESTARTS             1100

Specifies whether or not to enable automatic restarts in Knitro. When enabled, if a Knitro algorithm seems to be converging slowly or not converging, the algorithm will automatically restart, which may help with convergence.

0 No automatic restarts allowed.
n At most n>0 automatic restarts may be performed.

Default value: 0

type restarts_maxit

type KN_PARAM_RESTARTS_MAXIT

#define KN_PARAM_RESTARTS_MAXIT       1101

When restarts are enabled, this option can be used to specify a maximum number of iterations before enforcing a restart.

0 No iteration limit on restarts enforced.
n At most n>0 iterations are allowed without convergence before enforcing an automatic restart, if restarts are enabled.

Default value: 0

type scale

type KN_PARAM_SCALE

#define KN_PARAM_SCALE                1017
#  define KN_SCALE_NEVER                 0
#  define KN_SCALE_NO                    0
#  define KN_SCALE_USER_INTERNAL         1
#  define KN_SCALE_USER_NONE             2
#  define KN_SCALE_INTERNAL              3

Specifies whether to perform problem scaling of the objective function, constraint functions, or possibly variables.

If scaling is performed, internal computations, including some aspects of the optimality tests, are based on the scaled values, though the feasibility error is always computed in terms of the original, unscaled values.

0 (no) No scaling is performed.
1 (user_internal) User provided scaling is used if defined, otherwise Knitro internal scaling is applied.
2 (user_none) User provided scaling is used if defined, otherwise no scaling is applied.
3 (internal) Knitro internal scaling is applied.

Default value: 1

type scale_vars

type KN_PARAM_SCALE_VARS

#define KN_PARAM_SCALE_VARS           1153
#  define KN_SCALE_VARS_NONE             0
#  define KN_SCALE_VARS_BNDS             1

Specifies the strategy for scaling variables.

If scaling is performed, internal computations, including some aspects of the optimality tests, are based on the scaled values, though the feasibility error is always computed in terms of the original, unscaled values.

0 (none) No variable scaling is performed.
1 (bnds) Scaling of variables is applied based on their bound values.

Default value: 0

type soc

type KN_PARAM_SOC

#define KN_PARAM_SOC                  1019
#  define KN_SOC_NO                      0
#  define KN_SOC_MAYBE                   1
#  define KN_SOC_YES                     2

Specifies whether or not to try second order corrections (SOC).

A second order correction may be beneficial for problems with highly nonlinear constraints.

0 (no) No second order correction steps are attempted.
1 (maybe) Second order correction steps may be attempted on some iterations.
2 (yes) Second order correction steps are always attempted if the original step is rejected and there are nonlinear constraints.

Default value: 1

type strat_warm_start

type KN_PARAM_STRAT_WARM_START

#define KN_PARAM_STRAT_WARM_START     1118
#  define KN_STRAT_WARM_START_NO         0
#  define KN_STRAT_WARM_START_YES        1

Specifies whether or not to invoke a warm-start strategy.

A warm-start strategy may be beneficial when an initial point close to the solution can be provided. For example, this may occur when solving a sequence of closely related problems, and the solution from one problem can be used to initialize (or warm-start) the next problem in the sequence. The Knitro warm-start strategy will use this information to tune the algorithms to try to converge more quickly in this case. If the initial point is not sufficiently close to the solution, or is too infeasible, the warm-start strategy may not be helpful.

This option is currently only used for the Knitro barrier/interior-point algorithms. In this case it may also be useful to experiment with different (smaller than default) values for the initial barrier parameter bar_initmu. In general, the closer the initial point is to the solution, the smaller this value should be (Knitro will try by default to initialize this to a good value when applying a warm-start strategy).

0 (no) No warm-start strategy is applied.
1 (yes) Knitro will apply a warm-start strategy with special tunings.

Default value: 0

Derivatives options

type bfgs_scaling

type KN_PARAM_BFGS_SCALING

#define KN_PARAM_BFGS_SCALING         1131
#  define KN_BFGS_SCALING_DYNAMIC        0
#  define KN_BFGS_SCALING_INVHESS        1
#  define KN_BFGS_SCALING_HESS           2

Specify the initial scaling to use for the BFGS or L-BFGS Hessian approximation.

0 (dynamic) Dynamically choose which scaling to use.
1 (invhess) The scaling approximates the scale of the inverse Hessian.
2 (hess) The scaling approximates the scale of the Hessian.

Default value: 0

type derivcheck

type KN_PARAM_DERIVCHECK

#define KN_PARAM_DERIVCHECK           1080
#  define KN_DERIVCHECK_NONE             0
#  define KN_DERIVCHECK_FIRST            1
#  define KN_DERIVCHECK_SECOND           2
#  define KN_DERIVCHECK_ALL              3

Determine whether or not to perform a derivative check on the model.

0 (none) Do not perform a derivative check.
1 (first) Check first derivatives only.
2 (second) Check second derivatives (i.e. the Hessian) only.
3 (all) Check both first and second derivatives.

Default value: 0

type derivcheck_terminate

type KN_PARAM_DERIVCHECK_TERMINATE

#define KN_PARAM_DERIVCHECK_TERMINATE 1088
#  define KN_DERIVCHECK_STOPERROR        1
#  define KN_DERIVCHECK_STOPALWAYS       2

Determine whether to always terminate after the derivative check or only when the derivative checker detects a possible error.

1 (error) Terminate only when an error is detected.
2 (always) Always terminate when the derivative check is finished.

Default value: 1

type derivcheck_tol

type KN_PARAM_DERIVCHECK_TOL

#define KN_PARAM_DERIVCHECK_TOL       1082

Specifies the relative tolerance used for detecting derivative errors, when the Knitro derivative checker is enabled.

Default value: 1.0e-6

type derivcheck_type

type KN_PARAM_DERIVCHECK_TYPE

#define KN_PARAM_DERIVCHECK_TYPE      1081
#  define KN_DERIVCHECK_FORWARD          1
#  define KN_DERIVCHECK_CENTRAL          2

Specifies whether to use forward or central finite differencing for the derivative checker when it is enabled.

1 (forward) Use forward finite differencing for the derivative checker.
2 (central) Use central finite differencing for the derivative checker.

Default value: 1

type gradopt

type KN_PARAM_GRADOPT

#define KN_PARAM_GRADOPT              1007
#  define KN_GRADOPT_EXACT               1
#  define KN_GRADOPT_FORWARD             2
#  define KN_GRADOPT_CENTRAL             3

Specifies how to compute the gradients of the objective and constraint functions.

1 (exact) User provides a routine for computing the exact gradients.
2 (forward) Knitro computes gradients by forward finite differences.
3 (central) Knitro computes gradients by central finite differences.

Default value: 1

Note

It is highly recommended to provide exact gradients if at all possible as this greatly impacts the performance of the code.

type hessian_no_f

type KN_PARAM_HESSIAN_NO_F

#define KN_PARAM_HESSIAN_NO_F         1062
#  define KN_HESSIAN_NO_F_FORBID         0
#  define KN_HESSIAN_NO_F_ALLOW          1

Determines whether or not to allow Knitro to request Hessian (or Hessian-vector product) evaluations without the objective component included. If hessian_no_f=0, Knitro will only ask the user for the standard Hessian and will internally approximate the Hessian without the objective component when it is needed. When hessian_no_f=1, Knitro will provide a flag to the user EVALH_NO_F (or EVALHV_NO_F) when it wants an evaluation of the Hessian (or Hessian-vector product) without the objective component. Using hessian_no_f=1 (and providing the appropriate Hessian) may improve Knitro performance on some problems.

This option only has an effect when hessopt=1 (i.e. user-provided exact Hessians), or hessopt=5 (i.e. user-provided exact Hessians-vector products).

0 (forbid) Knitro will not ask for Hessian evaluations without the objective component.
1 (allow) Knitro may ask for Hessian evaluations without the objective component.

Default value: 0

type hessopt

type KN_PARAM_HESSOPT

#define KN_PARAM_HESSOPT              1008
#  define KN_HESSOPT_EXACT               1
#  define KN_HESSOPT_BFGS                2
#  define KN_HESSOPT_SR1                 3
#  define KN_HESSOPT_PRODUCT_FINDIFF     4
#  define KN_HESSOPT_PRODUCT             5
#  define KN_HESSOPT_LBFGS               6
#  define KN_HESSOPT_GAUSS_NEWTON        7

Specifies how to compute the (approximate) Hessian of the Lagrangian.

1 (exact) User provides a routine for computing the exact Hessian.
2 (bfgs) Knitro computes a (dense) quasi-Newton BFGS Hessian.
3 (sr1) Knitro computes a (dense) quasi-Newton SR1 Hessian.
4 (product_findiff) Knitro computes Hessian-vector products using finite-differences.
5 (product) User provides a routine to compute the Hessian-vector products.
6 (lbfgs) Knitro computes a limited-memory quasi-Newton BFGS Hessian (its size is determined by the option lmsize).
7 (gauss_newton) Knitro computes a Gauss-Newton approximation of the hessian (available for least-squares only, and default value for least-squares)

Default value: 1

Note

Options hessopt = 4 and hessopt = 5 are not available with the Interior/Direct or SQP algorithms.

Knitro usually performs best when the user provides exact Hessians (hessopt = 1) or exact Hessian-vector products (hessopt = 5). If neither can be provided but exact gradients are available (i.e., gradopt = 1), then hessopt = 4 may be a good option. This option is comparable in terms of robustness to the exact Hessian option and typically not much slower in terms of time, provided that gradient evaluations are not a dominant cost. However, this option is only available for some algorithms. If exact gradients cannot be provided, then one of the quasi-Newton options is preferred. Options hessopt = 2 and hessopt = 3 are only recommended for small problems (say, n < 1000) since they require working with a dense Hessian approximation. Note that with these last two options, the Hessian pattern will be ignored since Knitro computes a dense approximation. Option hessopt = 6 should be used for large problems.

type lmsize

type KN_PARAM_LMSIZE

#define KN_PARAM_LMSIZE               1038

Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option. The value must be between 1 and 100 and is only used with hessopt = 6.

Larger values may give a more accurate, but more expensive, Hessian approximation. Smaller values may give a less accurate, but faster, Hessian approximation. When using the limited memory BFGS approach it is recommended to experiment with different values of this parameter.

Default value: 10

Termination options

type feastol

type KN_PARAM_FEASTOL

#define KN_PARAM_FEASTOL              1022

Specifies the final relative stopping tolerance for the feasibility error.

Smaller values of feastol result in a higher degree of accuracy in the solution with respect to feasibility.

Default value: 1.0e-6

type feastol_abs

type KN_PARAM_FEASTOLABS

#define KN_PARAM_FEASTOLABS           1023

Specifies the final absolute stopping tolerance for the feasibility error. Smaller values of feastol_abs result in a higher degree of accuracy in the solution with respect to feasibility.

Default value: 1.0e-3

type findiff_estnoise

type KN_PARAM_FINDIFF_ESTNOISE

#define KN_PARAM_FINDIFF_ESTNOISE     1140
#  define KN_FINDIFF_ESTNOISE_NO         0
#  define KN_FINDIFF_ESTNOISE_YES        1
#  define KN_FINDIFF_ESTNOISE_WITHCURV   2

This option can be used to enable an estimate of the noise in the model when using finite-difference gradients. This noise estimate can then be used to set a finite-difference steplength appropriate for the estimated noise level. This can improve performance on models with noise (e.g. noisy black-box optimization models). The cost of the noise estimation procedure is usually a few extra function evaluations.

0 (no) Do not enable any noise estimation procedure for finite-difference gradients.
1 (yes) Enable noise estimation procedure for finite-difference gradients.
2 (withcurv) Enable noise estimation and curvature factor for finite-difference gradients.

Default value: 0

type findiff_relstepsize

type KN_PARAM_FINDIFF_RELSTEPSIZE

#define KN_PARAM_FINDIFF_RELSTEPSIZE  1123

Specifies the relative stepsize used for finite-difference gradients during the optimization. This option sets the stepsize for all variables. The API function KN_set_cb_relstepsizes() can be used to customize the settings for individual variables. Note that this option has no affect on the finite-difference derivatives computed for the derivative checker (default values are always used here). It is only used for the finite-difference derivatives computed during the optimization.

Default value: sqrt(eps) (forward-difference), eps^(1/3) (central difference)

type findiff_terminate

type KN_PARAM_FINDIFF_TERMINATE

#define KN_PARAM_FINDIFF_TERMINATE    1119
#  define KN_FINDIFF_TERMINATE_NONE      0
#  define KN_FINDIFF_TERMINATE_ERREST    1

This option specifies the termination criteria when using finite-difference gradients. The optimality (or KKT) conditions for nonlinear optimization depend on gradient values of the nonlinear objective and constraint functions (see Termination criteria). When using finite-difference gradients (e.g. gradopt > 1), there will typically be small errors in the computed gradients that will limit the precision in the solution (and the ability to satisfy the optimality conditions). By default, Knitro will try to estimate these finite-difference gradient errors and terminate when it seems that no more accuracy in the solution is possible. The solution will be treated as optimal as long as it is feasible and the optimality conditions are satisfied either by the optimality tolerances (opttol and opttol_abs) or the error estimates. On some problems, the error estimates may result in extra function evaluations on some iterations, but will often prevent extra iterations that produce no significant improvement in the solution. This special termination can be disabled by setting findiff_terminate = 0 (none).

0 (none) No special criteria; use the standard stopping conditions.
1 (errest) Allow termination based on estimates of the finite-difference error (when no more significant progress is likely).

Default value: 1

type fstopval

type KN_PARAM_FSTOPVAL

#define KN_PARAM_FSTOPVAL             1086

Used to implement a custom stopping condition based on the objective function value. Knitro will stop and declare that a satisfactory solution was found if a feasible objective function value at least as good as the value specified by fstopval is achieved. This stopping condition is only active when the absolute value of fstopval is less than objrange.

Default value: KN_INFINITY

type ftol

type KN_PARAM_FTOL

#define KN_PARAM_FTOL                 1090

The optimization process will terminate if the relative change in the objective function is less than ftol for ftol_iters consecutive feasible iterations.

Default value: 1.0e-15

type ftol_iters

type KN_PARAM_FTOL_ITERS

#define KN_PARAM_FTOL_ITERS           1091

The optimization process will terminate if the relative change in the objective function is less than ftol for ftol_iters consecutive feasible iterations.

Default value: 5

type infeastol

type KN_PARAM_INFEASTOL

#define KN_PARAM_INFEASTOL            1056

Specifies the (relative) tolerance used for declaring infeasibility of a model.

Smaller values of infeastol make it more difficult to satisfy the conditions Knitro uses for detecting infeasible models. If you believe Knitro incorrectly declares a model to be infeasible, then you should try a smaller value for infeastol.

Default value: 1.0e-8

type infeastol_iters

type KN_PARAM_INFEASTOL_ITERS

#define KN_PARAM_INFEASTOL_ITERS      1124

The optimization process will terminate if the relative change in the feasibility error is less than infeastol for infeastol_iters consecutive infeasible iterations.

Default value: 50

type maxfevals

type KN_PARAM_MAXFEVALS

#define KN_PARAM_MAXFEVALS            1085

Specifies the maximum number of function evaluations before termination. Values less than zero imply no limit.

Default value: -1 (unlimited)

type maxit

type KN_PARAM_MAXIT

#define KN_PARAM_MAXIT                1014

Specifies the maximum number of iterations before termination.

0 Let Knitro automatically choose a value based on the problem type. Currently Knitro sets this value to 10000 for LPs/NLPs and 3000 for MIP problems.
n At most n>0 iterations may be performed before terminating.

Default value: 0

type maxtime

type KN_PARAM_MAXTIME

#define KN_PARAM_MAXTIME              1163

Specifies, in seconds, the maximum allowable real time before termination.

Default value: 1.0e8

type opttol

type KN_PARAM_OPTTOL

#define KN_PARAM_OPTTOL               1027

Specifies the final relative stopping tolerance for the KKT (optimality) error.

Smaller values of opttol result in a higher degree of accuracy in the solution with respect to optimality.

Default value: 1.0e-6

type opttol_abs

type KN_PARAM_OPTTOLABS

#define KN_PARAM_OPTTOLABS            1028

Specifies the final absolute stopping tolerance for the KKT (optimality) error.

Smaller values of opttol_abs result in a higher degree of accuracy in the solution with respect to optimality.

Default value: 1.0e-3

type soltype

type KN_PARAM_SOLTYPE

#define KN_PARAM_SOLTYPE              1161
#  define KN_SOLTYPE_FINAL               0
#  define KN_SOLTYPE_BESTFEAS            1

This option specifies the solution returned by Knitro. Generally, the solution converged to by Knitro is a locally optimal solution that corresponds to the best feasible solution found. However, on rare occasions, Knitro may enounter a feasible solution during the optimization process that has a better objective value than the final solution converged to by Knitro. Setting soltype =1 in this case will return this iterate. This iterate can also be retrieved through the API function KN_get_best_feasible_iterate().

0 (final) Always return the final solution to which Knitro converges.
1 (bestfeas) Always return the best feasible solution encountered during the optimization.

Default value: 0

type xtol

type KN_PARAM_XTOL

#define KN_PARAM_XTOL                 1030

The optimization process will terminate if the relative change in all components of the solution point estimate is less than xtol for xtol_iters. consecutive iterations. If using the Interior/Direct or Interior/CG algorithm and the barrier parameter is still large, Knitro will first try decreasing the barrier parameter before terminating.

Default value: 1.0e-12

type xtol_iters

type KN_PARAM_XTOL_ITERS

#define KN_PARAM_XTOL_ITERS           1094

The optimization process will terminate if the relative change in the solution estimate is less than xtol for xtol_iters consecutive iterations. If set to 0, Knitro chooses this value based on the solver and context. Currently Knitro sets this value to 3 unless the MISQP algorithm is being used, in which case the value is set to 1 by default.

Default value: 0

Presolver options

type presolve

type KN_PARAM_PRESOLVE

#define KN_PARAM_PRESOLVE             1059
#  define KN_PRESOLVE_NO                 0
#  define KN_PRESOLVE_YES                1

Determine whether or not to use the Knitro presolver to try to simplify the model by removing variables or constraints.

0 (no) Do not use the Knitro presolver.
1 (yes) Enable the Knitro presolver.

Default value: 1

type presolve_level

type KN_PARAM_PRESOLVE_LEVEL

#define KN_PARAM_PRESOLVE_LEVEL       1122
#  define KN_PRESOLVE_LEVEL_AUTO        -1
#  define KN_PRESOLVE_LEVEL_1            1
#  define KN_PRESOLVE_LEVEL_2            2

Set the level of presolve operations to enable through the Knitro presolver. A higher presolve level enables more complex presolve operations.

-1 (auto) Let Knitro automatically choose the presolve level.
1 (level1) Enable the most basic presolve operations.
2 (level2) Enable more advanced presolve operations.

Default value: -1

type presolve_initpt

type KN_PARAM_PRESOLVE_INITPT

#define KN_PARAM_PRESOLVE_INITPT      1127
#  define KN_PRESOLVE_INITPT_AUTO       -1
#  define KN_PRESOLVE_INITPT_NOSHIFT     0
#  define KN_PRESOLVE_INITPT_LINSHIFT    1
#  define KN_PRESOLVE_INITPT_ANYSHIFT    2

Control whether the Knitro presolver can shift a user-supplied initial point.

-1 (auto) Let Knitro automatically choose whether to allow shifting.
0 (noshift) Do not allow presolver to shift user-supplied initial point.
1 (linshift) Allow presolver to shift user-supplied initial point if it only appears in linear constraints.
2 (anyshift) Allow presolver to shift any user-supplied initial point.

Default value: -1

type presolve_passes

type KN_PARAM_PRESOLVE_PASSES

#define KN_PARAM_PRESOLVE_PASSES      1121

Set a maximum limit on the number of passes through the Knitro presolve operations.

Default value: 10

type presolve_tol

type KN_PARAM_PRESOLVE_TOL

#define KN_PARAM_PRESOLVE_TOL         1060

Determines the tolerance used by the Knitro presolver to remove variables and constraints from the model. If you believe the Knitro presolver is incorrectly modifying the model, use a smaller value for this tolerance (or turn the presolver off).

Default value: 1.0e-6

type presolveop_redundant

type KN_PARAM_PRESOLVEOP_REDUNDANT

#define KN_PARAM_PRESOLVEOP_REDUNDANT 1143
#  define KN_PRESOLVEOP_REDUNDANT_NONE   0
#  define KN_PRESOLVEOP_REDUNDANT_DUPCON 1
#  define KN_PRESOLVEOP_REDUNDANT_DEPCON 2

Determine whether or not to enable the Knitro presolve operation to detect and remove redundant constraints.

0 (none) Do not remove redundant constraints.
1 (dupcon) Detect and remove duplicate constraints.
2 (depcon) Detect and remove linearly dependent constraints.

Default value: 1

type presolveop_substitution

type KN_PARAM_PRESOLVEOP_SUBSTITUTION

#define KN_PARAM_PRESOLVEOP_SUBSTITUTION 1146
#  define KN_PRESOLVEOP_SUBSTITUTION_AUTO  -1
#  define KN_PRESOLVEOP_SUBSTITUTION_NONE   0
#  define KN_PRESOLVEOP_SUBSTITUTION_SIMPLE 1
#  define KN_PRESOLVEOP_SUBSTITUTION_ALL    2

Determine whether or not to enable the Knitro presolve operation to substitute out variables when possible.

-1 (auto) Automatically determined (may depend on the algorithm).
0 (none) Do not perform any variable substitution.
1 (simple) Enable simple substitutions involving doubleton equality constraints.
2 (all) Enable all possible variable substitutions.

Default value: -1

type presolveop_substitution_tol

type KN_PARAM_PRESOLVEOP_SUBSTITUTION_TOL

    #define KN_PARAM_PRESOLVEOP_SUBSTITUTION_TOL 1147

Tolerance for applying a substitution. This is a relative tolerance on
coefficients involved with the substituted variable. Higher values mean
that less reductions will be applied (potentially improving numerical
focus). Zero value means all possible substitutions are applied.

Default value: 1e-2

type presolveop_tighten

type KN_PARAM_PRESOLVEOP_TIGHTEN

#define KN_PARAM_PRESOLVEOP_TIGHTEN   1125
#  define KN_PRESOLVEOP_TIGHTEN_AUTO    -1
#  define KN_PRESOLVEOP_TIGHTEN_NONE     0
#  define KN_PRESOLVEOP_TIGHTEN_VARBND   1
#  define KN_PRESOLVEOP_TIGHTEN_COEF     2
#  define KN_PRESOLVEOP_TIGHTEN_ALL      3

Determine whether or not to enable the Knitro presolve operation to tighten variable bounds or coefficients.

-1 (auto) Automatically determined (may depend on the algorithm).
0 (none) Do not tighten variable bounds (unless it removes a constraint).
1 (varbnd) Enable tightening variable bounds always.
2 (coef) Enable tightening coefficients in linear constraints.
3 (all) Enable tightening variable bounds and coefficients.

Default value: -1

Barrier options

type bar_conic_enable

type KN_PARAM_BAR_CONIC_ENABLE

#define KN_PARAM_BAR_CONIC_ENABLE     1113
#  define KN_BAR_CONIC_ENABLE_AUTO      -1
#  define KN_BAR_CONIC_ENABLE_NONE       0
#  define KN_BAR_CONIC_ENABLE_SOC        1

Enable special treatments for conic constraints when using the Interior/Direct algorithm (has no affect when using the Interior/CG algorithm).

-1 (auto) Let Knitro automatically choose the strategy.
0 (none) Do not apply any special treatment for conic constraints.
1 (soc) Apply special treatments for any Second Order Cone (SOC) constraints identified in the model.

Default value: -1

type bar_directinterval

type KN_PARAM_BAR_DIRECTINTERVAL

#define KN_PARAM_BAR_DIRECTINTERVAL   1058

Controls the maximum number of consecutive conjugate gradient (CG) steps before Knitro will try to enforce that a step is taken using direct linear algebra.

This option is only valid for the Interior/Direct algorithm and may be useful on problems where Knitro appears to be taking lots of conjugate gradient steps. Setting bar_directinterval to 0 will try to enforce that only direct steps are taken which may produce better results on some problems.

Default value: 10

type bar_feasible

type KN_PARAM_BAR_FEASIBLE

#define KN_PARAM_BAR_FEASIBLE         1006
#  define KN_BAR_FEASIBLE_NO             0
#  define KN_BAR_FEASIBLE_STAY           1
#  define KN_BAR_FEASIBLE_GET            2
#  define KN_BAR_FEASIBLE_GET_STAY       3

Specifies whether special emphasis is placed on getting and staying feasible in the interior-point algorithms.

0 (no) No special emphasis on feasibility.
1 (stay) Iterates must satisfy inequality constraints once they become sufficiently feasible.
2 (get) Special emphasis is placed on getting feasible before trying to optimize.
3 (get_stay) Implement both options 1 and 2 above.

Default value: 0

Note

This option can only be used with the Interior/Direct and Interior/CG algorithms.

If bar_feasible = stay or bar_feasible = get_stay, this will activate the feasible version of Knitro. The feasible version of Knitro will force iterates to strictly satisfy inequalities, but does not require satisfaction of equality constraints at intermediate iterates. This option and the honorbnds option may be useful in applications where functions are undefined outside the region defined by inequalities. The initial point must satisfy inequalities to a sufficient degree; if not, Knitro may generate infeasible iterates and does not switch to the feasible version until a sufficiently feasible point is found. Sufficient satisfaction occurs at a point x if it is true for all inequalities that

$cl + tol \leq c(x) \leq cu - tol$

The constant tol is determined by the option bar_feasmodetol.

If bar_feasible = get or bar_feasible = get_stay, Knitro will place special emphasis on first trying to get feasible before trying to optimize.

type bar_feasmodetol

type KN_PARAM_BAR_FEASMODETOL

#define KN_PARAM_BAR_FEASMODETOL      1021

Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible.

The tolerance applies to all inequality constraints in the problem. This option only has an effect if option bar_feasible = stay or bar_feasible = get_stay.

Default value: 1.0e-4

type bar_globalize

type KN_PARAM_BAR_GLOBALIZE

#define KN_PARAM_BAR_GLOBALIZE        1155
#  define KN_BAR_GLOBALIZE_NONE          0
#  define KN_BAR_GLOBALIZE_KKT           1
#  define KN_BAR_GLOBALIZE_FILTER        2

Specifies the globalization strategy used in the interior-point algorithms.

0 (none) No globalization strategy is applied.
1 (kkt) Apply a globalization strategy based on decreasing the KKT error.
2 (filter) Apply a globalization strategy using a filter based on the objective and constraint violation.

Default value: 2

type bar_initmu

type KN_PARAM_BAR_INITMU

#define KN_PARAM_BAR_INITMU           1025

Specifies the initial value for the barrier parameter $\mu$ used with the barrier algorithms.

This option has no effect on the Active Set algorithm.

Default value: 1.0e-1

type bar_initpi_mpec

type KN_PARAM_BAR_INITPI_MPEC

#define KN_PARAM_BAR_INITPI_MPEC      1093

Specifies the initial value for the MPEC penalty parameter $\pi$ used when solving problems with complementarity constraints using the barrier algorithms. If this value is non-positive, then Knitro uses an internal formula to initialize the MPEC penalty parameter.

Default value: 0.0

type bar_initpt

type KN_PARAM_BAR_INITPT

#define KN_PARAM_BAR_INITPT           1009
#  define KN_BAR_INITPT_AUTO             0
#  define KN_BAR_INITPT_CONVEX           1
#  define KN_BAR_INITPT_NEARBND          2
#  define KN_BAR_INITPT_CENTRAL          3

Indicates initial point strategy for x, slacks and multipliers when using a barrier algorithm. Note, this option only alters the initial x values if the user does not specify an initial x.

This option has no effect on the Active Set algorithm.

0 (auto) Let Knitro automatically choose the strategy.
1 (convex) Initialization designed for convex models.
2 (nearbnd) Initialization strategy that stays closer to the bounds.
3 (central) Initialization strategy that is more central on double-bounded variables.

Default value: 0

type bar_linsys

type KN_PARAM_BAR_LINSYS

#define KN_PARAM_BAR_LINSYS           1126
#  define KN_BAR_LINSYS_AUTO            -1
#  define KN_BAR_LINSYS_FULL             0
#  define KN_BAR_LINSYS_ELIMINATE_SLACKS 1
#  define KN_BAR_LINSYS_ELIMINATE_BOUNDS 2
#  define KN_BAR_LINSYS_ELIMINATE_INEQS  3

Indicates which linear system form is used inside the Interior/Direct algorithm for computing primal-dual steps. Eliminating more elements results in a smaller dimensional linear system (but also one that is, perhaps, less numerically stable). The bounds option may be preferable for very large problems with many bounded variables. The ineq option may generate significant speedups on models where the number of variables is small, but the number of inequality constraints is large.

-1 (auto) Let Knitro automatically choose the linear system form.
0 (full) Use the full linear system.
1 (slacks) Eliminate the slack variables.
2 (bounds) Eliminate the slack variables and bounds.
3 (ineqs) Eliminate the slack variables, bounds, and some inequalities.

Default value: -1

type bar_linsys_storage

type KN_PARAM_BAR_LINSYS_STORAGE

#define KN_PARAM_BAR_LINSYS_STORAGE   1129
#  define KN_BAR_LINSYS_STORAGE_AUTO    -1
#  define KN_BAR_LINSYS_STORAGE_LOWMEM   1
#  define KN_BAR_LINSYS_STORAGE_NORMAL   2

Indicates how to store in memory the linear systems used inside the Interior/Direct algorithm for computing primal-dual steps. The lowmem option uses one storage location for multiple linear systems. As a result it may use much less memory, but also may be less efficient when the Interior/Direct algorithm takes a lot of CG steps. The normal option uses separate storage for different linear systems.

-1 (auto) Let Knitro automatically choose the linear system storage approach.
1 (lowmem) Use common storage for multiple linear systems.
2 (normal) Use separate storage for different linear systems.

Default value: -1

type bar_maxcorrectors

type KN_PARAM_BAR_MAXCORRECTORS

#define KN_PARAM_BAR_MAXCORRECTORS    1117

Specifies the maximum number of corrector steps allowed for primal-dual steps.

If the value is positive and the algorithm used is Interior/Direct, then Knitro may add at most bar_maxcorrectors corrector steps to the primal-dual step to try to stay closer to the central path. This may speedup convergence on some models (although it may make the cost per iteration a little more expensive). If the value is negative, Knitro automatically determines the maximum number of corrector steps to apply.

Default value: -1

type bar_maxcrossit

type KN_PARAM_BAR_MAXCROSSIT

#define KN_PARAM_BAR_MAXCROSSIT       1039

Specifies the maximum number of crossover iterations before termination.

If the value is positive and the algorithm in operation is Interior/Direct or Interior/CG, then Knitro will crossover to the Active Set algorithm near the solution. The Active Set algorithm will then perform at most bar_maxcrossit iterations to get a more exact solution. If the value is 0, no Active Set crossover occurs and the interior-point solution is the final result.

If Active Set crossover is unable to improve the approximate interior-point solution, then Knitro will restore the interior-point solution. In some cases (especially on large-scale problems or difficult degenerate problems) the cost of the crossover procedure may be significant – for this reason, crossover is disabled by default. Enabling crossover generally provides a more accurate solution than Interior/Direct or Interior/CG.

Default value: 0

type bar_maxmu

type KN_PARAM_BAR_MAXMU

#define KN_PARAM_BAR_MAXMU            1154

Specifies the maximum allowable value for the barrier parameter $\mu$ used with the barrier algorithms.

Default value: 1.0e16

type bar_maxrefactor

type KN_PARAM_BAR_MAXREFACTOR

#define KN_PARAM_BAR_MAXREFACTOR      1043

Indicates the maximum number of refactorizations of the KKT system per iteration of the Interior/Direct algorithm before reverting to a CG step. If this value is set to -1, it will use a dynamic strategy.

These refactorizations are performed if negative curvature is detected in the model. Rather than reverting to a CG step, the Hessian matrix is modified in an attempt to make the subproblem convex and then the KKT system is refactorized. Increasing this value will make the Interior/Direct algorithm less likely to take CG steps. If the Interior/Direct algorithm is taking a large number of CG steps (as indicated by a positive value for “CGits” in the output), this may improve performance. This option has no effect on the Active Set algorithm.

Default value: -1

type bar_mpec_heuristic

type KN_PARAM_BAR_MPEC_HEURISTIC

#define KN_PARAM_BAR_MPEC_HEURISTIC   1142
#  define KN_BAR_MPEC_HEURISTIC_NO       0
#  define KN_BAR_MPEC_HEURISTIC_YES      1

Specifies whether or not to use a heuristic approach when solving MPEC models with the barrier algorithm. In some cases enabling this heuristic can speedup the convergence to the solution and provide a more precise solution on MPEC models (i.e., models with complementarity constraints – see Complementarity constraints).

0 (no) Do not enable the heuristic for MPEC models.
1 (yes) Enable the heuristic for MPEC models.

Default value: 0

type bar_murule

type KN_PARAM_BAR_MURULE

#define KN_PARAM_BAR_MURULE           1004
#  define KN_BAR_MURULE_AUTOMATIC        0
#  define KN_BAR_MURULE_AUTO             0
#  define KN_BAR_MURULE_MONOTONE         1
#  define KN_BAR_MURULE_ADAPTIVE         2
#  define KN_BAR_MURULE_PROBING          3
#  define KN_BAR_MURULE_DAMPMPC          4
#  define KN_BAR_MURULE_FULLMPC          5
#  define KN_BAR_MURULE_QUALITY          6

Indicates which strategy to use for modifying the barrier parameter $mu$ in the barrier algorithms.

Not all strategies are available for both barrier algorithms, as described below. This option has no effect on the Active Set algorithm.

0 (auto) Let Knitro automatically choose the strategy.
1 (monotone) Monotonically decrease the barrier parameter. Available for both barrier algorithms.
2 (adaptive) Use an adaptive rule based on the complementarity gap to determine the value of the barrier parameter. Available for both barrier algorithms.
3 (probing) Use a probing (affine-scaling) step to dynamically determine the barrier parameter. Available only for the Interior/Direct algorithm.
4 (dampmpc) Use a Mehrotra predictor-corrector type rule to determine the barrier parameter, with safeguards on the corrector step. Available only for the Interior/Direct algorithm.
5 (fullmpc) Use a Mehrotra predictor-corrector type rule to determine the barrier parameter, without safeguards on the corrector step. Available only for the Interior/Direct algorithm.
6 (quality) Minimize a quality function at each iteration to determine the barrier parameter. Available only for the Interior/Direct algorithm.

Default value: 0

type bar_penaltycons

type KN_PARAM_BAR_PENCONS

#define KN_PARAM_BAR_PENCONS          1050
#  define KN_BAR_PENCONS_AUTO           -1
#  define KN_BAR_PENCONS_NONE            0
#  define KN_BAR_PENCONS_ALL             2
#  define KN_BAR_PENCONS_EQUALITIES      3

Indicates whether a penalty approach is applied to the constraints.

Using a penalty approach may be helpful when the problem has degenerate or difficult constraints. It may also help to more quickly identify infeasible problems, or achieve feasibility in problems with difficult constraints.

This option has no effect on the Active Set algorithm.

-1 (auto) Let Knitro automatically choose the strategy.
0 (none) No constraints are penalized.
2 (all) A penalty approach is applied to all general constraints.
3 (equalities) Apply a penalty approach to equality constraints only.

Default value: -1

type bar_penaltyrule

type KN_PARAM_BAR_PENRULE

#define KN_PARAM_BAR_PENRULE          1049
#  define KN_BAR_PENRULE_AUTO            0
#  define KN_BAR_PENRULE_SINGLE          1
#  define KN_BAR_PENRULE_FLEX            2

Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate. This option has no effect on the Active Set algorithm.

0 (auto) Let Knitro automatically choose the strategy.
1 (single) Use a single penalty parameter in the merit function to weight feasibility versus optimality.
2 (flex) Use a more tolerant and flexible step acceptance procedure based on a range of penalty parameter values.

Default value: 0

type bar_refinement

type KN_PARAM_BAR_REFINEMENT

#define KN_PARAM_BAR_REFINEMENT       1079
#  define KN_BAR_REFINEMENT_NO           0
#  define KN_BAR_REFINEMENT_YES          1

Specifies whether to try to refine the barrier solution for better precision. If enabled, once the optimality conditions are satisfied, Knitro will apply an additional refinement/postsolve phase to try to obtain more precision in the barrier solution. The effect is similar to the effect of enabling bar_maxcrossit, but it is usually much more efficient since it does not involve switching to the Active Set algorithm.

Default value: 0

type bar_relaxcons

type KN_PARAM_BAR_RELAXCONS

#define KN_PARAM_BAR_RELAXCONS        1077
#  define KN_BAR_RELAXCONS_NONE          0
#  define KN_BAR_RELAXCONS_EQS           1
#  define KN_BAR_RELAXCONS_INEQS         2
#  define KN_BAR_RELAXCONS_ALL           3

Indicates whether a relaxation approach is applied to the constraints.

Using a relaxation approach may be helpful when the problem has degenerate or difficult constraints.

This option has no effect on the Active Set algorithm.

0 (none) No constraints are relaxed.
1 (eqs) A relaxation approach is applied to general equality constraints.
2 (ineqs) A relaxation approach is applied to general inequality constraints.
3 (all) A relaxation approach is applied to all general constraints.

Default value: 2

type bar_slackboundpush

type KN_PARAM_BAR_SLACKBOUNDPUSH

#define KN_PARAM_BAR_SLACKBOUNDPUSH   1102

Specifies the amount by which the barrier slack variables are initially pushed inside the bounds. A smaller value may be preferable when warm-starting from a point close to the solution.

Default value: 1.0e-1

type bar_switchobj

type KN_PARAM_BAR_SWITCHOBJ

#define KN_PARAM_BAR_SWITCHOBJ        1104
#  define KN_BAR_SWITCHOBJ_NONE          0
#  define KN_BAR_SWITCHOBJ_SCALARPROX    1
#  define KN_BAR_SWITCHOBJ_DIAGPROX      2

Indicates which objective function to use when the barrier algorithms switch to a pure feasibility phase.

0 (none) No (or zero) objective.
1 (scalarprox) Proximal point objective with scalar weighting.
2 (diagprox) Proximal point objective with diagonal weighting.

Default value: 1

type bar_switchrule

type KN_PARAM_BAR_SWITCHRULE

#define KN_PARAM_BAR_SWITCHRULE       1061
#  define KN_BAR_SWITCHRULE_AUTO        -1
#  define KN_BAR_SWITCHRULE_NEVER        0
#  define KN_BAR_SWITCHRULE_MODERATE     2
#  define KN_BAR_SWITCHRULE_AGGRESSIVE   3

Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase. This option has no effect on the Active Set algorithm.

-1 (auto) Let Knitro determine the switching procedure.
0 (never) Never switch to feasibility phase.
2 (moderate) Allow switches to feasibility phase.
3 (aggressive) Use a more aggressive switching rule.

Default value: -1

type bar_watchdog

type KN_PARAM_BAR_WATCHDOG

#define KN_PARAM_BAR_WATCHDOG         1089
#  define KN_BAR_WATCHDOG_NO             0
#  define KN_BAR_WATCHDOG_YES            1

Specifies whether to enable watchdog heuristic for barrier algorithms. In general, enabling the watchdog heuristic makes the barrier algorithms more likely to accept trial points. Specifically, the watchdog heuristic may occasionally accept trial points that increase the merit function, provided that subsequent iterates decrease the merit function.

Default value: 0

Active-set options

type act_lpalg

type KN_PARAM_ACT_LPALG

#define KN_PARAM_ACT_LPALG            1109
#  define KN_ACT_LPALG_DEFAULT           0
#  define KN_ACT_LPALG_PRIMAL            1
#  define KN_ACT_LPALG_DUAL              2
#  define KN_ACT_LPALG_BARRIER           3

Indicates which algorithm to use to solve linear programming (LP) subproblems when using the Knitro Active Set or SQP algorithms.

The barrier option is currently only active when using the CPLEX(R) or Xpress(R) LP solvers chosen via act_lpsolver.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

0 (default) use the default algorithm for the chosen LP solver.
1 (primal) use a primal simplex algorithm.
2 (dual) use a dual simplex algorithm.
3 (barrier) use a barrier/interior-point algorithm.

Default value: 0

type act_lpfeastol

type KN_PARAM_ACT_LPFEASTOL

#define KN_PARAM_ACT_LPFEASTOL        1098

Specifies the feasibility tolerance used for linear programming subproblems solved when using the Active Set or SQP algorithms.

Default value: 1.0e-8

type act_lppenalty

type KN_PARAM_ACT_LPPENALTY

#define KN_PARAM_ACT_LPPENALTY        1111
#  define KN_ACT_LPPENALTY_ALL           1
#  define KN_ACT_LPPENALTY_NONLINEAR     2
#  define KN_ACT_LPPENALTY_DYNAMIC       3

Indicates whether to use a penalty formulation for linear programming subproblems in the Knitro Active Set or SQP algorithms.

1 (all) penalize all constraints.
2 (nonlinear) penalize only nonlinear constraints.
3 (dynamic) dynamically choose which constraints to penalize.

Default value: 1

type act_lppresolve

type KN_PARAM_ACT_LPPRESOLVE

#define KN_PARAM_ACT_LPPRESOLVE       1110
#  define KN_ACT_LPPRESOLVE_OFF          0
#  define KN_ACT_LPPRESOLVE_ON           1

Indicates whether to apply a presolve for linear programming subproblems in the Knitro Active Set or SQP algorithms.

0 (off) presolve turned off for LP subproblems.
1 (on) presolve turned on for LP subproblems.

Default value: 0

type act_lpsolver

type KN_PARAM_ACT_LPSOLVER

#define KN_PARAM_ACT_LPSOLVER         1012
#  define KN_ACT_LPSOLVER_INTERNAL       1
#  define KN_ACT_LPSOLVER_CPLEX          2
#  define KN_ACT_LPSOLVER_XPRESS         3

Indicates which linear programming simplex solver the Knitro Active Set or SQP algorithms use when solving internal LP subproblems.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

1 (internal) Knitro uses its default LP solver.
2 (cplex) Knitro uses IBM ILOG-CPLEX(R), provided the user has a valid CPLEX license. The CPLEX library is loaded dynamically after KN_solve() is called.
3 (xpress) Knitro uses the FICO Xpress(R) solver, provided the user has a valid Xpress license. The Xpress library is loaded dynamically after KN_solve() is called.

Default value: 1

If act_lpsolver = cplex then the CPLEX shared object library or DLL must reside in the operating system’s load path. If this option is selected, Knitro will automatically look for standard CPLEX library names in the system’s load path (in order of most recent releases starting with CPLEX 12.10).

To override the automatic search and load a particular CPLEX library, set its name with the character type user option cplexlibname. Either supply the full path name in this option, or make sure the library resides in a directory that is listed in the operating system’s load path. For example, to specifically load the Windows CPLEX library cplex123.dll, make sure the directory containing the library is part of the PATH environment variable, and call the following (also be sure to check the return status of this call):

KN_set_char_param_by_name (kc, "cplexlibname", "cplex123.dll");

If act_lpsolver = xpress then the Xpress shared object library or DLL must reside in the operating system’s load path. If this option is selected, Knitro will automatically look for the standard Xpress dll/shared library name.

To override the automatic search and load a particular Xpress library, set its name with the character type user option xpresslibname. Either supply the full path name in this option, or make sure the library resides in a directory that is listed in the operating system’s load path.

type act_parametric

type KN_PARAM_ACT_PARAMETRIC

#define KN_PARAM_ACT_PARAMETRIC       1107
#  define KN_ACT_PARAMETRIC_NO           0
#  define KN_ACT_PARAMETRIC_MAYBE        1
#  define KN_ACT_PARAMETRIC_YES          2

Indicates whether to use a parametric approach when solving linear programming (LP) subproblems when using the Knitro Active Set or SQP algorithms. A parametric approach will solve a sequence of closely related LPs instead of one LP. It may increase the cost of an active-set iteration, but perhaps lead to convergence in fewer iterations.

0 (no) do not use a parametric solve (i.e. solve a single LP).
1 (maybe) use a parametric solve sometimes.
2 (yes) always try a parametric solve.

Default value: 1

type act_qpalg

type KN_PARAM_ACT_QPALG

#define KN_PARAM_ACT_QPALG            1092
#  define KN_ACT_QPALG_AUTO              0
#  define KN_ACT_QPALG_BAR_DIRECT        1
#  define KN_ACT_QPALG_BAR_CG            2
#  define KN_ACT_QPALG_ACT_CG            3

Indicates which algorithm to use to solve quadratic programming (QP) subproblems when using the Knitro Active Set or SQP algorithms.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

0 (auto) let Knitro automatically choose an algorithm, based on the problem characteristics.
1 (direct) use the Interior/Direct algorithm.
2 (cg) use the Interior/CG algorithm.
3 (active) use the Active Set algorithm.

Default value: 0

type act_qppenalty

type KN_PARAM_ACT_QPPENALTY

#define KN_PARAM_ACT_QPPENALTY        1128
#  define KN_ACT_QPPENALTY_AUTO         -1
#  define KN_ACT_QPPENALTY_NONE          0
#  define KN_ACT_QPPENALTY_ALL           1

Indicates whether to use a penalty formulation for quadratic programming subproblems in the Knitro SQP algorithm.

-1 (auto) let Knitro automatically decide.
0 (none) do not penalize constraints in QP subproblems.
1 (all) penalize all constraints in QP subproblems.

Default value: -1

type cplexlibname

type KN_PARAM_CPLEXLIB

#define KN_PARAM_CPLEXLIB             1048

See option act_lpsolver.

type xpresslibname

type KN_PARAM_XPRESSLIB

#define KN_PARAM_XPRESSLIB            1069

See option act_lpsolver.

MIP options

type mip_branchrule

type KN_PARAM_MIP_BRANCHRULE

#define KN_PARAM_MIP_BRANCHRULE       2002
#  define KN_MIP_BRANCH_AUTO             0
#  define KN_MIP_BRANCH_MOSTFRAC         1
#  define KN_MIP_BRANCH_PSEUDOCOST       2
#  define KN_MIP_BRANCH_STRONG           3

Specifies which branching rule to use for MIP branch and bound procedure.

0 (auto) Let Knitro automatically choose the branching rule.
1 (most_frac) Use most fractional (most infeasible) branching.
2 (pseudcost) Use pseudo-cost branching.
3 (strong) Use strong branching (see options mip_strong_candlim, mip_strong_level and mip_strong_maxit for further control of strong branching procedure).

Default value: 0

type mip_clique

type KN_PARAM_MIP_CLIQUE

#define KN_PARAM_MIP_CLIQUE           2038
#  define KN_MIP_CLIQUE_AUTO            -1
#  define KN_MIP_CLIQUE_NONE             0
#  define KN_MIP_CLIQUE_ROOT             1
#  define KN_MIP_CLIQUE_TREE             2

Specifies rules for adding clique cuts.

-1 (auto) Automatically determine whether to add clique cuts.
0 (none) Do not add clique cuts.
1 (root) Add clique cuts derived from the root node only.
2 (tree) Add clique cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_cut_flowcover

type KN_PARAM_MIP_CUT_FLOWCOVER

#define KN_PARAM_MIP_CUT_FLOWCOVER    2053
#  define KN_MIP_CUT_FLOWCOVER_AUTO     -1
#  define KN_MIP_CUT_FLOWCOVER_NONE      0
#  define KN_MIP_CUT_FLOWCOVER_ROOT      1
#  define KN_MIP_CUT_FLOWCOVER_TREE      2

Specifies rules for adding flow cover cuts.

-1 (auto) Automatically determine whether to add flow cover cuts.
0 (none) Do not add flow cover cuts.
1 (root) Add flow cover cuts derived from the root node only.
2 (tree) Add flow cover cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_cut_probing

type KN_PARAM_MIP_CUT_PROBING

#define KN_PARAM_MIP_CUT_PROBING      2052
#  define KN_MIP_CUT_PROBING_AUTO       -1
#  define KN_MIP_CUT_PROBING_NONE        0
#  define KN_MIP_CUT_PROBING_ROOT        1
#  define KN_MIP_CUT_PROBING_TREE        2

Specifies rules for adding probing cuts.

-1 (auto) Automatically determine whether to add probing cuts.
0 (none) Do not add probing cuts.
1 (root) Add probing cuts derived from the root node only.
2 (tree) Add probing cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_cutfactor

type KN_PARAM_MIP_CUTFACTOR

#define KN_PARAM_MIP_CUTFACTOR        2035

This value specifies a limit on the number of cuts added to a node subproblem. If non-negative, a maximum of mip_cutfactor times the number of constraints is possibly appended.

Default value: 1.0

type mip_cutoff

type KN_PARAM_MIP_CUTOFF

#define KN_PARAM_MIP_CUTOFF           2044

This value specifies the objective cutoff value for MIP.

Default value: KN_INFINITY

type mip_cutting_plane

type KN_PARAM_MIP_CUTTINGPLANE

#define KN_PARAM_MIP_CUTTINGPLANE     2043  /*-- CUTTING PLANE */
#  define KN_MIP_CUTTINGPLANE_NONE       0  /*--   NONE */
#  define KN_MIP_CUTTINGPLANE_ROOT       1  /*--   IN THE ROOT */

Specifies when to apply the cutting plane procedure.

0 (none) No cutting plane procedure enabled.
1 (root) Perform cutting plane procedure at the root node only.

Default value: 1

type mip_debug

type KN_PARAM_MIP_DEBUG

#define KN_PARAM_MIP_DEBUG            2013
#  define KN_MIP_DEBUG_NONE              0
#  define KN_MIP_DEBUG_ALL               1

Specifies debugging level for MIP solution.

0 (none) No MIP debugging output created.
1 (all) Write MIP debugging output to the file kdbg_mip.log.

Default value: 0

type mip_gomory

type KN_PARAM_MIP_GOMORY

#define KN_PARAM_MIP_GOMORY           2051  /*-- GOMORY CUTS */
#  define KN_MIP_GOMORY_AUTO            -1
#  define KN_MIP_GOMORY_NONE             0  /*--   NONE */
#  define KN_MIP_GOMORY_ROOT             1  /*--   IN THE ROOT ONLY */
#  define KN_MIP_GOMORY_TREE             2  /*--   IN THE WHOLE TREE */

Specifies rules for adding Gomory mixed-integer cuts.

-1 (auto) Automatically determine whether to add Gomory cuts.
0 (none) Do not add Gomory cuts.
1 (root) Add Gomory cuts derived from the root node only.
2 (tree) Add Gomory cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_gub_branch

type KN_PARAM_MIP_GUB_BRANCH

#define KN_PARAM_MIP_GUB_BRANCH       2015  /*-- BRANCH ON GENERALIZED BOUNDS */
#  define KN_MIP_GUB_BRANCH_NO           0
#  define KN_MIP_GUB_BRANCH_YES          1

Specifies whether or not to branch on generalized upper bounds (GUBs).

0 (no) Do not branch on GUBs.
1 (yes) Allow branching on GUBs.

Default value: 0

type mip_heuristic_diving

type KN_PARAM_MIP_HEUR_DIVING

#define KN_PARAM_MIP_HEUR_DIVING      2042

Specifies whether or not to enable the MIP diving heuristic. This option is a bit-valued option where various diving heuristics can be enabled by activating the corresponding bit value as described below. Setting this option to -1 will use an automatic setting and setting the value to 0 will disable all diving heuristics. Otherwise, set this parameter value to the sum of the values for the individual diving heuristics you wish to enable. For example, to enable only the “fractional” and “linesearch” diving heuristics, you would set this option value to 9 (summing 1 for fractional and 8 for linesearch).

-1 (auto) Let Knitro determine automaticallyfrom mip_heuristic_strategy.
1 (bit 0) Enable fractional diving heuristic.
2 (bit 1) Enable vector length diving heuristic.
4 (bit 2) Enable coefficient diving heuristic.
8 (bit 3) Enable linesearch diving heuristic.
16 (bit 4) Enable guided diving heuristic.

Default value: -1

type mip_heuristic_feaspump

type KN_PARAM_MIP_HEUR_FEASPUMP

#define KN_PARAM_MIP_HEUR_FEASPUMP    2040
#  define KN_MIP_HEUR_FEASPUMP_AUTO     -1
#  define KN_MIP_HEUR_FEASPUMP_OFF       0
#  define KN_MIP_HEUR_FEASPUMP_ON        1

Specifies whether or not to enable the MIP feasibility pump heuristic.

-1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.
0 (off) Feasibility pump heuristic is not applied.
1 (on) Feasibility pump heuristic is enabled.

Default value: -1

type mip_heuristic_lns

type KN_PARAM_MIP_HEUR_LNS

#define KN_PARAM_MIP_HEUR_LNS         2045

Specifies whether or not to enable the MIP large neighborhood search (LNS) heuristics. This option is a bit-valued option where various LNS heuristics can be enabled by activating the corresponding bit value as described below. Setting this option to -1 will use an automatic setting and setting the value to 0 will disable all LNS heuristics. Otherwise, set this parameter value to the sum of the values for the individual LNS heuristics you wish to enable. For example, to enable both the “RENS” and “RINS” LNS heuristics, you would set this option value to 3 (summing 1 for RENS and 2 for RINS).

-1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.
1 (bit 0) Enable relaxation enforced neighborhood search (RENS) heuristic.
2 (bit 1) Enable relaxation induced neighborhood search (RINS) heuristic.

Default value: -1

type mip_heuristic_maxit

type KN_PARAM_MIP_HEURISTIC_MAXIT

#define KN_PARAM_MIP_HEUR_MAXIT       2023

Specifies the maximum number of iterations to allow for MIP heuristic, if one is enabled.

Default value: 100

type mip_heuristic_misqp

type KN_PARAM_MIP_HEUR_MISQP

#define KN_PARAM_MIP_HEUR_MISQP       2049
#  define KN_MIP_HEUR_MISQP_AUTO        -1
#  define KN_MIP_HEUR_MISQP_OFF          0
#  define KN_MIP_HEUR_MISQP_ON           1

Specifies whether or not to enable the MIP MISQP heuristic.

-1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.
0 (off) MISQP heuristic is not applied.
1 (on) MISQP heuristic is enabled.

Default value: -1

type mip_heuristic_mpec

type KN_PARAM_MIP_HEUR_MPEC

#define KN_PARAM_MIP_HEUR_MPEC        2041
#  define KN_MIP_HEUR_MPEC_AUTO         -1
#  define KN_MIP_HEUR_MPEC_OFF           0
#  define KN_MIP_HEUR_MPEC_ON            1

Specifies whether or not to enable the MIP MPEC heuristic.

-1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.
0 (off) MPEC heuristic is not applied.
1 (on) MPEC heuristic is enabled.

Default value: -1

type mip_heuristic_localsearch

type KN_PARAM_MIP_HEUR_LOCALSEARCH

#define KN_PARAM_MIP_HEUR_LOCALSEARCH    2054
#  define KN_MIP_HEUR_LOCALSEARCH_AUTO     -1
#  define KN_MIP_HEUR_LOCALSEARCH_OFF       0
#  define KN_MIP_HEUR_LOCALSEARCH_ON        1

Specifies whether or not to enable the MIP local search heuristic.

-1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.
0 (off) Local search heuristic is not applied.
1 (on) Local search heuristic is enabled.

Default value: -1

type mip_heuristic_strategy

type KN_PARAM_MIP_HEUR_STRATEGY

#define KN_PARAM_MIP_HEUR_STRATEGY    2039
#  define KN_MIP_HEUR_STRATEGY_AUTO     -1
#  define KN_MIP_HEUR_STRATEGY_NONE      0
#  define KN_MIP_HEUR_STRATEGY_BASIC     1
#  define KN_MIP_HEUR_STRATEGY_ADVANCED  2
#  define KN_MIP_HEUR_STRATEGY_EXTENSIVE 3

Specifies the level of effort applied for the MIP heuristic search used to try to find an initial integer feasible point.

-1 (auto) Let Knitro choose the heuristic strategy to apply (if any).
0 (none) No heuristic search applied.
1 (basic) Apply basic heuristics.
2 (advanced) Apply more advanced heuristics.
3 (extensive) Apply most extensive heuristics.

Default value: -1

type mip_heuristic_terminate

type KN_PARAM_MIP_HEUR_TERMINATE

#define KN_PARAM_MIP_HEUR_TERMINATE   2033
#  define KN_MIP_HEUR_TERMINATE_FEASIBLE 1
#  define KN_MIP_HEUR_TERMINATE_LIMIT    2

Specifies the condition for terminating the MIP heuristic.

1 (feasible) Terminate at first feasible point or iteration limit (whichever comes first).
2 (limit) Always run to the iteration limit.

Default value: 1

type mip_implications

type KN_PARAM_MIP_IMPLICATIONS

#define KN_PARAM_MIP_IMPLICATIONS     2014  /*-- USE LOGICAL IMPLICATIONS */
#  define KN_MIP_IMPLICATIONS_NO         0
#  define KN_MIP_IMPLICATIONS_YES        1

Specifies whether or not to add constraints to the MIP derived from logical implications.

0 (no) Do not add constraints from logical implications.
1 (yes) Knitro adds constraints from logical implications.

Default value: 1

type mip_integer_tol

type KN_PARAM_MIP_INTEGERTOL

#define KN_PARAM_MIP_INTEGERTOL       2009

This value specifies the threshold for deciding whether or not a variable is determined to be an integer.

Default value: 1.0e-8

type mip_intvar_strategy

type KN_PARAM_MIP_INTVAR_STRATEGY

#define KN_PARAM_MIP_INTVAR_STRATEGY  2030
#  define KN_MIP_INTVAR_STRATEGY_NONE    0
#  define KN_MIP_INTVAR_STRATEGY_RELAX   1
#  define KN_MIP_INTVAR_STRATEGY_MPEC    2

Specifies how to handle integer variables.

0 (none) No special treatment applied.
1 (relax) Relax all integer variables.
2 (mpec) Convert all binary variables to complementarity constraints.

Default value: 0

type mip_knapsack

type KN_PARAM_MIP_KNAPSACK

#define KN_PARAM_MIP_KNAPSACK         2016  /*-- KNAPSACK CUTS */
#  define KN_MIP_KNAPSACK_AUTO          -1
#  define KN_MIP_KNAPSACK_NONE           0  /*--   NONE */
#  define KN_MIP_KNAPSACK_ROOT           1  /*--   IN THE ROOT */
#  define KN_MIP_KNAPSACK_TREE           2  /*--   IN THE WHOLE TREE */

Specifies rules for adding MIP knapsack cuts.

-1 (auto) Automatically determine whether to add knapsack cuts.
0 (none) Do not add knapsack cuts.
1 (root) Add knapsack cuts derived from the root node only.
2 (tree) Add knapsack cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_liftproject

type KN_PARAM_MIP_LIFTPROJECT

#define KN_PARAM_MIP_LIFTPROJECT      2047  /*-- LIFT&PROJECT CUTS */
#  define KN_MIP_LIFTPROJECT_AUTO       -1
#  define KN_MIP_LIFTPROJECT_NONE        0  /*--   NONE */
#  define KN_MIP_LIFTPROJECT_ROOT        1  /*--   IN THE ROOT */

Specifies rules for adding lift and project cuts.

-1 (auto) Automatically determine whether to add lift and project cuts.
0 (none) Do not add lift and project cuts.
1 (root) Add lift and project cuts at the root node only.

Default value: -1

type mip_lpalg

type KN_PARAM_MIP_LPALG

#define KN_PARAM_MIP_LPALG            2019
#  define KN_MIP_LPALG_AUTO              0
#  define KN_MIP_LPALG_BAR_DIRECT        1
#  define KN_MIP_LPALG_BAR_CG            2
#  define KN_MIP_LPALG_ACT_CG            3

Specifies which algorithm to use for any linear programming (LP) subproblem solves that may occur in the MIP branch-and-bound procedure.

LP subproblems may arise if the problem is a mixed integer linear program (MILP), or if using mip_method = HQG. (Nonlinear programming subproblems use the algorithm specified by the algorithm option.)

0 (auto) Let Knitro automatically choose an algorithm, based on the problem characteristics.
1 (direct) Use the Interior/Direct (barrier) algorithm.
2 (cg) Use the Interior/CG (barrier) algorithm.
3 (active) Use the Active Set (simplex) algorithm.

Default value: 0

type mip_maxnodes

type KN_PARAM_MIP_MAXNODES

#define KN_PARAM_MIP_MAXNODES         2021

Specifies the maximum number of nodes explored (0 means no limit).

Default value: 0

type mip_maxsolves

type KN_PARAM_MIP_MAXSOLVES

#define KN_PARAM_MIP_MAXSOLVES        2008

Specifies the maximum number of subproblem solves allowed (0 means no limit).

Default value: 0

type mip_method

type KN_PARAM_MIP_METHOD

#define KN_PARAM_MIP_METHOD           2001
#  define KN_MIP_METHOD_AUTO             0
#  define KN_MIP_METHOD_BB               1
#  define KN_MIP_METHOD_HQG              2
#  define KN_MIP_METHOD_MISQP            3

Specifies which MIP method to use.

0 (auto) Let Knitro automatically choose the method.
1 (BB) Use the standard branch-and-bound method.
2 (HQG) Use the hybrid Quesada-Grossman method (for convex, nonlinear problems only).
3 (MISQP) Use mixed-integer SQP method (allows for non-relaxable integer variables).

Default value: 0

type mip_mir

type KN_PARAM_MIP_MIR

#define KN_PARAM_MIP_MIR              2037  /*-- MIR CUTS */
#  define KN_MIP_MIR_AUTO               -1
#  define KN_MIP_MIR_NONE                0  /*--   NONE */
#  define KN_MIP_MIR_ROOT                1  /*--   IN THE ROOT */
#  define KN_MIP_MIR_TREE                2  /*--   IN THE WHOLE TREE */

Specifies rules for adding mixed-integer rounding cuts.

-1 (auto) Let Knitro decide whether to add mixed-integer rounding cuts.
0 (none) Do not add mixed-integer rounding cuts.
1 (root) Add mixed-integer rounding cuts derived from the root node only.
2 (tree) Add mixed-integer rounding cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

type mip_multistart

type KN_PARAM_MIP_MULTISTART

#define KN_PARAM_MIP_MULTISTART       2046
#  define KN_MIP_MULTISTART_OFF          0
#  define KN_MIP_MULTISTART_ON           1

Use to enable MIP multi-start at the branch-and-bound level.

0 (off) Do not enable MIP multi-start for branch-and-bound.
1 (on) Enable MIP multi-start for branch-and-bound.

Default value: 0

type mip_nodealg

type KN_PARAM_MIP_NODEALG

#define KN_PARAM_MIP_NODEALG          2032
#  define KN_MIP_NODEALG_AUTO            0
#  define KN_MIP_NODEALG_BAR_DIRECT      1
#  define KN_MIP_NODEALG_BAR_CG          2
#  define KN_MIP_NODEALG_ACT_CG          3
#  define KN_MIP_NODEALG_ACT_SQP         4
#  define KN_MIP_NODEALG_MULTI           5

Specifies which algorithm to use for standard node subproblem solves in MIP (same options as algorithm user option).

Default value: 0

type mip_numthreads

type KN_PARAM_MIP_NUMTHREADS

#define KN_PARAM_MIP_NUMTHREADS       2048

Specify the number of threads to use for MIP branch-and-bound (when mip_method = 1).

0 Let Knitro choose the number of threads.

n>0 Use n threads for the MIP branch-and-bound.

Default value: 0

type mip_opt_gap_abs

type KN_PARAM_MIP_OPTGAPABS

#define KN_PARAM_MIP_OPTGAPABS        2004

The absolute optimality gap stop tolerance for MIP.

Default value: 1.0e-6

type mip_opt_gap_rel

type KN_PARAM_MIP_OPTGAPREL

#define KN_PARAM_MIP_OPTGAPREL        2005

The relative optimality gap stop tolerance for MIP.

Default value: 1.0e-4

type mip_outinterval

type KN_PARAM_MIP_OUTINTERVAL

#define KN_PARAM_MIP_OUTINTERVAL      2011

Specifies node printing interval for mip_outlevel when mip_outlevel > 0.

0 Let Knitro decide.
1 Print output every node.
2 Print output every 2nd node.
N Print output every Nth node.

Default value: 0

type mip_outlevel

type KN_PARAM_MIP_OUTLEVEL

#define KN_PARAM_MIP_OUTLEVEL         2010
#  define KN_MIP_OUTLEVEL_NONE           0
#  define KN_MIP_OUTLEVEL_ITERS          1
#  define KN_MIP_OUTLEVEL_ITERSTIME      2
#  define KN_MIP_OUTLEVEL_ROOT           3

Specifies how much MIP information to print.

0 (none) Do not print any MIP node information.
1 (iters) Print one line of output for every node.
2 (iterstime) Also print accumulated time for every node.
3 (root) Also print detailed log from root node solve.

Default value: 2

type mip_outsub

type KN_PARAM_MIP_OUTSUB

#define KN_PARAM_MIP_OUTSUB           2012
#  define KN_MIP_OUTSUB_NONE             0
#  define KN_MIP_OUTSUB_YES              1
#  define KN_MIP_OUTSUB_YESPROB          2

Specifies MIP subproblem solve debug output control. This output is only produced if mip_debug = 1 and appears in the file kdbg_mip.log.

0 Do not print any debug output from subproblem solves.
1 Subproblem debug output enabled, controlled by option outlev.
2 Subproblem debug output enabled and print problem characteristics.

Default value: 0

type mip_pseudoinit

type KN_PARAM_MIP_PSEUDOINIT

#define KN_PARAM_MIP_PSEUDOINIT       2026
#  define KN_MIP_PSEUDOINIT_AUTO         0
#  define KN_MIP_PSEUDOINIT_AVE          1
#  define KN_MIP_PSEUDOINIT_STRONG       2

Specifies the method used to initialize pseudo-costs corresponding to variables that have not yet been branched on in the MIP method.

0 Let Knitro automatically choose the method.
1 Initialize using the average value of computed pseudo-costs.
2 Initialize using strong branching.

Default value: 0

type mip_relaxable

type KN_PARAM_MIP_RELAXABLE

#define KN_PARAM_MIP_RELAXABLE        2031
#  define KN_MIP_RELAXABLE_NONE          0
#  define KN_MIP_RELAXABLE_ALL           1

Specifies whether integer variables are relaxable.

0 (none) Integer variables are not relaxable.
1 (all) All integer variables are relaxable.

Default value: 1

type mip_restart

type KN_PARAM_MIP_RESTART

#define KN_PARAM_MIP_RESTART          2050
#  define KN_MIP_RESTART_OFF             0
#  define KN_MIP_RESTART_ON              1

Specifies whether to enable the MIP restart procedure.

0 (off) Do not enable the MIP restart procedure.
1 (on) Enable the MIP restart procedure.

Default value: 1

type mip_rootalg

type KN_PARAM_MIP_ROOTALG

#define KN_PARAM_MIP_ROOTALG          2018
#  define KN_MIP_ROOTALG_AUTO            0
#  define KN_MIP_ROOTALG_BAR_DIRECT      1
#  define KN_MIP_ROOTALG_BAR_CG          2
#  define KN_MIP_ROOTALG_ACT_CG          3
#  define KN_MIP_ROOTALG_ACT_SQP         4
#  define KN_MIP_ROOTALG_MULTI           5

Specifies which algorithm to use for the root node solve in MIP (same options as algorithm user option).

Default value: 0

type mip_rounding

type KN_PARAM_MIP_ROUNDING

#define KN_PARAM_MIP_ROUNDING         2017
#  define KN_MIP_ROUND_AUTO             -1
#  define KN_MIP_ROUND_NONE              0  /*-- DO NOT ATTEMPT ROUNDING */
#  define KN_MIP_ROUND_HEURISTIC         2  /*-- USE FAST HEURISTIC */
#  define KN_MIP_ROUND_NLP_SOME          3  /*-- SOLVE NLP IF LIKELY TO WORK */
#  define KN_MIP_ROUND_NLP_ALWAYS        4  /*-- SOLVE NLP ALWAYS */

Specifies the MIP rounding rule to apply.

-1 (auto) Let Knitro choose the rounding rule.
0 (none) No rounding heuristic is used.
2 (heur_only) Round using a fast heuristic only.
3 (nlp_sometimes) Round and solve a subproblem if likely to succeed.
4 (nlp_always) Always round and solve a subproblem.

Default value: -1

type mip_selectdir

type KN_PARAM_MIP_SELECTDIR

#define KN_PARAM_MIP_SELECTDIR        2034
#  define KN_MIP_SELECTDIR_DOWN          0
#  define KN_MIP_SELECTDIR_UP            1

Specifies the MIP node selection direction rule (for tiebreakers) for choosing the next node in the branch-and-bound tree.

0 (down) Choose the down (i.e. <=) node first.
1 (up) Choose the up (i.e. >=) node first.

Default value: 0

type mip_selectrule

type KN_PARAM_MIP_SELECTRULE

#define KN_PARAM_MIP_SELECTRULE       2003
#  define KN_MIP_SEL_AUTO                0
#  define KN_MIP_SEL_DEPTHFIRST          1
#  define KN_MIP_SEL_BESTBOUND           2
#  define KN_MIP_SEL_COMBO_1             3

Specifies the MIP select rule for choosing the next node in the branch-and-bound tree.

0 (auto) Let Knitro choose the node selection rule.
1 (depth_first) Search the tree using a depth first procedure.
2 (best_bound) Select the node with the best relaxation bound.
3 (combo_1) Use depth first unless pruned, then best bound.

Default value: 0

type mip_strong_candlim

type KN_PARAM_MIP_STRONG_CANDLIM

#define KN_PARAM_MIP_STRONG_CANDLIM   2028

Specifies the maximum number of candidates to explore for MIP strong branching.

Default value: 10

type mip_strong_level

type KN_PARAM_MIP_STRONG_LEVEL

#define KN_PARAM_MIP_STRONG_LEVEL     2029

Specifies the maximum number of tree levels on which to perform MIP strong branching.

Default value: 10

type mip_strong_maxit

type KN_PARAM_MIP_STRONG_MAXIT

#define KN_PARAM_MIP_STRONG_MAXIT     2027

Specifies the maximum number of iterations to allow for MIP strong branching solves.

Default value: 1000

type mip_sub_maxtime

type KN_PARAM_MIP_SUB_MAXTIME

#define KN_PARAM_MIP_SUB_MAXTIME      2055

Specifies the maximum allowable real time in seconds for MIP node subproblems.

Default value: 1.0e8

type mip_terminate

type KN_PARAM_MIP_TERMINATE

#define KN_PARAM_MIP_TERMINATE        2020
#  define KN_MIP_TERMINATE_OPTIMAL       0
#  define KN_MIP_TERMINATE_FEASIBLE      1

Specifies conditions for terminating the MIP algorithm.

0 (optimal) Terminate at optimum.
1 (feasible) Terminate at first integer feasible point.

Default value: 0

type mip_zerohalf

type KN_PARAM_MIP_ZEROHALF

#define KN_PARAM_MIP_ZEROHALF         2036
#  define KN_MIP_ZEROHALF_AUTO          -1
#  define KN_MIP_ZEROHALF_NONE           0
#  define KN_MIP_ZEROHALF_ROOT           1
#  define KN_MIP_ZEROHALF_TREE           2

Specifies rules for adding zero-half cuts.

-1 (auto) Automatically determine whether to add zero-half cuts.
0 (none) Do not add zero-half cuts.
1 (root) Add zero-half cuts derived from the root node only.
2 (tree) Add zero-half cuts derived at every node depending on the solution of the relaxation and the cut generation strategy.

Default value: -1

Multi-algorithm options

type ma_outsub

type KN_PARAM_MA_OUTSUB

#define KN_PARAM_MA_OUTSUB            1067
#  define KN_MA_OUTSUB_NONE              0
#  define KN_MA_OUTSUB_YES               1

Enable writing algorithm output to files for the multi-algorithm (alg=5) procedure.

0 Do not write detailed algorithm output to files.
1 Write detailed algorithm output to files named knitro_ma_*.log.

Default value: 0

type ma_sub_maxtime

type KN_PARAM_MA_SUB_MAXTIME

#define KN_PARAM_MA_SUB_MAXTIME       1164

Specifies, in seconds, the maximum allowable real time for multi-algorithm (“MA”) subproblems (alg=5).

Default value: 1.0e8

type ma_terminate

type KN_PARAM_MA_TERMINATE

#define KN_PARAM_MA_TERMINATE         1063
#  define KN_MA_TERMINATE_ALL            0
#  define KN_MA_TERMINATE_OPTIMAL        1
#  define KN_MA_TERMINATE_FEASIBLE       2
#  define KN_MA_TERMINATE_ANY            3

Define the termination condition for the multi-algorithm (alg=5) procedure.

0 Terminate after all algorithms have completed.
1 Terminate at first locally optimal solution.
2 Terminate at first feasible solution estimate.
3 Terminate at first solution estimate of any type.

Default value: 1

Multi-Start options

type ms_enable

type KN_PARAM_MS_ENABLE

#define KN_PARAM_MULTISTART           1033
#define KN_PARAM_MS_ENABLE            1033
#  define KN_MS_ENABLE_NO                0
#  define KN_MS_ENABLE_YES               1

Indicates whether Knitro will solve from multiple start points to find a better local minimum.

0 (no) Knitro solves from a single initial point.
1 (yes) Knitro solves using multiple start points.

Default value: 0

type ms_initpt_cluster

type KN_PARAM_MS_INITPT_CLUSTER

#define KN_PARAM_MS_INITPT_CLUSTER    1149
#  define KN_MS_INITPT_CLUSTER_NONE      0
#  define KN_MS_INITPT_CLUSTER_SL        1

The strategy for clustering initial points in multi-start.

0 (none) Do not apply clustering.
1 (sl) Apply single linkage based clustering.

Default value: 0

type ms_maxbndrange

type KN_PARAM_MS_MAXBNDRANGE

#define KN_PARAM_MS_MAXBNDRANGE       1035

Specifies the maximum range that an unbounded variable can take when determining new start points.

If a variable is unbounded in one or both directions, then new start point values are restricted by the option. If $x_i$ is such a variable, then all initial values satisfy

$\max \{ b^L_i, x_i^0 - {\tt ms\_maxbndrange}/2 \} \leq x_i \leq \min \{ b^U_i, x_i^0 + {\tt ms\_maxbndrange}/2 \},$

where $x_i^0$ is the initial value of $x_i$ provided by the user, and $b^L_i$ and $b^U_i$ are the variable bounds (possibly infinite) on $x_i$ . This option has no effect unless ms_enable = yes.

Default value: 1000.0

type ms_maxsolves

type KN_PARAM_MS_MAXSOLVES

#define KN_PARAM_MS_MAXSOLVES         1034

Specifies how many start points to try in multi-start. This option has no effect unless ms_enable = yes.

0 Let Knitro automatically choose a value based on the problem size and context. For stand-alone continuous models, the value is min(100, 10 N), where N is the number of variables in the problem. For node subproblems inside branch-and-bound, the default value is 8.
n Try n>0 start points.

Default value: 0

type ms_num_to_save

type KN_PARAM_MS_NUMTOSAVE

#define KN_PARAM_MS_NUMTOSAVE         1051

Specifies the number of distinct feasible points to save in a file named knitro_mspoints.log.

Each point results from a Knitro solve from a different starting point, and must satisfy the absolute and relative feasibility tolerances. The file stores points in order from best objective to worst. Points are distinct if they differ in objective value or some component by the value of ms_savetol using a relative tolerance test. This option has no effect unless ms_enable = yes.

Default value: 0

type ms_numthreads

type KN_PARAM_MS_NUMTHREADS

#define KN_PARAM_MS_NUMTHREADS        1137

Specify the number of threads to use for multi-start (when ms_enable = 1).

0 Let Knitro choose the number of threads (currently sets ms_numthreads based on numthreads).

n>0 Use n threads for the multi-start (solve n problems in parallel).

Default value: 0

type ms_outsub

type KN_PARAM_MS_OUTSUB

#define KN_PARAM_MS_OUTSUB            1068
#  define KN_MS_OUTSUB_NONE              0
#  define KN_MS_OUTSUB_YES               1

Enable writing algorithm output to files for the parallel multi-start procedure.

0 Do not write detailed algorithm output to files.
1 Write detailed algorithm output to files named knitro_ms_*.log.

Default value: 0

type ms_savetol

type KN_PARAM_MS_SAVETOL

#define KN_PARAM_MS_SAVETOL           1052

Specifies the tolerance for deciding if two feasible points are distinct.

Points are distinct if they differ in objective value or some component by the value of ms_savetol using a relative tolerance test. A large value can cause the saved feasible points in the file knitro_mspoints.log to cluster around more widely separated points. This option has no effect unless ms_enable = yes. and ms_num_to_save is positive.

Default value: 1.0e-6

type ms_seed

type KN_PARAM_MS_SEED

#define KN_PARAM_MS_SEED              1066

Seed value used to generate random initial points in multi-start; should be a non-negative integer.

Default value: 0

type ms_startptrange

type KN_PARAM_MS_STARTPTRANGE

#define KN_PARAM_MS_STARTPTRANGE      1055

Specifies the maximum range that each variable can take when determining new start points.

If a variable has upper and lower bounds and the difference between them is less than or equal to ms_startptrange, then new start point values for the variable can be any number between its upper and lower bounds.

If the variable is unbounded in one or both directions, or the difference between bounds is greater than ms_startptrange, then new start point values are restricted by the option. If $x_i$ is such a variable, then all initial values satisfy

$\max \{ b^L_i, x_i^0 - \tau \} \leq x_i \leq \min \{ b^U_i, x_i^0 + \tau \}, \tau = \min \{ {\tt ms\_startptrange}/2, {\tt ms\_maxbndrange}/2 \}$

where $x_i^0$ is the initial value of $x_i$ provided by the user, and $b^L_i$ and $b^U_i$ are the variable bounds (possibly infinite) on $x_i$ . This option has no effect unless ms_enable = yes.

Default value: 1.0e20

type ms_sub_maxtime

type KN_PARAM_MS_SUB_MAXTIME

#define KN_PARAM_MS_SUB_MAXTIME       1165

Specifies, in seconds, the maximum allowable real time for multi-start subproblems (i.e. local solves from a given initial point). This option has no effect unless ms_enable = yes.

Default value: 1.0e8

type ms_terminate

type KN_PARAM_MS_TERMINATE

#define KN_PARAM_MS_TERMINATE         1054
#  define KN_MS_TERMINATE_MAXSOLVES      0
#  define KN_MS_TERMINATE_OPTIMAL        1
#  define KN_MS_TERMINATE_FEASIBLE       2
#  define KN_MS_TERMINATE_ANY            3
#  define KN_MS_TERMINATE_RULEBASED      4

Specifies the condition for terminating multi-start.

This option has no effect unless ms_enable = yes.

0 (maxsolves) Terminate after ms_maxsolves.
1 (optimal) Terminate after the first local optimal solution is found or ms_maxsolves, whichever comes first.
2 (feasible) Terminate after the first feasible solution estimate is found or ms_maxsolves, whichever comes first.
3 (any) Terminate after the first solution estimate of any type is found or ms_maxsolves, whichever comes first.
4 (rulebased) Terminate using rules that estimate when the probability of finding new local solutions is low.

Default value: 4

type ms_terminaterule_tol

type KN_PARAM_MS_TERMINATERULE_TOL

#define KN_PARAM_MS_TERMINATERULE_TOL 1160

The tolerance in (0,1] for the rule-based termination of multi-start. Specifying a non-positive value will enable an automatic tolerance selection. Values closer to 1 trigger termination sooner, while values closer to zero will trigger termination in more solves.

Default value: 0.0

Parallelism options

type blas_numthreads

type KN_PARAM_BLAS_NUMTHREADS

#define KN_PARAM_BLAS_NUMTHREADS      1135

Specify the number of threads to use for BLAS operations when blasoption = 1 (see Parallelism).

Default value: 0 (Knitro will automatically set blas_numthreads based on numthreads)

type concurrent_evals

type KN_PARAM_CONCURRENT_EVALS

#define KN_PARAM_CONCURRENT_EVALS     1134
#  define KN_CONCURRENT_EVALS_NO         0
#  define KN_CONCURRENT_EVALS_YES        1

Determines whether or not the user provided callback functions used for function and derivative evaluations can take place concurrently in parallel (for possibly different values of “x”). If it is not safe to have concurrent evaluations, then setting concurrent_evals=0, will put these evaluations in a critical region so that only one evaluation can take place at a time. If concurrent_evals=1 then concurrent evaluations are allowed when Knitro is run in parallel, and it is the responsibility of the user to ensure that these evaluations are stable. See Parallelism.

0 (no) Do not allow concurrent callback evaluations.
1 (yes) Allow concurrent callback evaluations.

Default value: 1

type conic_numthreads

type KN_PARAM_CONIC_NUMTHREADS

#define KN_PARAM_CONIC_NUMTHREADS     1138

Specify the number of threads to use for operations in the conic algorithm (when bar_conic_enable = 1).

0 Let Knitro choose the number of threads (currently sets conic_numthreads based on numthreads).

n>0 Use n threads for the conic solver.

Default value: 0

type findiff_numthreads

type KN_PARAM_FINDIFF_NUMTHREADS

#define KN_PARAM_FINDIFF_NUMTHREADS   1141

Specify the number of threads to use when computing finite-difference gradients (using more than 1 thread here is only beneficial when concurrent_evals=1).

0 Let Knitro choose the number of threads.

n>0 Use n threads for the finite-difference gradients.

Default value: 0

type linsolver_numthreads

type KN_PARAM_LINSOLVER_NUMTHREADS

#define KN_PARAM_LINSOLVER_NUMTHREADS 1136

Specify the number of threads to use for linear system solve operations when linsolver = 6 (see Parallelism).

Default value: 0 (Knitro will automatically set linsolver_numthreads based on numthreads)

type numthreads

type KN_PARAM_NUMTHREADS

#define KN_PARAM_NUMTHREADS           1133

Specify the number of threads to use for parallel computing features (see Parallelism).

Default value: -1 (Knitro will automatically determine the number of threads to use and how to distribute them)

Output options

type debug

type KN_PARAM_DEBUG

#define KN_PARAM_DEBUG                1031
#  define KN_DEBUG_NONE                  0
#  define KN_DEBUG_PROBLEM               1
#  define KN_DEBUG_EXECUTION             2

Controls the level of debugging output.

Debugging output can slow execution of Knitro and should not be used in a production setting. All debugging output is suppressed if option outlev = 0.

0 (none) No debugging output.
1 (problem) Print algorithm information to kdbg*.log
output files.
2 (execution) Print program execution information.

Default value: 0

type newpoint

type KN_PARAM_NEWPOINT

#define KN_PARAM_NEWPOINT             1001
#  define KN_NEWPOINT_NONE               0
#  define KN_NEWPOINT_SAVEONE            1
#  define KN_NEWPOINT_SAVEALL            2
Specifies additional action to take after every iteration in a solve of a continuous problem, or after every new incumbent of the NLPBB algorithm.

For a continuous problem, an iteration of Knitro results in a new point that is closer to a solution. The new point includes values of x and Lagrange multipliers lambda.

For the NLPBB algorithm, the new incumbent includes values of x.

0 (none) Knitro takes no additional action.

1 (saveone) Knitro writes x and lambda to the file knitro_newpoint.log. Previous contents of the file are overwritten.

2 (saveall) Knitro appends x and lambda to the file knitro_newpoint.log. Warning: this option can generate a very large file. All iterates, including the start point, crossover points, and the final solution are saved. Each iterate also prints the objective value at the new point, except the initial start point.

Default value: 0

type out_csvinfo

type KN_PARAM_OUT_CSVINFO

#define KN_PARAM_OUT_CSVINFO          1096
#  define KN_OUT_CSVINFO_NO              0
#  define KN_OUT_CSVINFO_YES             1

Controls whether or not to generates a file knitro_solve.csv containing solve information in comma separated format.

0 (no) No solution information file is generated.
1 (yes) The knitro_solve.csv solution file is generated.

Default value: 0

type out_csvname

type KN_PARAM_OUT_CSVNAME

#define KN_PARAM_OUT_CSVNAME          1106

Use to specify a custom csv filename when using out_csvinfo.

Default value: knitro_solve.csv

type out_hints

type KN_PARAM_OUT_HINTS

#define KN_PARAM_OUT_HINTS            1115
#  define KN_OUT_HINTS_NO                0
#  define KN_OUT_HINTS_YES               1

Specifies whether to print diagnostic hints (e.g. about user option settings) after solving.

0 (no) Do not print any hints.
1 (yes) Print diagnostic hints on occasion.

Default value: 1

type outappend

type KN_PARAM_OUTAPPEND

#define KN_PARAM_OUTAPPEND            1046
#  define KN_OUTAPPEND_NO                0
#  define KN_OUTAPPEND_YES               1

Specifies whether output should be started in a new file, or appended to existing files.

The option affects knitro.log and files produced when debug = 1. It does not affect knitro_newpoint.log, which is controlled by option newpoint.

0 (no) Erase any existing files when opening for output.
1 (yes) Append output to any existing files.

Default value: 0

type outdir

type KN_PARAM_OUTDIR

#define KN_PARAM_OUTDIR               1047

Specifies a single directory as the location to write all output files.

The option should be a full pathname to the directory, and the directory must already exist.

type outlev

type KN_PARAM_OUTLEV

#define KN_PARAM_OUTLEV               1015
#  define KN_OUTLEV_NONE                 0
#  define KN_OUTLEV_SUMMARY              1
#  define KN_OUTLEV_ITER_10              2
#  define KN_OUTLEV_ITER                 3
#  define KN_OUTLEV_ITER_VERBOSE         4
#  define KN_OUTLEV_ITER_X               5
#  define KN_OUTLEV_ALL                  6

Controls the level of output produced by Knitro.

0 (none) Printing of all output is suppressed.
1 (summary) Print only summary information.
2 (iter_10) Print basic information every 10 iterations.
3 (iter) Print basic information at each iteration.
4 (iter_verbose) Print basic information and the function count at each iteration.
5 (iter_x) Print all the above, and the values of the solution vector x.
6 (all) Print all the above, and the values of the constraints c at x and the Lagrange multipliers lambda.

Default value: 2

type outmode

type KN_PARAM_OUTMODE

#define KN_PARAM_OUTMODE              1016
#  define KN_OUTMODE_SCREEN              0
#  define KN_OUTMODE_FILE                1
#  define KN_OUTMODE_BOTH                2

Specifies where to direct the output from Knitro.

0 (screen) Output is directed to standard out (e.g., screen).
1 (file) Output is sent to a file named knitro.log.
2 (both) Output is directed to both the screen and file knitro.log.

Default value: 0

type outname

type KN_PARAM_OUTNAME

#define KN_PARAM_OUTNAME              1105

Use to specify a custom filename when output is written to a file using outmode.

Default value: knitro.log

Tuner options

type tuner

type KN_PARAM_TUNER

#define KN_PARAM_TUNER                1070
    #  define KN_TUNER_OFF                   0
    #  define KN_TUNER_ON                    1

Indicates whether to invoke the Knitro-Tuner (see The Knitro-Tuner).

0 (off) Do not invoke the Knitro-Tuner.
1 (on) Invoke the Knitro-Tuner.

Default value: 0

type tuner_optionsfile

type KN_PARAM_TUNER_OPTIONSFILE

#define KN_PARAM_TUNER_OPTIONSFILE    1071

Can be used to specify the location of a Tuner options file (see The Knitro-Tuner).

Default value: NULL

type tuner_outsub

type KN_PARAM_TUNER_OUTSUB

#define KN_PARAM_TUNER_OUTSUB         1074
#  define KN_TUNER_OUTSUB_NONE           0
#  define KN_TUNER_OUTSUB_SUMMARY        1
#  define KN_TUNER_OUTSUB_ALL            2

Enable writing additional Tuner subproblem solve output to files for the Knitro-Tuner procedure (tuner=1).

0 Do not write detailed solve output to files.
1 Write summary solve output to a file named knitro_tuner_summary.log.
2 Write detailed individual solve output to files named knitro_tuner_*.log.

Default value: 0

type tuner_sub_maxtime

type KN_PARAM_TUNER_SUB_MAXTIME

#define KN_PARAM_TUNER_SUB_MAXTIME    1166

Specifies, in seconds, the maximum allowable real time for Knitro-Tuner subproblems (i.e. individual solves with a particular option setting). This option has no effect unless tuner = on.

Default value: 1.0e8

type tuner_terminate

type KN_PARAM_TUNER_TERMINATE

#define KN_PARAM_TUNER_TERMINATE      1075
#  define KN_TUNER_TERMINATE_ALL         0
#  define KN_TUNER_TERMINATE_OPTIMAL     1
#  define KN_TUNER_TERMINATE_FEASIBLE    2
#  define KN_TUNER_TERMINATE_ANY         3

Define the termination condition for the Knitro-Tuner procedure (tuner=1).

0 Terminate after all solves have completed.
1 Terminate at first locally optimal solution.
2 Terminate at first feasible solution estimate.
3 Terminate at first solution estimate of any type.

Default value: 0