# 14.4 Parameters (alphabetical list sorted by type)¶

## 14.4.1 Double parameters¶

- "basisRelTolS"¶
Maximum relative dual bound violation allowed in an optimal basic solution.

- Default
1.0e-12

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("basisRelTolS", 1.0e-12)`

- Generic name
MSK_DPAR_BASIS_REL_TOL_S

- Groups

- "basisTolS"¶
Maximum absolute dual bound violation in an optimal basic solution.

- Default
1.0e-6

- Accepted
[1.0e-9; +inf]

- Example
`M.setSolverParam("basisTolS", 1.0e-6)`

- Generic name
MSK_DPAR_BASIS_TOL_S

- Groups

- "basisTolX"¶
Maximum absolute primal bound violation allowed in an optimal basic solution.

- Default
1.0e-6

- Accepted
[1.0e-9; +inf]

- Example
`M.setSolverParam("basisTolX", 1.0e-6)`

- Generic name
MSK_DPAR_BASIS_TOL_X

- Groups

- "intpntCoTolDfeas"¶
Dual feasibility tolerance used by the interior-point optimizer for conic problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntCoTolDfeas", 1.0e-8)`

- See also
- Generic name
MSK_DPAR_INTPNT_CO_TOL_DFEAS

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolInfeas"¶
Infeasibility tolerance used by the interior-point optimizer for conic problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible.

- Default
1.0e-12

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntCoTolInfeas", 1.0e-12)`

- Generic name
MSK_DPAR_INTPNT_CO_TOL_INFEAS

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolMuRed"¶
Relative complementarity gap tolerance used by the interior-point optimizer for conic problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntCoTolMuRed", 1.0e-8)`

- Generic name
MSK_DPAR_INTPNT_CO_TOL_MU_RED

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolNearRel"¶
Optimality tolerance used by the interior-point optimizer for conic problems. If

**MOSEK**cannot compute a solution that has the prescribed accuracy then it will check if the solution found satisfies the termination criteria with all tolerances multiplied by the value of this parameter. If yes, then the solution is also declared optimal.- Default
1000

- Accepted
[1.0; +inf]

- Example
`M.setSolverParam("intpntCoTolNearRel", 1000)`

- Generic name
MSK_DPAR_INTPNT_CO_TOL_NEAR_REL

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolPfeas"¶
Primal feasibility tolerance used by the interior-point optimizer for conic problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntCoTolPfeas", 1.0e-8)`

- See also
- Generic name
MSK_DPAR_INTPNT_CO_TOL_PFEAS

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntCoTolRelGap"¶
Relative gap termination tolerance used by the interior-point optimizer for conic problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntCoTolRelGap", 1.0e-8)`

- See also
- Generic name
MSK_DPAR_INTPNT_CO_TOL_REL_GAP

- Groups
Interior-point method, Termination criteria, Conic interior-point method

- "intpntTolDfeas"¶
Dual feasibility tolerance used by the interior-point optimizer for linear problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntTolDfeas", 1.0e-8)`

- Generic name
MSK_DPAR_INTPNT_TOL_DFEAS

- Groups

- "intpntTolDsafe"¶
Controls the initial dual starting point used by the interior-point optimizer. If the interior-point optimizer converges slowly and/or the constraint or variable bounds are very large, then it might be worthwhile to increase this value.

- Default
1.0

- Accepted
[1.0e-4; +inf]

- Example
`M.setSolverParam("intpntTolDsafe", 1.0)`

- Generic name
MSK_DPAR_INTPNT_TOL_DSAFE

- Groups

- "intpntTolInfeas"¶
Infeasibility tolerance used by the interior-point optimizer for linear problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible.

- Default
1.0e-10

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntTolInfeas", 1.0e-10)`

- Generic name
MSK_DPAR_INTPNT_TOL_INFEAS

- Groups

- "intpntTolMuRed"¶
Relative complementarity gap tolerance used by the interior-point optimizer for linear problems.

- Default
1.0e-16

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntTolMuRed", 1.0e-16)`

- Generic name
MSK_DPAR_INTPNT_TOL_MU_RED

- Groups

- "intpntTolPath"¶
Controls how close the interior-point optimizer follows the central path. A large value of this parameter means the central path is followed very closely. On numerically unstable problems it may be worthwhile to increase this parameter.

- Default
1.0e-8

- Accepted
[0.0; 0.9999]

- Example
`M.setSolverParam("intpntTolPath", 1.0e-8)`

- Generic name
MSK_DPAR_INTPNT_TOL_PATH

- Groups

- "intpntTolPfeas"¶
Primal feasibility tolerance used by the interior-point optimizer for linear problems.

- Default
1.0e-8

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntTolPfeas", 1.0e-8)`

- Generic name
MSK_DPAR_INTPNT_TOL_PFEAS

- Groups

- "intpntTolPsafe"¶
Controls the initial primal starting point used by the interior-point optimizer. If the interior-point optimizer converges slowly and/or the constraint or variable bounds are very large, then it may be worthwhile to increase this value.

- Default
1.0

- Accepted
[1.0e-4; +inf]

- Example
`M.setSolverParam("intpntTolPsafe", 1.0)`

- Generic name
MSK_DPAR_INTPNT_TOL_PSAFE

- Groups

- "intpntTolRelGap"¶
Relative gap termination tolerance used by the interior-point optimizer for linear problems.

- Default
1.0e-8

- Accepted
[1.0e-14; +inf]

- Example
`M.setSolverParam("intpntTolRelGap", 1.0e-8)`

- Generic name
MSK_DPAR_INTPNT_TOL_REL_GAP

- Groups

- "intpntTolRelStep"¶
Relative step size to the boundary for linear and quadratic optimization problems.

- Default
0.9999

- Accepted
[1.0e-4; 0.999999]

- Example
`M.setSolverParam("intpntTolRelStep", 0.9999)`

- Generic name
MSK_DPAR_INTPNT_TOL_REL_STEP

- Groups

- "intpntTolStepSize"¶
Minimal step size tolerance. If the step size falls below the value of this parameter, then the interior-point optimizer assumes that it is stalled. In other words the interior-point optimizer does not make any progress and therefore it is better to stop.

- Default
1.0e-6

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("intpntTolStepSize", 1.0e-6)`

- Generic name
MSK_DPAR_INTPNT_TOL_STEP_SIZE

- Groups

- "lowerObjCut"¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)

`lowerObjCut`

,`upperObjCut`

\(]\), then**MOSEK**is terminated.- Default
-1.0e30

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("lowerObjCut", -1.0e30)`

- See also
- Generic name
MSK_DPAR_LOWER_OBJ_CUT

- Groups

- "lowerObjCutFiniteTrh"¶
If the lower objective cut is less than the value of this parameter value, then the lower objective cut i.e.

`lowerObjCut`

is treated as \(-\infty\).- Default
-0.5e30

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("lowerObjCutFiniteTrh", -0.5e30)`

- Generic name
MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH

- Groups

- "mioDjcMaxBigm"¶
Maximum allowed big-M value when reformulating disjunctive constraints to linear constraints. Higher values make it more likely that a disjunction is reformulated to linear constraints, but also increase the risk of numerical problems.

- Default
1.0e6

- Accepted
[0; +inf]

- Example
`M.setSolverParam("mioDjcMaxBigm", 1.0e6)`

- Generic name
MSK_DPAR_MIO_DJC_MAX_BIGM

- Groups

- "mioMaxTime"¶
This parameter limits the maximum time spent by the mixed-integer optimizer. A negative number means infinity.

- Default
-1.0

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("mioMaxTime", -1.0)`

- Generic name
MSK_DPAR_MIO_MAX_TIME

- Groups

- "mioRelGapConst"¶
This value is used to compute the relative gap for the solution to an integer optimization problem.

- Default
1.0e-10

- Accepted
[1.0e-15; +inf]

- Example
`M.setSolverParam("mioRelGapConst", 1.0e-10)`

- Generic name
MSK_DPAR_MIO_REL_GAP_CONST

- Groups

- "mioTolAbsGap"¶
Absolute optimality tolerance employed by the mixed-integer optimizer.

- Default
0.0

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("mioTolAbsGap", 0.0)`

- Generic name
MSK_DPAR_MIO_TOL_ABS_GAP

- Groups

- "mioTolAbsRelaxInt"¶
Absolute integer feasibility tolerance. If the distance to the nearest integer is less than this tolerance then an integer constraint is assumed to be satisfied.

- Default
1.0e-5

- Accepted
[1e-9; +inf]

- Example
`M.setSolverParam("mioTolAbsRelaxInt", 1.0e-5)`

- Generic name
MSK_DPAR_MIO_TOL_ABS_RELAX_INT

- Groups

- "mioTolFeas"¶
Feasibility tolerance for mixed integer solver.

- Default
1.0e-6

- Accepted
[1e-9; 1e-3]

- Example
`M.setSolverParam("mioTolFeas", 1.0e-6)`

- Generic name
MSK_DPAR_MIO_TOL_FEAS

- Groups

- "mioTolRelDualBoundImprovement"¶
If the relative improvement of the dual bound is smaller than this value, the solver will terminate the root cut generation. A value of 0.0 means that the value is selected automatically.

- Default
0.0

- Accepted
[0.0; 1.0]

- Example
`M.setSolverParam("mioTolRelDualBoundImprovement", 0.0)`

- Generic name
MSK_DPAR_MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT

- Groups

- "mioTolRelGap"¶
Relative optimality tolerance employed by the mixed-integer optimizer.

- Default
1.0e-4

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("mioTolRelGap", 1.0e-4)`

- Generic name
MSK_DPAR_MIO_TOL_REL_GAP

- Groups

- "optimizerMaxTime"¶
Maximum amount of time the optimizer is allowed to spent on the optimization. A negative number means infinity.

- Default
-1.0

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("optimizerMaxTime", -1.0)`

- Generic name
MSK_DPAR_OPTIMIZER_MAX_TIME

- Groups

- "presolveTolAbsLindep"¶
Absolute tolerance employed by the linear dependency checker.

- Default
1.0e-6

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("presolveTolAbsLindep", 1.0e-6)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_ABS_LINDEP

- Groups

- "presolveTolAij"¶
Absolute zero tolerance employed for \(a_{ij}\) in the presolve.

- Default
1.0e-12

- Accepted
[1.0e-15; +inf]

- Example
`M.setSolverParam("presolveTolAij", 1.0e-12)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_AIJ

- Groups

- "presolveTolPrimalInfeasPerturbation"¶
The presolve is allowed to perturbe a bound on a constraint or variable by this amount if it removes an infeasibility.

- Default
1.0e-6

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("presolveTolPrimalInfeasPerturbation", 1.0e-6)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION

- Groups

- "presolveTolRelLindep"¶
Relative tolerance employed by the linear dependency checker.

- Default
1.0e-10

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("presolveTolRelLindep", 1.0e-10)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_REL_LINDEP

- Groups

- "presolveTolS"¶
Absolute zero tolerance employed for \(s_i\) in the presolve.

- Default
1.0e-8

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("presolveTolS", 1.0e-8)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_S

- Groups

- "presolveTolX"¶
Absolute zero tolerance employed for \(x_j\) in the presolve.

- Default
1.0e-8

- Accepted
[0.0; +inf]

- Example
`M.setSolverParam("presolveTolX", 1.0e-8)`

- Generic name
MSK_DPAR_PRESOLVE_TOL_X

- Groups

- "simLuTolRelPiv"¶
Relative pivot tolerance employed when computing the LU factorization of the basis in the simplex optimizers and in the basis identification procedure. A value closer to 1.0 generally improves numerical stability but typically also implies an increase in the computational work.

- Default
0.01

- Accepted
[1.0e-6; 0.999999]

- Example
`M.setSolverParam("simLuTolRelPiv", 0.01)`

- Generic name
MSK_DPAR_SIM_LU_TOL_REL_PIV

- Groups

- "simplexAbsTolPiv"¶
Absolute pivot tolerance employed by the simplex optimizers.

- Default
1.0e-7

- Accepted
[1.0e-12; +inf]

- Example
`M.setSolverParam("simplexAbsTolPiv", 1.0e-7)`

- Generic name
MSK_DPAR_SIMPLEX_ABS_TOL_PIV

- Groups

- "upperObjCut"¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)

`lowerObjCut`

,`upperObjCut`

\(]\), then**MOSEK**is terminated.- Default
1.0e30

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("upperObjCut", 1.0e30)`

- See also
- Generic name
MSK_DPAR_UPPER_OBJ_CUT

- Groups

- "upperObjCutFiniteTrh"¶
If the upper objective cut is greater than the value of this parameter, then the upper objective cut

`upperObjCut`

is treated as \(\infty\).- Default
0.5e30

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("upperObjCutFiniteTrh", 0.5e30)`

- Generic name
MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH

- Groups

## 14.4.2 Integer parameters¶

- "autoUpdateSolInfo"¶
Controls whether the solution information items are automatically updated after an optimization is performed.

- Default
- Accepted
- Example
`M.setSolverParam("autoUpdateSolInfo", "off")`

- Generic name
MSK_IPAR_AUTO_UPDATE_SOL_INFO

- Groups

- "biCleanOptimizer"¶
Controls which simplex optimizer is used in the clean-up phase. Anything else than

`"primalSimplex"`

or`"dualSimplex"`

is equivalent to`"freeSimplex"`

.- Default
- Accepted
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example
`M.setSolverParam("biCleanOptimizer", "free")`

- Generic name
MSK_IPAR_BI_CLEAN_OPTIMIZER

- Groups

- "biIgnoreMaxIter"¶
If the parameter

`intpntBasis`

has the value`"noError"`

and the interior-point optimizer has terminated due to maximum number of iterations, then basis identification is performed if this parameter has the value`"on"`

.- Default
- Accepted
- Example
`M.setSolverParam("biIgnoreMaxIter", "off")`

- Generic name
MSK_IPAR_BI_IGNORE_MAX_ITER

- Groups

- "biIgnoreNumError"¶
If the parameter

`intpntBasis`

has the value`"noError"`

and the interior-point optimizer has terminated due to a numerical problem, then basis identification is performed if this parameter has the value`"on"`

.- Default
- Accepted
- Example
`M.setSolverParam("biIgnoreNumError", "off")`

- Generic name
MSK_IPAR_BI_IGNORE_NUM_ERROR

- Groups

- "biMaxIterations"¶
Controls the maximum number of simplex iterations allowed to optimize a basis after the basis identification.

- Default
1000000

- Accepted
[0; +inf]

- Example
`M.setSolverParam("biMaxIterations", 1000000)`

- Generic name
MSK_IPAR_BI_MAX_ITERATIONS

- Groups

- "cacheLicense"¶
Specifies if the license is kept checked out for the lifetime of the

**MOSEK**environment/model/process (`"on"`

) or returned to the server immediately after the optimization (`"off"`

).By default the license is checked out for the lifetime of the process by the first call to

`Model.solve`

.Check-in and check-out of licenses have an overhead. Frequent communication with the license server should be avoided.

- Default
- Accepted
- Example
`M.setSolverParam("cacheLicense", "on")`

- Generic name
MSK_IPAR_CACHE_LICENSE

- Groups

- "infeasPreferPrimal"¶
If both certificates of primal and dual infeasibility are supplied then only the primal is used when this option is turned on.

- Default
- Accepted
- Example
`M.setSolverParam("infeasPreferPrimal", "on")`

- Generic name
MSK_IPAR_INFEAS_PREFER_PRIMAL

- Groups

- "infeasReportAuto"¶
Controls whether an infeasibility report is automatically produced after the optimization if the problem is primal or dual infeasible.

- Default
- Accepted
- Example
`M.setSolverParam("infeasReportAuto", "off")`

- Generic name
MSK_IPAR_INFEAS_REPORT_AUTO

- Groups

- "intpntBasis"¶
Controls whether the interior-point optimizer also computes an optimal basis.

- Default
- Accepted
- Example
`M.setSolverParam("intpntBasis", "always")`

- See also
`biIgnoreMaxIter`

,`biIgnoreNumError`

,`biMaxIterations`

,`biCleanOptimizer`

- Generic name
MSK_IPAR_INTPNT_BASIS

- Groups

- "intpntDiffStep"¶
Controls whether different step sizes are allowed in the primal and dual space.

- Default
- Accepted
- Example
`M.setSolverParam("intpntDiffStep", "on")`

- Generic name
MSK_IPAR_INTPNT_DIFF_STEP

- Groups

- "intpntMaxIterations"¶
Controls the maximum number of iterations allowed in the interior-point optimizer.

- Default
400

- Accepted
[0; +inf]

- Example
`M.setSolverParam("intpntMaxIterations", 400)`

- Generic name
MSK_IPAR_INTPNT_MAX_ITERATIONS

- Groups

- "intpntMaxNumCor"¶
Controls the maximum number of correctors allowed by the multiple corrector procedure. A negative value means that

**MOSEK**is making the choice.- Default
-1

- Accepted
[-1; +inf]

- Example
`M.setSolverParam("intpntMaxNumCor", -1)`

- Generic name
MSK_IPAR_INTPNT_MAX_NUM_COR

- Groups

- "intpntOffColTrh"¶
Controls how many offending columns are detected in the Jacobian of the constraint matrix.

\(0\)

no detection

\(1\)

aggressive detection

\(>1\)

higher values mean less aggressive detection

- Default
40

- Accepted
[0; +inf]

- Example
`M.setSolverParam("intpntOffColTrh", 40)`

- Generic name
MSK_IPAR_INTPNT_OFF_COL_TRH

- Groups

- "intpntOrderGpNumSeeds"¶
The GP ordering is dependent on a random seed. Therefore, trying several random seeds may lead to a better ordering. This parameter controls the number of random seeds tried.

A value of 0 means that MOSEK makes the choice.

- Default
0

- Accepted
[0; +inf]

- Example
`M.setSolverParam("intpntOrderGpNumSeeds", 0)`

- Generic name
MSK_IPAR_INTPNT_ORDER_GP_NUM_SEEDS

- Groups

- "intpntOrderMethod"¶
Controls the ordering strategy used by the interior-point optimizer when factorizing the Newton equation system.

- Default
- Accepted
`"free"`

,`"appminloc"`

,`"experimental"`

,`"tryGraphpar"`

,`"forceGraphpar"`

,`"none"`

- Example
`M.setSolverParam("intpntOrderMethod", "free")`

- Generic name
MSK_IPAR_INTPNT_ORDER_METHOD

- Groups

- "intpntRegularizationUse"¶
Controls whether regularization is allowed.

- Default
- Accepted
- Example
`M.setSolverParam("intpntRegularizationUse", "on")`

- Generic name
MSK_IPAR_INTPNT_REGULARIZATION_USE

- Groups

- "intpntScaling"¶
Controls how the problem is scaled before the interior-point optimizer is used.

- Default
- Accepted
- Example
`M.setSolverParam("intpntScaling", "free")`

- Generic name
MSK_IPAR_INTPNT_SCALING

- Groups

- "intpntSolveForm"¶
Controls whether the primal or the dual problem is solved.

- Default
- Accepted
- Example
`M.setSolverParam("intpntSolveForm", "free")`

- Generic name
MSK_IPAR_INTPNT_SOLVE_FORM

- Groups

- "intpntStartingPoint"¶
Starting point used by the interior-point optimizer.

- Default
- Accepted
- Example
`M.setSolverParam("intpntStartingPoint", "free")`

- Generic name
MSK_IPAR_INTPNT_STARTING_POINT

- Groups

- "licenseDebug"¶
This option is used to turn on debugging of the license manager.

- Default
- Accepted
- Example
`M.setSolverParam("licenseDebug", "off")`

- Generic name
MSK_IPAR_LICENSE_DEBUG

- Groups

- "licensePauseTime"¶
If

`licenseWait`

is`"on"`

and no license is available, then**MOSEK**sleeps a number of milliseconds between each check of whether a license has become free.- Default
100

- Accepted
[0; 1000000]

- Example
`M.setSolverParam("licensePauseTime", 100)`

- Generic name
MSK_IPAR_LICENSE_PAUSE_TIME

- Groups

- "licenseSuppressExpireWrns"¶
Controls whether license features expire warnings are suppressed.

- Default
- Accepted
- Example
`M.setSolverParam("licenseSuppressExpireWrns", "off")`

- Generic name
MSK_IPAR_LICENSE_SUPPRESS_EXPIRE_WRNS

- Groups

- "licenseTrhExpiryWrn"¶
If a license feature expires in a numbers of days less than the value of this parameter then a warning will be issued.

- Default
7

- Accepted
[0; +inf]

- Example
`M.setSolverParam("licenseTrhExpiryWrn", 7)`

- Generic name
MSK_IPAR_LICENSE_TRH_EXPIRY_WRN

- Groups

- "licenseWait"¶
If all licenses are in use

**MOSEK**returns with an error code. However, by turning on this parameter**MOSEK**will wait for an available license.- Default
- Accepted
- Example
`M.setSolverParam("licenseWait", "off")`

- Generic name
MSK_IPAR_LICENSE_WAIT

- Groups

- "log"¶
Controls the amount of log information. The value 0 implies that all log information is suppressed. A higher level implies that more information is logged.

Please note that if a task is employed to solve a sequence of optimization problems the value of this parameter is reduced by the value of

`logCutSecondOpt`

for the second and any subsequent optimizations.- Default
10

- Accepted
[0; +inf]

- Example
`M.setSolverParam("log", 10)`

- See also
- Generic name
MSK_IPAR_LOG

- Groups

- "logBi"¶
Controls the amount of output printed by the basis identification procedure. A higher level implies that more information is logged.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logBi", 1)`

- Generic name
MSK_IPAR_LOG_BI

- Groups

- "logBiFreq"¶
Controls how frequently the optimizer outputs information about the basis identification and how frequent the user-defined callback function is called.

- Default
2500

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logBiFreq", 2500)`

- Generic name
MSK_IPAR_LOG_BI_FREQ

- Groups

- "logCutSecondOpt"¶
If a task is employed to solve a sequence of optimization problems, then the value of the log levels is reduced by the value of this parameter. E.g

`log`

and`logSim`

are reduced by the value of this parameter for the second and any subsequent optimizations.

- "logExpand"¶
Controls the amount of logging when a data item such as the maximum number constrains is expanded.

- Default
0

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logExpand", 0)`

- Generic name
MSK_IPAR_LOG_EXPAND

- Groups

- "logFile"¶
If turned on, then some log info is printed when a file is written or read.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logFile", 1)`

- Generic name
MSK_IPAR_LOG_FILE

- Groups

- "logInfeasAna"¶
Controls amount of output printed by the infeasibility analyzer procedures. A higher level implies that more information is logged.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logInfeasAna", 1)`

- Generic name
MSK_IPAR_LOG_INFEAS_ANA

- Groups

- "logIntpnt"¶
Controls amount of output printed by the interior-point optimizer. A higher level implies that more information is logged.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logIntpnt", 1)`

- Generic name
MSK_IPAR_LOG_INTPNT

- Groups

- "logLocalInfo"¶
Controls whether local identifying information like environment variables, filenames, IP addresses etc. are printed to the log.

Note that this will only affect some functions. Some functions that specifically emit system information will not be affected.

- Default
- Accepted
- Example
`M.setSolverParam("logLocalInfo", "on")`

- Generic name
MSK_IPAR_LOG_LOCAL_INFO

- Groups

- "logMio"¶
Controls the log level for the mixed-integer optimizer. A higher level implies that more information is logged.

- Default
4

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logMio", 4)`

- Generic name
MSK_IPAR_LOG_MIO

- Groups

- "logMioFreq"¶
Controls how frequent the mixed-integer optimizer prints the log line. It will print line every time

`logMioFreq`

relaxations have been solved.- Default
10

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("logMioFreq", 10)`

- Generic name
MSK_IPAR_LOG_MIO_FREQ

- Groups

- "logOrder"¶
If turned on, then factor lines are added to the log.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logOrder", 1)`

- Generic name
MSK_IPAR_LOG_ORDER

- Groups

- "logPresolve"¶
Controls amount of output printed by the presolve procedure. A higher level implies that more information is logged.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logPresolve", 1)`

- Generic name
MSK_IPAR_LOG_PRESOLVE

- Groups

- "logResponse"¶
Controls amount of output printed when response codes are reported. A higher level implies that more information is logged.

- Default
0

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logResponse", 0)`

- Generic name
MSK_IPAR_LOG_RESPONSE

- Groups

- "logSim"¶
Controls amount of output printed by the simplex optimizer. A higher level implies that more information is logged.

- Default
4

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logSim", 4)`

- Generic name
MSK_IPAR_LOG_SIM

- Groups

- "logSimFreq"¶
Controls how frequent the simplex optimizer outputs information about the optimization and how frequent the user-defined callback function is called.

- Default
1000

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logSimFreq", 1000)`

- Generic name
MSK_IPAR_LOG_SIM_FREQ

- Groups

- "logSimMinor"¶
Currently not in use.

- Default
1

- Accepted
[0; +inf]

- Example
`M.setSolverParam("logSimMinor", 1)`

- Generic name
MSK_IPAR_LOG_SIM_MINOR

- Groups

- "mioBranchDir"¶
Controls whether the mixed-integer optimizer is branching up or down by default.

- "mioConicOuterApproximation"¶
If this option is turned on outer approximation is used when solving relaxations of conic problems; otherwise interior point is used.

- Default
- Accepted
- Example
`M.setSolverParam("mioConicOuterApproximation", "off")`

- Generic name
MSK_IPAR_MIO_CONIC_OUTER_APPROXIMATION

- Groups

- "mioConstructSol"¶
If set to

`"on"`

and all integer variables have been given a value for which a feasible mixed integer solution exists, then**MOSEK**generates an initial solution to the mixed integer problem by fixing all integer values and solving the remaining problem.- Default
- Accepted
- Example
`M.setSolverParam("mioConstructSol", "off")`

- Generic name
MSK_IPAR_MIO_CONSTRUCT_SOL

- Groups

- "mioCutClique"¶
Controls whether clique cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutClique", "on")`

- Generic name
MSK_IPAR_MIO_CUT_CLIQUE

- Groups

- "mioCutCmir"¶
Controls whether mixed integer rounding cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutCmir", "on")`

- Generic name
MSK_IPAR_MIO_CUT_CMIR

- Groups

- "mioCutGmi"¶
Controls whether GMI cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutGmi", "on")`

- Generic name
MSK_IPAR_MIO_CUT_GMI

- Groups

- "mioCutImpliedBound"¶
Controls whether implied bound cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutImpliedBound", "on")`

- Generic name
MSK_IPAR_MIO_CUT_IMPLIED_BOUND

- Groups

- "mioCutKnapsackCover"¶
Controls whether knapsack cover cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutKnapsackCover", "off")`

- Generic name
MSK_IPAR_MIO_CUT_KNAPSACK_COVER

- Groups

- "mioCutLipro"¶
Controls whether lift-and-project cuts should be generated.

- Default
- Accepted
- Example
`M.setSolverParam("mioCutLipro", "off")`

- Generic name
MSK_IPAR_MIO_CUT_LIPRO

- Groups

- "mioCutSelectionLevel"¶
Controls how aggressively generated cuts are selected to be included in the relaxation.

\(-1\). The optimizer chooses the level of cut selection

\(0\). Generated cuts less likely to be added to the relaxation

\(1\). Cuts are more aggressively selected to be included in the relaxation

- Default
-1

- Accepted
[-1; +1]

- Example
`M.setSolverParam("mioCutSelectionLevel", -1)`

- Generic name
MSK_IPAR_MIO_CUT_SELECTION_LEVEL

- Groups

- "mioDataPermutationMethod"¶
Controls what problem data permutation method is appplied to mixed-integer problems.

- Default
- Accepted
- Example
`M.setSolverParam("mioDataPermutationMethod", "none")`

- Generic name
MSK_IPAR_MIO_DATA_PERMUTATION_METHOD

- Groups

- "mioFeaspumpLevel"¶
Controls the way the Feasibility Pump heuristic is employed by the mixed-integer optimizer.

\(-1\). The optimizer chooses how the Feasibility Pump is used

\(0\). The Feasibility Pump is disabled

\(1\). The Feasibility Pump is enabled with an effort to improve solution quality

\(2\). The Feasibility Pump is enabled with an effort to reach feasibility early

- Default
-1

- Accepted
[-1; 2]

- Example
`M.setSolverParam("mioFeaspumpLevel", -1)`

- Generic name
MSK_IPAR_MIO_FEASPUMP_LEVEL

- Groups

- "mioHeuristicLevel"¶
Controls the heuristic employed by the mixed-integer optimizer to locate an initial good integer feasible solution. A value of zero means the heuristic is not used at all. A larger value than \(0\) means that a gradually more sophisticated heuristic is used which is computationally more expensive. A negative value implies that the optimizer chooses the heuristic. Normally a value around \(3\) to \(5\) should be optimal.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("mioHeuristicLevel", -1)`

- Generic name
MSK_IPAR_MIO_HEURISTIC_LEVEL

- Groups

- "mioMaxNumBranches"¶
Maximum number of branches allowed during the branch and bound search. A negative value means infinite.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("mioMaxNumBranches", -1)`

- Generic name
MSK_IPAR_MIO_MAX_NUM_BRANCHES

- Groups

- "mioMaxNumRelaxs"¶
Maximum number of relaxations allowed during the branch and bound search. A negative value means infinite.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("mioMaxNumRelaxs", -1)`

- Generic name
MSK_IPAR_MIO_MAX_NUM_RELAXS

- Groups

- "mioMaxNumRootCutRounds"¶
Maximum number of cut separation rounds at the root node.

- Default
100

- Accepted
[0; +inf]

- Example
`M.setSolverParam("mioMaxNumRootCutRounds", 100)`

- Generic name
MSK_IPAR_MIO_MAX_NUM_ROOT_CUT_ROUNDS

- Groups

- "mioMaxNumSolutions"¶
The mixed-integer optimizer can be terminated after a certain number of different feasible solutions has been located. If this parameter has the value \(n>0\), then the mixed-integer optimizer will be terminated when \(n\) feasible solutions have been located.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("mioMaxNumSolutions", -1)`

- Generic name
MSK_IPAR_MIO_MAX_NUM_SOLUTIONS

- Groups

- "mioMemoryEmphasisLevel"¶
Controls how much emphasis is put on reducing memory usage. Being more conservative about memory usage may come at the cost of decreased solution speed.

\(0\). The optimizer chooses

\(1\). More emphasis is put on reducing memory usage and less on speed

- Default
0

- Accepted
[0; +1]

- Example
`M.setSolverParam("mioMemoryEmphasisLevel", 0)`

- Generic name
MSK_IPAR_MIO_MEMORY_EMPHASIS_LEVEL

- Groups

- "mioMode"¶
Controls whether the optimizer includes the integer restrictions and disjunctive constraints when solving a (mixed) integer optimization problem.

- Default
- Accepted
- Example
`M.setSolverParam("mioMode", "satisfied")`

- Generic name
MSK_IPAR_MIO_MODE

- Groups

- "mioNodeOptimizer"¶
Controls which optimizer is employed at the non-root nodes in the mixed-integer optimizer.

- Default
- Accepted
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example
`M.setSolverParam("mioNodeOptimizer", "free")`

- Generic name
MSK_IPAR_MIO_NODE_OPTIMIZER

- Groups

- "mioNodeSelection"¶
Controls the node selection strategy employed by the mixed-integer optimizer.

- "mioNumericalEmphasisLevel"¶
Controls how much emphasis is put on reducing numerical problems possibly at the expense of solution speed.

\(0\). The optimizer chooses

\(1\). More emphasis is put on reducing numerical problems

\(2\). Even more emphasis

- Default
0

- Accepted
[0; +2]

- Example
`M.setSolverParam("mioNumericalEmphasisLevel", 0)`

- Generic name
MSK_IPAR_MIO_NUMERICAL_EMPHASIS_LEVEL

- Groups

- "mioPerspectiveReformulate"¶
Enables or disables perspective reformulation in presolve.

- Default
- Accepted
- Example
`M.setSolverParam("mioPerspectiveReformulate", "on")`

- Generic name
MSK_IPAR_MIO_PERSPECTIVE_REFORMULATE

- Groups

- "mioPresolveAggregatorUse"¶
Controls if the aggregator should be used.

- "mioProbingLevel"¶
Controls the amount of probing employed by the mixed-integer optimizer in presolve.

\(-1\). The optimizer chooses the level of probing employed

\(0\). Probing is disabled

\(1\). A low amount of probing is employed

\(2\). A medium amount of probing is employed

\(3\). A high amount of probing is employed

- Default
-1

- Accepted
[-1; 3]

- Example
`M.setSolverParam("mioProbingLevel", -1)`

- Generic name
MSK_IPAR_MIO_PROBING_LEVEL

- Groups

- "mioPropagateObjectiveConstraint"¶
Use objective domain propagation.

- Default
- Accepted
- Example
`M.setSolverParam("mioPropagateObjectiveConstraint", "off")`

- Generic name
MSK_IPAR_MIO_PROPAGATE_OBJECTIVE_CONSTRAINT

- Groups

- "mioQcqoReformulationMethod"¶
Controls what reformulation method is applied to mixed-integer quadratic problems.

- Default
- Accepted
`"free"`

,`"none"`

,`"linearization"`

,`"eigenValMethod"`

,`"diagSdp"`

,`"relaxSdp"`

- Example
`M.setSolverParam("mioQcqoReformulationMethod", "free")`

- Generic name
MSK_IPAR_MIO_QCQO_REFORMULATION_METHOD

- Groups

- "mioRinsMaxNodes"¶
Controls the maximum number of nodes allowed in each call to the RINS heuristic. The default value of -1 means that the value is determined automatically. A value of zero turns off the heuristic.

- Default
-1

- Accepted
[-1; +inf]

- Example
`M.setSolverParam("mioRinsMaxNodes", -1)`

- Generic name
MSK_IPAR_MIO_RINS_MAX_NODES

- Groups

- "mioRootOptimizer"¶
Controls which optimizer is employed at the root node in the mixed-integer optimizer.

- Default
- Accepted
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example
`M.setSolverParam("mioRootOptimizer", "free")`

- Generic name
MSK_IPAR_MIO_ROOT_OPTIMIZER

- Groups

- "mioRootRepeatPresolveLevel"¶
Controls whether presolve can be repeated at root node.

\(-1\). The optimizer chooses whether presolve is repeated

\(0\). Never repeat presolve

\(1\). Always repeat presolve

- Default
-1

- Accepted
[-1; 1]

- Example
`M.setSolverParam("mioRootRepeatPresolveLevel", -1)`

- Generic name
MSK_IPAR_MIO_ROOT_REPEAT_PRESOLVE_LEVEL

- Groups

- "mioSeed"¶
Sets the random seed used for randomization in the mixed integer optimizer. Selecting a different seed can change the path the optimizer takes to the optimal solution.

- Default
42

- Accepted
[0; +inf]

- Example
`M.setSolverParam("mioSeed", 42)`

- Generic name
MSK_IPAR_MIO_SEED

- Groups

- "mioSymmetryLevel"¶
Controls the amount of symmetry detection and handling employed by the mixed-integer optimizer in presolve.

\(-1\). The optimizer chooses the level of symmetry detection and handling employed

\(0\). Symmetry detection and handling is disabled

\(1\). A low amount of symmetry detection and handling is employed

\(2\). A medium amount of symmetry detection and handling is employed

\(3\). A high amount of symmetry detection and handling is employed

\(4\). An extremely high amount of symmetry detection and handling is employed

- Default
-1

- Accepted
[-1; 4]

- Example
`M.setSolverParam("mioSymmetryLevel", -1)`

- Generic name
MSK_IPAR_MIO_SYMMETRY_LEVEL

- Groups

- "mioVbDetectionLevel"¶
Controls how much effort is put into detecting variable bounds.

\(-1\). The optimizer chooses

\(0\). No variable bounds are detected

\(1\). Only detect variable bounds that are directly represented in the problem

\(2\). Detect variable bounds in probing

- Default
-1

- Accepted
[-1; +2]

- Example
`M.setSolverParam("mioVbDetectionLevel", -1)`

- Generic name
MSK_IPAR_MIO_VB_DETECTION_LEVEL

- Groups

- "mtSpincount"¶
Set the number of iterations to spin before sleeping.

- Default
0

- Accepted
[0; 1000000000]

- Example
`M.setSolverParam("mtSpincount", 0)`

- Generic name
MSK_IPAR_MT_SPINCOUNT

- Groups

- "numThreads"¶
Controls the number of threads employed by the optimizer. If set to 0 the number of threads used will be equal to the number of cores detected on the machine.

- Default
0

- Accepted
[0; +inf]

- Example
`M.setSolverParam("numThreads", 0)`

- Generic name
MSK_IPAR_NUM_THREADS

- Groups

- "optimizer"¶
The parameter controls which optimizer is used to optimize the task.

- Default
- Accepted
`"free"`

,`"intpnt"`

,`"conic"`

,`"primalSimplex"`

,`"dualSimplex"`

,`"freeSimplex"`

,`"mixedInt"`

- Example
`M.setSolverParam("optimizer", "free")`

- Generic name
MSK_IPAR_OPTIMIZER

- Groups

- "presolveEliminatorMaxFill"¶
Controls the maximum amount of fill-in that can be created by one pivot in the elimination phase of the presolve. A negative value means the parameter value is selected automatically.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveEliminatorMaxFill", -1)`

- Generic name
MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_FILL

- Groups

- "presolveEliminatorMaxNumTries"¶
Control the maximum number of times the eliminator is tried. A negative value implies

**MOSEK**decides.- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveEliminatorMaxNumTries", -1)`

- Generic name
MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_NUM_TRIES

- Groups

- "presolveLevel"¶
Currently not used.

- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveLevel", -1)`

- Generic name
MSK_IPAR_PRESOLVE_LEVEL

- Groups

- "presolveLindepAbsWorkTrh"¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.

- Default
100

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveLindepAbsWorkTrh", 100)`

- Generic name
MSK_IPAR_PRESOLVE_LINDEP_ABS_WORK_TRH

- Groups

- "presolveLindepRelWorkTrh"¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.

- Default
100

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveLindepRelWorkTrh", 100)`

- Generic name
MSK_IPAR_PRESOLVE_LINDEP_REL_WORK_TRH

- Groups

- "presolveLindepUse"¶
Controls whether the linear constraints are checked for linear dependencies.

- "presolveMaxNumPass"¶
Control the maximum number of times presolve passes over the problem. A negative value implies

**MOSEK**decides.- Default
-1

- Accepted
[-inf; +inf]

- Example
`M.setSolverParam("presolveMaxNumPass", -1)`

- Generic name
MSK_IPAR_PRESOLVE_MAX_NUM_PASS

- Groups

- "presolveUse"¶
Controls whether the presolve is applied to a problem before it is optimized.

- "remoteUseCompression"¶
Use compression when sending data to an optimization server.

- "removeUnusedSolutions"¶
Removes unused solutions before the optimization is performed.

- Default
- Accepted
- Example
`M.setSolverParam("removeUnusedSolutions", "off")`

- Generic name
MSK_IPAR_REMOVE_UNUSED_SOLUTIONS

- Groups

- "simBasisFactorUse"¶
Controls whether an LU factorization of the basis is used in a hot-start. Forcing a refactorization sometimes improves the stability of the simplex optimizers, but in most cases there is a performance penalty.

- Default
- Accepted
- Example
`M.setSolverParam("simBasisFactorUse", "on")`

- Generic name
MSK_IPAR_SIM_BASIS_FACTOR_USE

- Groups

- "simDegen"¶
Controls how aggressively degeneration is handled.

- Default
- Accepted
- Example
`M.setSolverParam("simDegen", "free")`

- Generic name
MSK_IPAR_SIM_DEGEN

- Groups

- "simDualCrash"¶
Controls whether crashing is performed in the dual simplex optimizer. If this parameter is set to \(x\), then a crash will be performed if a basis consists of more than \((100-x)\mod f_v\) entries, where \(f_v\) is the number of fixed variables.

- Default
90

- Accepted
[0; +inf]

- Example
`M.setSolverParam("simDualCrash", 90)`

- Generic name
MSK_IPAR_SIM_DUAL_CRASH

- Groups

- "simDualPhaseoneMethod"¶
An experimental feature.

- Default
0

- Accepted
[0; 10]

- Example
`M.setSolverParam("simDualPhaseoneMethod", 0)`

- Generic name
MSK_IPAR_SIM_DUAL_PHASEONE_METHOD

- Groups

- "simDualRestrictSelection"¶
The dual simplex optimizer can use a so-called restricted selection/pricing strategy to choose the outgoing variable. Hence, if restricted selection is applied, then the dual simplex optimizer first choose a subset of all the potential outgoing variables. Next, for some time it will choose the outgoing variable only among the subset. From time to time the subset is redefined. A larger value of this parameter implies that the optimizer will be more aggressive in its restriction strategy, i.e. a value of 0 implies that the restriction strategy is not applied at all.

- Default
50

- Accepted
[0; 100]

- Example
`M.setSolverParam("simDualRestrictSelection", 50)`

- Generic name
MSK_IPAR_SIM_DUAL_RESTRICT_SELECTION

- Groups

- "simDualSelection"¶
Controls the choice of the incoming variable, known as the selection strategy, in the dual simplex optimizer.

- "simExploitDupvec"¶
Controls if the simplex optimizers are allowed to exploit duplicated columns.

- Default
- Accepted
- Example
`M.setSolverParam("simExploitDupvec", "off")`

- Generic name
MSK_IPAR_SIM_EXPLOIT_DUPVEC

- Groups

- "simHotstart"¶
Controls the type of hot-start that the simplex optimizer perform.

- Default
- Accepted
- Example
`M.setSolverParam("simHotstart", "free")`

- Generic name
MSK_IPAR_SIM_HOTSTART

- Groups

- "simHotstartLu"¶
Determines if the simplex optimizer should exploit the initial factorization.

- Default
- Accepted
- Example
`M.setSolverParam("simHotstartLu", "on")`

- Generic name
MSK_IPAR_SIM_HOTSTART_LU

- Groups

- "simMaxIterations"¶
Maximum number of iterations that can be used by a simplex optimizer.

- Default
10000000

- Accepted
[0; +inf]

- Example
`M.setSolverParam("simMaxIterations", 10000000)`

- Generic name
MSK_IPAR_SIM_MAX_ITERATIONS

- Groups

- "simMaxNumSetbacks"¶
Controls how many set-backs are allowed within a simplex optimizer. A set-back is an event where the optimizer moves in the wrong direction. This is impossible in theory but may happen due to numerical problems.

- Default
250

- Accepted
[0; +inf]

- Example
`M.setSolverParam("simMaxNumSetbacks", 250)`

- Generic name
MSK_IPAR_SIM_MAX_NUM_SETBACKS

- Groups

- "simNonSingular"¶
Controls if the simplex optimizer ensures a non-singular basis, if possible.

- Default
- Accepted
- Example
`M.setSolverParam("simNonSingular", "on")`

- Generic name
MSK_IPAR_SIM_NON_SINGULAR

- Groups

- "simPrimalCrash"¶
Controls whether crashing is performed in the primal simplex optimizer. In general, if a basis consists of more than (100-this parameter value)% fixed variables, then a crash will be performed.

- Default
90

- Accepted
[0; +inf]

- Example
`M.setSolverParam("simPrimalCrash", 90)`

- Generic name
MSK_IPAR_SIM_PRIMAL_CRASH

- Groups

- "simPrimalPhaseoneMethod"¶
An experimental feature.

- Default
0

- Accepted
[0; 10]

- Example
`M.setSolverParam("simPrimalPhaseoneMethod", 0)`

- Generic name
MSK_IPAR_SIM_PRIMAL_PHASEONE_METHOD

- Groups

- "simPrimalRestrictSelection"¶
The primal simplex optimizer can use a so-called restricted selection/pricing strategy to choose the outgoing variable. Hence, if restricted selection is applied, then the primal simplex optimizer first choose a subset of all the potential incoming variables. Next, for some time it will choose the incoming variable only among the subset. From time to time the subset is redefined. A larger value of this parameter implies that the optimizer will be more aggressive in its restriction strategy, i.e. a value of 0 implies that the restriction strategy is not applied at all.

- Default
50

- Accepted
[0; 100]

- Example
`M.setSolverParam("simPrimalRestrictSelection", 50)`

- Generic name
MSK_IPAR_SIM_PRIMAL_RESTRICT_SELECTION

- Groups

- "simPrimalSelection"¶
Controls the choice of the incoming variable, known as the selection strategy, in the primal simplex optimizer.

- "simRefactorFreq"¶
Controls how frequent the basis is refactorized. The value 0 means that the optimizer determines the best point of refactorization. It is strongly recommended NOT to change this parameter.

- Default
0

- Accepted
[0; +inf]

- Example
`M.setSolverParam("simRefactorFreq", 0)`

- Generic name
MSK_IPAR_SIM_REFACTOR_FREQ

- Groups

- "simReformulation"¶
Controls if the simplex optimizers are allowed to reformulate the problem.

- Default
- Accepted
- Example
`M.setSolverParam("simReformulation", "off")`

- Generic name
MSK_IPAR_SIM_REFORMULATION

- Groups

- "simSaveLu"¶
Controls if the LU factorization stored should be replaced with the LU factorization corresponding to the initial basis.

- Default
- Accepted
- Example
`M.setSolverParam("simSaveLu", "off")`

- Generic name
MSK_IPAR_SIM_SAVE_LU

- Groups

- "simScaling"¶
Controls how much effort is used in scaling the problem before a simplex optimizer is used.

- Default
- Accepted
- Example
`M.setSolverParam("simScaling", "free")`

- Generic name
MSK_IPAR_SIM_SCALING

- Groups

- "simScalingMethod"¶
Controls how the problem is scaled before a simplex optimizer is used.

- Default
- Accepted
- Example
`M.setSolverParam("simScalingMethod", "pow2")`

- Generic name
MSK_IPAR_SIM_SCALING_METHOD

- Groups

- "simSeed"¶
Sets the random seed used for randomization in the simplex optimizers.

- Default
23456

- Accepted
[0; 32749]

- Example
`M.setSolverParam("simSeed", 23456)`

- Generic name
MSK_IPAR_SIM_SEED

- Groups

- "simSolveForm"¶
Controls whether the primal or the dual problem is solved by the primal-/dual-simplex optimizer.

- Default
- Accepted
- Example
`M.setSolverParam("simSolveForm", "free")`

- Generic name
MSK_IPAR_SIM_SOLVE_FORM

- Groups

- "simSwitchOptimizer"¶
The simplex optimizer sometimes chooses to solve the dual problem instead of the primal problem. This implies that if you have chosen to use the dual simplex optimizer and the problem is dualized, then it actually makes sense to use the primal simplex optimizer instead. If this parameter is on and the problem is dualized and furthermore the simplex optimizer is chosen to be the primal (dual) one, then it is switched to the dual (primal).

- Default
- Accepted
- Example
`M.setSolverParam("simSwitchOptimizer", "off")`

- Generic name
MSK_IPAR_SIM_SWITCH_OPTIMIZER

- Groups

- "writeJsonIndentation"¶
When set, the JSON task and solution files are written with indentation for better readability.

- Default
- Accepted
- Example
`M.setSolverParam("writeJsonIndentation", "off")`

- Generic name
MSK_IPAR_WRITE_JSON_INDENTATION

- Groups

- "writeLpFullObj"¶
Write all variables, including the ones with 0-coefficients, in the objective.

- Default
- Accepted
- Example
`M.setSolverParam("writeLpFullObj", "on")`

- Generic name
MSK_IPAR_WRITE_LP_FULL_OBJ

- Groups

- "writeLpLineWidth"¶
Maximum width of line in an LP file written by

**MOSEK**.- Default
80

- Accepted
[40; +inf]

- Example
`M.setSolverParam("writeLpLineWidth", 80)`

- Generic name
MSK_IPAR_WRITE_LP_LINE_WIDTH

- Groups

## 14.4.3 String parameters¶

- "basSolFileName"¶
Name of the

`bas`

solution file.- Accepted
Any valid file name.

- Example
`M.setSolverParam("basSolFileName", "somevalue")`

- Generic name
MSK_SPAR_BAS_SOL_FILE_NAME

- Groups

- "dataFileName"¶
Data are read and written to this file.

- Accepted
Any valid file name.

- Example
`M.setSolverParam("dataFileName", "somevalue")`

- Generic name
MSK_SPAR_DATA_FILE_NAME

- Groups

- "intSolFileName"¶
Name of the

`int`

solution file.- Accepted
Any valid file name.

- Example
`M.setSolverParam("intSolFileName", "somevalue")`

- Generic name
MSK_SPAR_INT_SOL_FILE_NAME

- Groups

- "itrSolFileName"¶
Name of the

`itr`

solution file.- Accepted
Any valid file name.

- Example
`M.setSolverParam("itrSolFileName", "somevalue")`

- Generic name
MSK_SPAR_ITR_SOL_FILE_NAME

- Groups

- "remoteOptserverHost"¶
URL of the remote optimization server in the format

`(http|https)://server:port`

. If set, all subsequent calls to any**MOSEK**function that involves synchronous optimization will be sent to the specified OptServer instead of being executed locally. Passing empty string deactivates this redirection.- Accepted
Any valid URL.

- Example
`M.setSolverParam("remoteOptserverHost", "somevalue")`

- Generic name
MSK_SPAR_REMOTE_OPTSERVER_HOST

- Groups

- "remoteTlsCert"¶
List of known server certificates in PEM format.

- Accepted
PEM files separated by new-lines.

- Example
`M.setSolverParam("remoteTlsCert", "somevalue")`

- Generic name
MSK_SPAR_REMOTE_TLS_CERT

- Groups

- "remoteTlsCertPath"¶
Path to known server certificates in PEM format.

- Accepted
Any valid path.

- Example
`M.setSolverParam("remoteTlsCertPath", "somevalue")`

- Generic name
MSK_SPAR_REMOTE_TLS_CERT_PATH

- Groups

- "writeLpGenVarName"¶
Sometimes when an LP file is written additional variables must be inserted. They will have the prefix denoted by this parameter.

- Default
xmskgen

- Accepted
Any valid string.

- Example
`M.setSolverParam("writeLpGenVarName", "xmskgen")`

- Generic name
MSK_SPAR_WRITE_LP_GEN_VAR_NAME

- Groups