13.3 Parameters (alphabetical list sorted by type)¶
13.3.1 Double parameters¶
- dparam¶
The enumeration type containing all double parameters.
- MSK_DPAR_ANA_SOL_INFEAS_TOL¶
If a constraint violates its bound with an amount larger than this value, the constraint name, index and violation will be printed by the solution analyzer.
- Default
1e-6
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(ANA_SOL_INFEAS_TOL = 1e-6)- Groups
- MSK_DPAR_BASIS_REL_TOL_S¶
Maximum relative dual bound violation allowed in an optimal basic solution.
- Default
1.0e-12
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(BASIS_REL_TOL_S = 1.0e-12)- Groups
- MSK_DPAR_BASIS_TOL_S¶
Maximum absolute dual bound violation in an optimal basic solution.
- Default
1.0e-6
- Accepted
[1.0e-9; +inf]
- Example
prob$dparam <- list(BASIS_TOL_S = 1.0e-6)- Groups
- MSK_DPAR_BASIS_TOL_X¶
Maximum absolute primal bound violation allowed in an optimal basic solution.
- Default
1.0e-6
- Accepted
[1.0e-9; +inf]
- Example
prob$dparam <- list(BASIS_TOL_X = 1.0e-6)- Groups
- MSK_DPAR_CHECK_CONVEXITY_REL_TOL¶
Not in use.
- Default
1e-10
- Accepted
[0; +inf]
- Example
prob$dparam <- list(CHECK_CONVEXITY_REL_TOL = 1e-10)- Groups
- MSK_DPAR_DATA_SYM_MAT_TOL¶
Absolute zero tolerance for elements in in symmetric matrices. If any value in a symmetric matrix is smaller than this parameter in absolute terms MOSEK will treat the values as zero and generate a warning.
- Default
1.0e-12
- Accepted
[1.0e-16; 1.0e-6]
- Example
prob$dparam <- list(DATA_SYM_MAT_TOL = 1.0e-12)- Groups
- MSK_DPAR_DATA_SYM_MAT_TOL_HUGE¶
An element in a symmetric matrix which is larger than this value in absolute size causes an error.
- Default
1.0e20
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_SYM_MAT_TOL_HUGE = 1.0e20)- Groups
- MSK_DPAR_DATA_SYM_MAT_TOL_LARGE¶
An element in a symmetric matrix which is larger than this value in absolute size causes a warning message to be printed.
- Default
1.0e10
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_SYM_MAT_TOL_LARGE = 1.0e10)- Groups
- MSK_DPAR_DATA_TOL_AIJ_HUGE¶
An element in \(A\) which is larger than this value in absolute size causes an error.
- Default
1.0e20
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_AIJ_HUGE = 1.0e20)- Groups
- MSK_DPAR_DATA_TOL_AIJ_LARGE¶
An element in \(A\) which is larger than this value in absolute size causes a warning message to be printed.
- Default
1.0e10
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_AIJ_LARGE = 1.0e10)- Groups
- MSK_DPAR_DATA_TOL_BOUND_INF¶
Any bound which in absolute value is greater than this parameter is considered infinite.
- Default
1.0e16
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_BOUND_INF = 1.0e16)- Groups
- MSK_DPAR_DATA_TOL_BOUND_WRN¶
If a bound value is larger than this value in absolute size, then a warning message is issued.
- Default
1.0e8
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_BOUND_WRN = 1.0e8)- Groups
- MSK_DPAR_DATA_TOL_C_HUGE¶
An element in \(c\) which is larger than the value of this parameter in absolute terms is considered to be huge and generates an error.
- Default
1.0e16
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_C_HUGE = 1.0e16)- Groups
- MSK_DPAR_DATA_TOL_CJ_LARGE¶
An element in \(c\) which is larger than this value in absolute terms causes a warning message to be printed.
- Default
1.0e8
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_CJ_LARGE = 1.0e8)- Groups
- MSK_DPAR_DATA_TOL_QIJ¶
Absolute zero tolerance for elements in \(Q\) matrices.
- Default
1.0e-16
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_QIJ = 1.0e-16)- Groups
- MSK_DPAR_DATA_TOL_X¶
Zero tolerance for constraints and variables i.e. if the distance between the lower and upper bound is less than this value, then the lower and upper bound is considered identical.
- Default
1.0e-8
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(DATA_TOL_X = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_CO_TOL_DFEAS¶
Dual feasibility tolerance used by the interior-point optimizer for conic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_CO_TOL_DFEAS = 1.0e-8)- See also
- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_CO_TOL_INFEAS¶
Infeasibility tolerance used by the interior-point optimizer for conic problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible.
- Default
1.0e-12
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_CO_TOL_INFEAS = 1.0e-12)- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_CO_TOL_MU_RED¶
Relative complementarity gap tolerance used by the interior-point optimizer for conic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_CO_TOL_MU_RED = 1.0e-8)- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_CO_TOL_NEAR_REL¶
Optimality tolerance used by the interior-point optimizer for conic problems. If MOSEK cannot compute a solution that has the prescribed accuracy then it will check if the solution found satisfies the termination criteria with all tolerances multiplied by the value of this parameter. If yes, then the solution is also declared optimal.
- Default
1000
- Accepted
[1.0; +inf]
- Example
prob$dparam <- list(INTPNT_CO_TOL_NEAR_REL = 1000)- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_CO_TOL_PFEAS¶
Primal feasibility tolerance used by the interior-point optimizer for conic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_CO_TOL_PFEAS = 1.0e-8)- See also
- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_CO_TOL_REL_GAP¶
Relative gap termination tolerance used by the interior-point optimizer for conic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_CO_TOL_REL_GAP = 1.0e-8)- See also
- Groups
Interior-point method, Termination criteria, Conic interior-point method
- MSK_DPAR_INTPNT_QO_TOL_DFEAS¶
Dual feasibility tolerance used by the interior-point optimizer for quadratic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_QO_TOL_DFEAS = 1.0e-8)- See also
- Groups
- MSK_DPAR_INTPNT_QO_TOL_INFEAS¶
Infeasibility tolerance used by the interior-point optimizer for quadratic problems. Controls when the interior-point optimizer declares the model primal or dual infeasible. A small number means the optimizer gets more conservative about declaring the model infeasible.
- Default
1.0e-12
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_QO_TOL_INFEAS = 1.0e-12)- Groups
- MSK_DPAR_INTPNT_QO_TOL_MU_RED¶
Relative complementarity gap tolerance used by the interior-point optimizer for quadratic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_QO_TOL_MU_RED = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_QO_TOL_NEAR_REL¶
Optimality tolerance used by the interior-point optimizer for quadratic problems. If MOSEK cannot compute a solution that has the prescribed accuracy then it will check if the solution found satisfies the termination criteria with all tolerances multiplied by the value of this parameter. If yes, then the solution is also declared optimal.
- Default
1000
- Accepted
[1.0; +inf]
- Example
prob$dparam <- list(INTPNT_QO_TOL_NEAR_REL = 1000)- Groups
- MSK_DPAR_INTPNT_QO_TOL_PFEAS¶
Primal feasibility tolerance used by the interior-point optimizer for quadratic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_QO_TOL_PFEAS = 1.0e-8)- See also
- Groups
- MSK_DPAR_INTPNT_QO_TOL_REL_GAP¶
Relative gap termination tolerance used by the interior-point optimizer for quadratic problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_QO_TOL_REL_GAP = 1.0e-8)- See also
- Groups
- MSK_DPAR_INTPNT_TOL_DFEAS¶
Dual feasibility tolerance used by the interior-point optimizer for linear problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_TOL_DFEAS = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_TOL_DSAFE¶
Controls the initial dual starting point used by the interior-point optimizer. If the interior-point optimizer converges slowly and/or the constraint or variable bounds are very large, then it might be worthwhile to increase this value.
- Default
1.0
- Accepted
[1.0e-4; +inf]
- Example
prob$dparam <- list(INTPNT_TOL_DSAFE = 1.0)- Groups
- MSK_DPAR_INTPNT_TOL_INFEAS¶
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
prob$dparam <- list(INTPNT_TOL_INFEAS = 1.0e-10)- Groups
- MSK_DPAR_INTPNT_TOL_MU_RED¶
Relative complementarity gap tolerance used by the interior-point optimizer for linear problems.
- Default
1.0e-16
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_TOL_MU_RED = 1.0e-16)- Groups
- MSK_DPAR_INTPNT_TOL_PATH¶
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
prob$dparam <- list(INTPNT_TOL_PATH = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_TOL_PFEAS¶
Primal feasibility tolerance used by the interior-point optimizer for linear problems.
- Default
1.0e-8
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_TOL_PFEAS = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_TOL_PSAFE¶
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
prob$dparam <- list(INTPNT_TOL_PSAFE = 1.0)- Groups
- MSK_DPAR_INTPNT_TOL_REL_GAP¶
Relative gap termination tolerance used by the interior-point optimizer for linear problems.
- Default
1.0e-8
- Accepted
[1.0e-14; +inf]
- Example
prob$dparam <- list(INTPNT_TOL_REL_GAP = 1.0e-8)- Groups
- MSK_DPAR_INTPNT_TOL_REL_STEP¶
Relative step size to the boundary for linear and quadratic optimization problems.
- Default
0.9999
- Accepted
[1.0e-4; 0.999999]
- Example
prob$dparam <- list(INTPNT_TOL_REL_STEP = 0.9999)- Groups
- MSK_DPAR_INTPNT_TOL_STEP_SIZE¶
Minimal step size tolerance. If the step size falls below the value of this parameter, then the interior-point optimizer assumes that it is stalled. In other words the interior-point optimizer does not make any progress and therefore it is better to stop.
- Default
1.0e-6
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(INTPNT_TOL_STEP_SIZE = 1.0e-6)- Groups
- MSK_DPAR_LOWER_OBJ_CUT¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)
MSK_DPAR_LOWER_OBJ_CUT,MSK_DPAR_UPPER_OBJ_CUT\(]\), then MOSEK is terminated.- Default
-1.0e30
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(LOWER_OBJ_CUT = -1.0e30)- See also
- Groups
- MSK_DPAR_LOWER_OBJ_CUT_FINITE_TRH¶
If the lower objective cut is less than the value of this parameter value, then the lower objective cut i.e.
MSK_DPAR_LOWER_OBJ_CUTis treated as \(-\infty\).- Default
-0.5e30
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(LOWER_OBJ_CUT_FINITE_TRH = -0.5e30)- Groups
- MSK_DPAR_MIO_DJC_MAX_BIGM¶
Maximum allowed big-M value when reformulating disjunctive constraints to linear constraints. Higher values make it more likely that a disjunction is reformulated to linear constraints, but also increase the risk of numerical problems.
- Default
1.0e6
- Accepted
[0; +inf]
- Example
prob$dparam <- list(MIO_DJC_MAX_BIGM = 1.0e6)- Groups
- MSK_DPAR_MIO_MAX_TIME¶
This parameter limits the maximum time spent by the mixed-integer optimizer. A negative number means infinity.
- Default
-1.0
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(MIO_MAX_TIME = -1.0)- Groups
- MSK_DPAR_MIO_REL_GAP_CONST¶
This value is used to compute the relative gap for the solution to an integer optimization problem.
- Default
1.0e-10
- Accepted
[1.0e-15; +inf]
- Example
prob$dparam <- list(MIO_REL_GAP_CONST = 1.0e-10)- Groups
- MSK_DPAR_MIO_TOL_ABS_GAP¶
Absolute optimality tolerance employed by the mixed-integer optimizer.
- Default
0.0
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(MIO_TOL_ABS_GAP = 0.0)- Groups
- MSK_DPAR_MIO_TOL_ABS_RELAX_INT¶
Absolute integer feasibility tolerance. If the distance to the nearest integer is less than this tolerance then an integer constraint is assumed to be satisfied.
- Default
1.0e-5
- Accepted
[1e-9; +inf]
- Example
prob$dparam <- list(MIO_TOL_ABS_RELAX_INT = 1.0e-5)- Groups
- MSK_DPAR_MIO_TOL_FEAS¶
Feasibility tolerance for mixed integer solver.
- Default
1.0e-6
- Accepted
[1e-9; 1e-3]
- Example
prob$dparam <- list(MIO_TOL_FEAS = 1.0e-6)- Groups
- MSK_DPAR_MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT¶
If the relative improvement of the dual bound is smaller than this value, the solver will terminate the root cut generation. A value of 0.0 means that the value is selected automatically.
- Default
0.0
- Accepted
[0.0; 1.0]
- Example
prob$dparam <- list(MIO_TOL_REL_DUAL_BOUND_IMPROVEMENT = 0.0)- Groups
- MSK_DPAR_MIO_TOL_REL_GAP¶
Relative optimality tolerance employed by the mixed-integer optimizer.
- Default
1.0e-4
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(MIO_TOL_REL_GAP = 1.0e-4)- Groups
- MSK_DPAR_OPTIMIZER_MAX_TICKS¶
CURRENTLY NOT IN USE.
Maximum amount of ticks the optimizer is allowed to spent on the optimization. A negative number means infinity.
- Default
-1.0
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(OPTIMIZER_MAX_TICKS = -1.0)- Groups
- MSK_DPAR_OPTIMIZER_MAX_TIME¶
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
prob$dparam <- list(OPTIMIZER_MAX_TIME = -1.0)- Groups
- MSK_DPAR_PRESOLVE_TOL_ABS_LINDEP¶
Absolute tolerance employed by the linear dependency checker.
- Default
1.0e-6
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_ABS_LINDEP = 1.0e-6)- Groups
- MSK_DPAR_PRESOLVE_TOL_AIJ¶
Absolute zero tolerance employed for \(a_{ij}\) in the presolve.
- Default
1.0e-12
- Accepted
[1.0e-15; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_AIJ = 1.0e-12)- Groups
- MSK_DPAR_PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION¶
The presolve is allowed to perturb a bound on a constraint or variable by this amount if it removes an infeasibility.
- Default
1.0e-6
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_PRIMAL_INFEAS_PERTURBATION = 1.0e-6)- Groups
- MSK_DPAR_PRESOLVE_TOL_REL_LINDEP¶
Relative tolerance employed by the linear dependency checker.
- Default
1.0e-10
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_REL_LINDEP = 1.0e-10)- Groups
- MSK_DPAR_PRESOLVE_TOL_S¶
Absolute zero tolerance employed for \(s_i\) in the presolve.
- Default
1.0e-8
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_S = 1.0e-8)- Groups
- MSK_DPAR_PRESOLVE_TOL_X¶
Absolute zero tolerance employed for \(x_j\) in the presolve.
- Default
1.0e-8
- Accepted
[0.0; +inf]
- Example
prob$dparam <- list(PRESOLVE_TOL_X = 1.0e-8)- Groups
- MSK_DPAR_QCQO_REFORMULATE_REL_DROP_TOL¶
This parameter determines when columns are dropped in incomplete Cholesky factorization during reformulation of quadratic problems.
- Default
1e-15
- Accepted
[0; +inf]
- Example
prob$dparam <- list(QCQO_REFORMULATE_REL_DROP_TOL = 1e-15)- Groups
- MSK_DPAR_SEMIDEFINITE_TOL_APPROX¶
Tolerance to define a matrix to be positive semidefinite.
- Default
1.0e-10
- Accepted
[1.0e-15; +inf]
- Example
prob$dparam <- list(SEMIDEFINITE_TOL_APPROX = 1.0e-10)- Groups
- MSK_DPAR_SIM_LU_TOL_REL_PIV¶
Relative pivot tolerance employed when computing the LU factorization of the basis in the simplex optimizers and in the basis identification procedure. A value closer to 1.0 generally improves numerical stability but typically also implies an increase in the computational work.
- Default
0.01
- Accepted
[1.0e-6; 0.999999]
- Example
prob$dparam <- list(SIM_LU_TOL_REL_PIV = 0.01)- Groups
- MSK_DPAR_SIMPLEX_ABS_TOL_PIV¶
Absolute pivot tolerance employed by the simplex optimizers.
- Default
1.0e-7
- Accepted
[1.0e-12; +inf]
- Example
prob$dparam <- list(SIMPLEX_ABS_TOL_PIV = 1.0e-7)- Groups
- MSK_DPAR_UPPER_OBJ_CUT¶
If either a primal or dual feasible solution is found proving that the optimal objective value is outside the interval \([\)
MSK_DPAR_LOWER_OBJ_CUT,MSK_DPAR_UPPER_OBJ_CUT\(]\), then MOSEK is terminated.- Default
1.0e30
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(UPPER_OBJ_CUT = 1.0e30)- See also
- Groups
- MSK_DPAR_UPPER_OBJ_CUT_FINITE_TRH¶
If the upper objective cut is greater than the value of this parameter, then the upper objective cut
MSK_DPAR_UPPER_OBJ_CUTis treated as \(\infty\).- Default
0.5e30
- Accepted
[-inf; +inf]
- Example
prob$dparam <- list(UPPER_OBJ_CUT_FINITE_TRH = 0.5e30)- Groups
13.3.2 Integer parameters¶
- iparam¶
The enumeration type containing all integer parameters.
- MSK_IPAR_ANA_SOL_BASIS¶
Controls whether the basis matrix is analyzed in solution analyzer.
- MSK_IPAR_ANA_SOL_PRINT_VIOLATED¶
A parameter of the problem analyzer. Controls whether a list of violated constraints is printed. All constraints violated by more than the value set by the parameter
MSK_DPAR_ANA_SOL_INFEAS_TOLwill be printed.
- MSK_IPAR_AUTO_SORT_A_BEFORE_OPT¶
Controls whether the elements in each column of \(A\) are sorted before an optimization is performed. This is not required but makes the optimization more deterministic.
- MSK_IPAR_AUTO_UPDATE_SOL_INFO¶
Controls whether the solution information items are automatically updated after an optimization is performed.
- Default
- Accepted
- Example
prob$iparam <- list(AUTO_UPDATE_SOL_INFO = "OFF")- Groups
- MSK_IPAR_BASIS_SOLVE_USE_PLUS_ONE¶
If a slack variable is in the basis, then the corresponding column in the basis is a unit vector with -1 in the right position. However, if this parameter is set to
"MSK_ON", -1 is replaced by 1.- Default
- Accepted
- Example
prob$iparam <- list(BASIS_SOLVE_USE_PLUS_ONE = "OFF")- Groups
- MSK_IPAR_BI_CLEAN_OPTIMIZER¶
Controls which simplex optimizer is used in the clean-up phase. Anything else than
"MSK_OPTIMIZER_PRIMAL_SIMPLEX"or"MSK_OPTIMIZER_DUAL_SIMPLEX"is equivalent to"MSK_OPTIMIZER_FREE_SIMPLEX".- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(BI_CLEAN_OPTIMIZER = "OPTIMIZER_FREE")- Groups
- MSK_IPAR_BI_IGNORE_MAX_ITER¶
If the parameter
MSK_IPAR_INTPNT_BASIShas the value"MSK_BI_NO_ERROR"and the interior-point optimizer has terminated due to maximum number of iterations, then basis identification is performed if this parameter has the value"MSK_ON".- Default
- Accepted
- Example
prob$iparam <- list(BI_IGNORE_MAX_ITER = "OFF")- Groups
- MSK_IPAR_BI_IGNORE_NUM_ERROR¶
If the parameter
MSK_IPAR_INTPNT_BASIShas the value"MSK_BI_NO_ERROR"and the interior-point optimizer has terminated due to a numerical problem, then basis identification is performed if this parameter has the value"MSK_ON".- Default
- Accepted
- Example
prob$iparam <- list(BI_IGNORE_NUM_ERROR = "OFF")- Groups
- MSK_IPAR_BI_MAX_ITERATIONS¶
Controls the maximum number of simplex iterations allowed to optimize a basis after the basis identification.
- Default
1000000
- Accepted
[0; +inf]
- Example
prob$iparam <- list(BI_MAX_ITERATIONS = 1000000)- Groups
- MSK_IPAR_CACHE_LICENSE¶
Specifies if the license is kept checked out for the lifetime of the MOSEK environment/model/process (
"MSK_ON") or returned to the server immediately after the optimization ("MSK_OFF").By default the license is checked out for the lifetime of the session at the start of first optimization.
Check-in and check-out of licenses have an overhead. Frequent communication with the license server should be avoided.
- Default
- Accepted
- Example
prob$iparam <- list(CACHE_LICENSE = "ON")- Groups
- MSK_IPAR_COMPRESS_STATFILE¶
Control compression of stat files.
- MSK_IPAR_INFEAS_GENERIC_NAMES¶
Controls whether generic names are used when an infeasible subproblem is created.
- Default
- Accepted
- Example
prob$iparam <- list(INFEAS_GENERIC_NAMES = "OFF")- Groups
- MSK_IPAR_INFEAS_PREFER_PRIMAL¶
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
prob$iparam <- list(INFEAS_PREFER_PRIMAL = "ON")- Groups
- MSK_IPAR_INFEAS_REPORT_AUTO¶
Controls whether an infeasibility report is automatically produced after the optimization if the problem is primal or dual infeasible.
- Default
- Accepted
- Example
prob$iparam <- list(INFEAS_REPORT_AUTO = "OFF")- Groups
- MSK_IPAR_INFEAS_REPORT_LEVEL¶
Controls the amount of information presented in an infeasibility report. Higher values imply more information.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(INFEAS_REPORT_LEVEL = 1)- Groups
- MSK_IPAR_INTPNT_BASIS¶
Controls whether the interior-point optimizer also computes an optimal basis.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_BASIS = "BI_ALWAYS")- See also
MSK_IPAR_BI_IGNORE_MAX_ITER,MSK_IPAR_BI_IGNORE_NUM_ERROR,MSK_IPAR_BI_MAX_ITERATIONS,MSK_IPAR_BI_CLEAN_OPTIMIZER- Groups
- MSK_IPAR_INTPNT_DIFF_STEP¶
Controls whether different step sizes are allowed in the primal and dual space.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_DIFF_STEP = "ON")- Groups
- MSK_IPAR_INTPNT_HOTSTART¶
Currently not in use.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_HOTSTART = "INTPNT_HOTSTART_NONE")- Groups
- MSK_IPAR_INTPNT_MAX_ITERATIONS¶
Controls the maximum number of iterations allowed in the interior-point optimizer.
- Default
400
- Accepted
[0; +inf]
- Example
prob$iparam <- list(INTPNT_MAX_ITERATIONS = 400)- Groups
- MSK_IPAR_INTPNT_MAX_NUM_COR¶
Controls the maximum number of correctors allowed by the multiple corrector procedure. A negative value means that MOSEK is making the choice.
- Default
-1
- Accepted
[-1; +inf]
- Example
prob$iparam <- list(INTPNT_MAX_NUM_COR = -1)- Groups
- MSK_IPAR_INTPNT_MAX_NUM_REFINEMENT_STEPS¶
Maximum number of steps to be used by the iterative refinement of the search direction. A negative value implies that the optimizer chooses the maximum number of iterative refinement steps.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(INTPNT_MAX_NUM_REFINEMENT_STEPS = -1)- Groups
- MSK_IPAR_INTPNT_OFF_COL_TRH¶
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
prob$iparam <- list(INTPNT_OFF_COL_TRH = 40)- Groups
- MSK_IPAR_INTPNT_ORDER_GP_NUM_SEEDS¶
The GP ordering is dependent on a random seed. Therefore, trying several random seeds may lead to a better ordering. This parameter controls the number of random seeds tried.
A value of 0 means that MOSEK makes the choice.
- Default
0
- Accepted
[0; +inf]
- Example
prob$iparam <- list(INTPNT_ORDER_GP_NUM_SEEDS = 0)- Groups
- MSK_IPAR_INTPNT_ORDER_METHOD¶
Controls the ordering strategy used by the interior-point optimizer when factorizing the Newton equation system.
- Default
- Accepted
"FREE","APPMINLOC","EXPERIMENTAL","TRY_GRAPHPAR","FORCE_GRAPHPAR","NONE"- Example
prob$iparam <- list(INTPNT_ORDER_METHOD = "ORDER_METHOD_FREE")- Groups
- MSK_IPAR_INTPNT_PURIFY¶
Currently not in use.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_PURIFY = "PURIFY_NONE")- Groups
- MSK_IPAR_INTPNT_REGULARIZATION_USE¶
Controls whether regularization is allowed.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_REGULARIZATION_USE = "ON")- Groups
- MSK_IPAR_INTPNT_SCALING¶
Controls how the problem is scaled before the interior-point optimizer is used.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_SCALING = "SCALING_FREE")- Groups
- MSK_IPAR_INTPNT_SOLVE_FORM¶
Controls whether the primal or the dual problem is solved.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_SOLVE_FORM = "SOLVE_FREE")- Groups
- MSK_IPAR_INTPNT_STARTING_POINT¶
Starting point used by the interior-point optimizer.
- Default
- Accepted
- Example
prob$iparam <- list(INTPNT_STARTING_POINT = "STARTING_POINT_FREE")- Groups
- MSK_IPAR_LICENSE_DEBUG¶
This option is used to turn on debugging of the license manager.
- Default
- Accepted
- Example
prob$iparam <- list(LICENSE_DEBUG = "OFF")- Groups
- MSK_IPAR_LICENSE_PAUSE_TIME¶
If
MSK_IPAR_LICENSE_WAITis"MSK_ON"and no license is available, then MOSEK sleeps a number of milliseconds between each check of whether a license has become free.- Default
100
- Accepted
[0; 1000000]
- Example
prob$iparam <- list(LICENSE_PAUSE_TIME = 100)- Groups
- MSK_IPAR_LICENSE_SUPPRESS_EXPIRE_WRNS¶
Controls whether license features expire warnings are suppressed.
- Default
- Accepted
- Example
prob$iparam <- list(LICENSE_SUPPRESS_EXPIRE_WRNS = "OFF")- Groups
- MSK_IPAR_LICENSE_TRH_EXPIRY_WRN¶
If a license feature expires in a numbers of days less than the value of this parameter then a warning will be issued.
- Default
7
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LICENSE_TRH_EXPIRY_WRN = 7)- Groups
- MSK_IPAR_LICENSE_WAIT¶
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
prob$iparam <- list(LICENSE_WAIT = "OFF")- Groups
- MSK_IPAR_LOG¶
Controls the amount of log information. The value 0 implies that all log information is suppressed. A higher level implies that more information is logged.
Please note that if a task is employed to solve a sequence of optimization problems the value of this parameter is reduced by the value of
MSK_IPAR_LOG_CUT_SECOND_OPTfor the second and any subsequent optimizations.- Default
10
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG = 10)- See also
- Groups
- MSK_IPAR_LOG_ANA_PRO¶
Controls amount of output from the problem analyzer.
- MSK_IPAR_LOG_BI¶
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
prob$iparam <- list(LOG_BI = 1)- Groups
- MSK_IPAR_LOG_BI_FREQ¶
Controls how frequently the optimizer outputs information about the basis identification and how frequent the user-defined callback function is called.
- Default
2500
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_BI_FREQ = 2500)- Groups
- MSK_IPAR_LOG_CUT_SECOND_OPT¶
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
MSK_IPAR_LOGandMSK_IPAR_LOG_SIMare reduced by the value of this parameter for the second and any subsequent optimizations.- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_CUT_SECOND_OPT = 1)- See also
MSK_IPAR_LOG,MSK_IPAR_LOG_INTPNT,MSK_IPAR_LOG_MIO,MSK_IPAR_LOG_SIM- Groups
- MSK_IPAR_LOG_EXPAND¶
Controls the amount of logging when a data item such as the maximum number constrains is expanded.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_EXPAND = 1)- Groups
- MSK_IPAR_LOG_FEAS_REPAIR¶
Controls the amount of output printed when performing feasibility repair. A value higher than one means extensive logging.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_FEAS_REPAIR = 1)- Groups
- MSK_IPAR_LOG_FILE¶
If turned on, then some log info is printed when a file is written or read.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_FILE = 1)- Groups
- MSK_IPAR_LOG_INCLUDE_SUMMARY¶
Not relevant for this API.
- Default
- Accepted
- Example
prob$iparam <- list(LOG_INCLUDE_SUMMARY = "OFF")- Groups
- MSK_IPAR_LOG_INFEAS_ANA¶
Controls amount of output printed by the infeasibility analyzer procedures. A higher level implies that more information is logged.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_INFEAS_ANA = 1)- Groups
- MSK_IPAR_LOG_INTPNT¶
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
prob$iparam <- list(LOG_INTPNT = 1)- Groups
- MSK_IPAR_LOG_LOCAL_INFO¶
Controls whether local identifying information like environment variables, filenames, IP addresses etc. are printed to the log.
Note that this will only affect some functions. Some functions that specifically emit system information will not be affected.
- Default
- Accepted
- Example
prob$iparam <- list(LOG_LOCAL_INFO = "ON")- Groups
- MSK_IPAR_LOG_MIO¶
Controls the log level for the mixed-integer optimizer. A higher level implies that more information is logged.
- Default
4
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_MIO = 4)- Groups
- MSK_IPAR_LOG_MIO_FREQ¶
Controls how frequent the mixed-integer optimizer prints the log line. It will print line every time
MSK_IPAR_LOG_MIO_FREQrelaxations have been solved.- Default
10
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(LOG_MIO_FREQ = 10)- Groups
- MSK_IPAR_LOG_ORDER¶
If turned on, then factor lines are added to the log.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_ORDER = 1)- Groups
- MSK_IPAR_LOG_PRESOLVE¶
Controls amount of output printed by the presolve procedure. A higher level implies that more information is logged.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_PRESOLVE = 1)- Groups
- MSK_IPAR_LOG_RESPONSE¶
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
prob$iparam <- list(LOG_RESPONSE = 0)- Groups
- MSK_IPAR_LOG_SENSITIVITY¶
Controls the amount of logging during the sensitivity analysis.
\(0\). Means no logging information is produced.
\(1\). Timing information is printed.
\(2\). Sensitivity results are printed.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_SENSITIVITY = 1)- Groups
- MSK_IPAR_LOG_SENSITIVITY_OPT¶
Controls the amount of logging from the optimizers employed during the sensitivity analysis. 0 means no logging information is produced.
- Default
0
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_SENSITIVITY_OPT = 0)- Groups
- MSK_IPAR_LOG_SIM¶
Controls amount of output printed by the simplex optimizer. A higher level implies that more information is logged.
- Default
4
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_SIM = 4)- Groups
- MSK_IPAR_LOG_SIM_FREQ¶
Controls how frequent the simplex optimizer outputs information about the optimization and how frequent the user-defined callback function is called.
- Default
1000
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_SIM_FREQ = 1000)- Groups
- MSK_IPAR_LOG_SIM_MINOR¶
Currently not in use.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_SIM_MINOR = 1)- Groups
- MSK_IPAR_LOG_STORAGE¶
When turned on, MOSEK prints messages regarding the storage usage and allocation.
- Default
0
- Accepted
[0; +inf]
- Example
prob$iparam <- list(LOG_STORAGE = 0)- Groups
- MSK_IPAR_MAX_NUM_WARNINGS¶
Each warning is shown a limited number of times controlled by this parameter. A negative value is identical to infinite number of times.
- Default
10
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(MAX_NUM_WARNINGS = 10)- Groups
- MSK_IPAR_MIO_BRANCH_DIR¶
Controls whether the mixed-integer optimizer is branching up or down by default.
- MSK_IPAR_MIO_CONIC_OUTER_APPROXIMATION¶
If this option is turned on outer approximation is used when solving relaxations of conic problems; otherwise interior point is used.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CONIC_OUTER_APPROXIMATION = "OFF")- Groups
- MSK_IPAR_MIO_CONSTRUCT_SOL¶
If set to
"MSK_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
prob$iparam <- list(MIO_CONSTRUCT_SOL = "OFF")- Groups
- MSK_IPAR_MIO_CUT_CLIQUE¶
Controls whether clique cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_CLIQUE = "ON")- Groups
- MSK_IPAR_MIO_CUT_CMIR¶
Controls whether mixed integer rounding cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_CMIR = "ON")- Groups
- MSK_IPAR_MIO_CUT_GMI¶
Controls whether GMI cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_GMI = "ON")- Groups
- MSK_IPAR_MIO_CUT_IMPLIED_BOUND¶
Controls whether implied bound cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_IMPLIED_BOUND = "ON")- Groups
- MSK_IPAR_MIO_CUT_KNAPSACK_COVER¶
Controls whether knapsack cover cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_KNAPSACK_COVER = "ON")- Groups
- MSK_IPAR_MIO_CUT_LIPRO¶
Controls whether lift-and-project cuts should be generated.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_CUT_LIPRO = "OFF")- Groups
- MSK_IPAR_MIO_CUT_SELECTION_LEVEL¶
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
prob$iparam <- list(MIO_CUT_SELECTION_LEVEL = -1)- Groups
- MSK_IPAR_MIO_DATA_PERMUTATION_METHOD¶
Controls what problem data permutation method is appplied to mixed-integer problems.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_DATA_PERMUTATION_METHOD = "MIO_DATA_PERMUTATION_METHOD_NONE")- Groups
- MSK_IPAR_MIO_DUAL_RAY_ANALYSIS_LEVEL¶
Controls the amount of symmetry detection and handling employed by the mixed-integer optimizer in presolve.
\(-1\). The optimizer chooses the level of dual ray analysis employed
\(0\). Dual ray analysis is disabled
\(1\). A lower amount of dual ray analysis is employed
\(2\). A higher amount of dual ray analysis is employed
- Default
-1
- Accepted
[-1; 2]
- Example
prob$iparam <- list(MIO_DUAL_RAY_ANALYSIS_LEVEL = -1)- Groups
- MSK_IPAR_MIO_FEASPUMP_LEVEL¶
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
prob$iparam <- list(MIO_FEASPUMP_LEVEL = -1)- Groups
- MSK_IPAR_MIO_HEURISTIC_LEVEL¶
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
prob$iparam <- list(MIO_HEURISTIC_LEVEL = -1)- Groups
- MSK_IPAR_MIO_MAX_NUM_BRANCHES¶
Maximum number of branches allowed during the branch and bound search. A negative value means infinite.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(MIO_MAX_NUM_BRANCHES = -1)- Groups
- MSK_IPAR_MIO_MAX_NUM_RELAXS¶
Maximum number of relaxations allowed during the branch and bound search. A negative value means infinite.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(MIO_MAX_NUM_RELAXS = -1)- Groups
- MSK_IPAR_MIO_MAX_NUM_RESTARTS¶
Maximum number of restarts allowed during the branch and bound search.
- Default
0
- Accepted
[0; +inf]
- Example
prob$iparam <- list(MIO_MAX_NUM_RESTARTS = 0)- Groups
- MSK_IPAR_MIO_MAX_NUM_ROOT_CUT_ROUNDS¶
Maximum number of cut separation rounds at the root node.
- Default
100
- Accepted
[0; +inf]
- Example
prob$iparam <- list(MIO_MAX_NUM_ROOT_CUT_ROUNDS = 100)- Groups
- MSK_IPAR_MIO_MAX_NUM_SOLUTIONS¶
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
prob$iparam <- list(MIO_MAX_NUM_SOLUTIONS = -1)- Groups
- MSK_IPAR_MIO_MEMORY_EMPHASIS_LEVEL¶
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
prob$iparam <- list(MIO_MEMORY_EMPHASIS_LEVEL = 0)- Groups
- MSK_IPAR_MIO_MIN_REL¶
Number of times a variable must have been branched on for its pseudocost to be considered reliable.
- Default
5
- Accepted
[0; +inf]
- Example
prob$iparam <- list(MIO_MIN_REL = 5)- Groups
- MSK_IPAR_MIO_MODE¶
Controls whether the optimizer includes the integer restrictions and disjunctive constraints when solving a (mixed) integer optimization problem.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_MODE = "MIO_MODE_SATISFIED")- Groups
- MSK_IPAR_MIO_NODE_OPTIMIZER¶
Controls which optimizer is employed at the non-root nodes in the mixed-integer optimizer.
- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(MIO_NODE_OPTIMIZER = "OPTIMIZER_FREE")- Groups
- MSK_IPAR_MIO_NODE_SELECTION¶
Controls the node selection strategy employed by the mixed-integer optimizer.
- MSK_IPAR_MIO_NUMERICAL_EMPHASIS_LEVEL¶
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
prob$iparam <- list(MIO_NUMERICAL_EMPHASIS_LEVEL = 0)- Groups
- MSK_IPAR_MIO_PERSPECTIVE_REFORMULATE¶
Enables or disables perspective reformulation in presolve.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_PERSPECTIVE_REFORMULATE = "ON")- Groups
- MSK_IPAR_MIO_PRESOLVE_AGGREGATOR_USE¶
Controls if the aggregator should be used.
- MSK_IPAR_MIO_PROBING_LEVEL¶
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
prob$iparam <- list(MIO_PROBING_LEVEL = -1)- Groups
- MSK_IPAR_MIO_PROPAGATE_OBJECTIVE_CONSTRAINT¶
Use objective domain propagation.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_PROPAGATE_OBJECTIVE_CONSTRAINT = "OFF")- Groups
- MSK_IPAR_MIO_QCQO_REFORMULATION_METHOD¶
Controls what reformulation method is applied to mixed-integer quadratic problems.
- Default
- Accepted
"FREE","NONE","LINEARIZATION","EIGEN_VAL_METHOD","DIAG_SDP","RELAX_SDP"- Example
prob$iparam <- list(MIO_QCQO_REFORMULATION_METHOD = "MIO_QCQO_REFORMULATION_METHOD_FREE")- Groups
- MSK_IPAR_MIO_RINS_MAX_NODES¶
Controls the maximum number of nodes allowed in each call to the RINS heuristic. The default value of -1 means that the value is determined automatically. A value of zero turns off the heuristic.
- Default
-1
- Accepted
[-1; +inf]
- Example
prob$iparam <- list(MIO_RINS_MAX_NODES = -1)- Groups
- MSK_IPAR_MIO_ROOT_OPTIMIZER¶
Controls which optimizer is employed at the root node in the mixed-integer optimizer.
- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(MIO_ROOT_OPTIMIZER = "OPTIMIZER_FREE")- Groups
- MSK_IPAR_MIO_ROOT_REPEAT_PRESOLVE_LEVEL¶
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
prob$iparam <- list(MIO_ROOT_REPEAT_PRESOLVE_LEVEL = -1)- Groups
- MSK_IPAR_MIO_SEED¶
Sets the random seed used for randomization in the mixed integer optimizer. Selecting a different seed can change the path the optimizer takes to the optimal solution.
- Default
42
- Accepted
[0; +inf]
- Example
prob$iparam <- list(MIO_SEED = 42)- Groups
- MSK_IPAR_MIO_SYMMETRY_LEVEL¶
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
prob$iparam <- list(MIO_SYMMETRY_LEVEL = -1)- Groups
- MSK_IPAR_MIO_VAR_SELECTION¶
Controls the variable selection strategy employed by the mixed-integer optimizer.
- Default
- Accepted
- Example
prob$iparam <- list(MIO_VAR_SELECTION = "MIO_VAR_SELECTION_FREE")- Groups
- MSK_IPAR_MIO_VB_DETECTION_LEVEL¶
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
prob$iparam <- list(MIO_VB_DETECTION_LEVEL = -1)- Groups
- MSK_IPAR_MT_SPINCOUNT¶
Set the number of iterations to spin before sleeping.
- Default
0
- Accepted
[0; 1000000000]
- Example
prob$iparam <- list(MT_SPINCOUNT = 0)- Groups
- MSK_IPAR_NUM_THREADS¶
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
prob$iparam <- list(NUM_THREADS = 0)- Groups
- MSK_IPAR_OPF_WRITE_HEADER¶
Write a text header with date and MOSEK version in an OPF file.
- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_HEADER = "ON")- Groups
- MSK_IPAR_OPF_WRITE_HINTS¶
Write a hint section with problem dimensions in the beginning of an OPF file.
- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_HINTS = "ON")- Groups
- MSK_IPAR_OPF_WRITE_LINE_LENGTH¶
Aim to keep lines in OPF files not much longer than this.
- Default
80
- Accepted
[0; +inf]
- Example
prob$iparam <- list(OPF_WRITE_LINE_LENGTH = 80)- Groups
- MSK_IPAR_OPF_WRITE_PARAMETERS¶
Write a parameter section in an OPF file.
- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_PARAMETERS = "OFF")- Groups
- MSK_IPAR_OPF_WRITE_PROBLEM¶
Write objective, constraints, bounds etc. to an OPF file.
- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_PROBLEM = "ON")- Groups
- MSK_IPAR_OPF_WRITE_SOL_BAS¶
If
MSK_IPAR_OPF_WRITE_SOLUTIONSis"MSK_ON"and a basic solution is defined, include the basic solution in OPF files.- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_SOL_BAS = "ON")- Groups
- MSK_IPAR_OPF_WRITE_SOL_ITG¶
If
MSK_IPAR_OPF_WRITE_SOLUTIONSis"MSK_ON"and an integer solution is defined, write the integer solution in OPF files.- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_SOL_ITG = "ON")- Groups
- MSK_IPAR_OPF_WRITE_SOL_ITR¶
If
MSK_IPAR_OPF_WRITE_SOLUTIONSis"MSK_ON"and an interior solution is defined, write the interior solution in OPF files.- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_SOL_ITR = "ON")- Groups
- MSK_IPAR_OPF_WRITE_SOLUTIONS¶
Enable inclusion of solutions in the OPF files.
- Default
- Accepted
- Example
prob$iparam <- list(OPF_WRITE_SOLUTIONS = "OFF")- Groups
- MSK_IPAR_OPTIMIZER¶
The parameter controls which optimizer is used to optimize the task.
- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(OPTIMIZER = "OPTIMIZER_FREE")- Groups
- MSK_IPAR_PARAM_READ_CASE_NAME¶
If turned on, then names in the parameter file are case sensitive.
- Default
- Accepted
- Example
prob$iparam <- list(PARAM_READ_CASE_NAME = "ON")- Groups
- MSK_IPAR_PARAM_READ_IGN_ERROR¶
If turned on, then errors in parameter settings is ignored.
- Default
- Accepted
- Example
prob$iparam <- list(PARAM_READ_IGN_ERROR = "OFF")- Groups
- MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_FILL¶
Controls the maximum amount of fill-in that can be created by one pivot in the elimination phase of the presolve. A negative value means the parameter value is selected automatically.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_ELIMINATOR_MAX_FILL = -1)- Groups
- MSK_IPAR_PRESOLVE_ELIMINATOR_MAX_NUM_TRIES¶
Control the maximum number of times the eliminator is tried. A negative value implies MOSEK decides.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_ELIMINATOR_MAX_NUM_TRIES = -1)- Groups
- MSK_IPAR_PRESOLVE_LEVEL¶
Currently not used.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_LEVEL = -1)- Groups
- MSK_IPAR_PRESOLVE_LINDEP_ABS_WORK_TRH¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.
- Default
100
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_LINDEP_ABS_WORK_TRH = 100)- Groups
- MSK_IPAR_PRESOLVE_LINDEP_NEW¶
Controls whether whether a new experimental linear dependency checker is employed.
- MSK_IPAR_PRESOLVE_LINDEP_REL_WORK_TRH¶
Controls linear dependency check in presolve. The linear dependency check is potentially computationally expensive.
- Default
100
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_LINDEP_REL_WORK_TRH = 100)- Groups
- MSK_IPAR_PRESOLVE_LINDEP_USE¶
Controls whether the linear constraints are checked for linear dependencies.
- MSK_IPAR_PRESOLVE_MAX_NUM_PASS¶
Control the maximum number of times presolve passes over the problem. A negative value implies MOSEK decides.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_MAX_NUM_PASS = -1)- Groups
- MSK_IPAR_PRESOLVE_MAX_NUM_REDUCTIONS¶
Controls the maximum number of reductions performed by the presolve. The value of the parameter is normally only changed in connection with debugging. A negative value implies that an infinite number of reductions are allowed.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(PRESOLVE_MAX_NUM_REDUCTIONS = -1)- Groups
- MSK_IPAR_PRESOLVE_USE¶
Controls whether the presolve is applied to a problem before it is optimized.
- MSK_IPAR_PRIMAL_REPAIR_OPTIMIZER¶
Controls which optimizer that is used to find the optimal repair.
- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(PRIMAL_REPAIR_OPTIMIZER = "OPTIMIZER_FREE")- Groups
- MSK_IPAR_PTF_WRITE_PARAMETERS¶
If
MSK_IPAR_PTF_WRITE_PARAMETERSis"MSK_ON", the parameters section is written.- Default
- Accepted
- Example
prob$iparam <- list(PTF_WRITE_PARAMETERS = "OFF")- Groups
- MSK_IPAR_PTF_WRITE_SOLUTIONS¶
If
MSK_IPAR_PTF_WRITE_SOLUTIONSis"MSK_ON", the solution section is written if any solutions are available, otherwise solution section is not written even if solutions are available.- Default
- Accepted
- Example
prob$iparam <- list(PTF_WRITE_SOLUTIONS = "OFF")- Groups
- MSK_IPAR_PTF_WRITE_TRANSFORM¶
If
MSK_IPAR_PTF_WRITE_TRANSFORMis"MSK_ON", constraint blocks with identifiable conic slacks are transformed into conic constraints and the slacks are eliminated.- Default
- Accepted
- Example
prob$iparam <- list(PTF_WRITE_TRANSFORM = "ON")- Groups
- MSK_IPAR_READ_DEBUG¶
Turns on additional debugging information when reading files.
- Default
- Accepted
- Example
prob$iparam <- list(READ_DEBUG = "OFF")- Groups
- MSK_IPAR_READ_KEEP_FREE_CON¶
Controls whether the free constraints are included in the problem.
- Default
- Accepted
- Example
prob$iparam <- list(READ_KEEP_FREE_CON = "OFF")- Groups
- MSK_IPAR_READ_MPS_FORMAT¶
Controls how strictly the MPS file reader interprets the MPS format.
- MSK_IPAR_READ_MPS_WIDTH¶
Controls the maximal number of characters allowed in one line of the MPS file.
- Default
1024
- Accepted
[80; +inf]
- Example
prob$iparam <- list(READ_MPS_WIDTH = 1024)- Groups
- MSK_IPAR_READ_TASK_IGNORE_PARAM¶
Controls whether MOSEK should ignore the parameter setting defined in the task file and use the default parameter setting instead.
- Default
- Accepted
- Example
prob$iparam <- list(READ_TASK_IGNORE_PARAM = "OFF")- Groups
- MSK_IPAR_REMOTE_USE_COMPRESSION¶
Use compression when sending data to an optimization server.
- MSK_IPAR_REMOVE_UNUSED_SOLUTIONS¶
Removes unused solutions before the optimization is performed.
- Default
- Accepted
- Example
prob$iparam <- list(REMOVE_UNUSED_SOLUTIONS = "OFF")- Groups
- MSK_IPAR_SENSITIVITY_ALL¶
Not applicable.
- Default
- Accepted
- Example
prob$iparam <- list(SENSITIVITY_ALL = "OFF")- Groups
- MSK_IPAR_SENSITIVITY_OPTIMIZER¶
Controls which optimizer is used for optimal partition sensitivity analysis.
- Default
- Accepted
"FREE","INTPNT","CONIC","PRIMAL_SIMPLEX","DUAL_SIMPLEX","FREE_SIMPLEX","MIXED_INT"- Example
prob$iparam <- list(SENSITIVITY_OPTIMIZER = "OPTIMIZER_FREE_SIMPLEX")- Groups
- MSK_IPAR_SENSITIVITY_TYPE¶
Controls which type of sensitivity analysis is to be performed.
- Default
- Accepted
- Example
prob$iparam <- list(SENSITIVITY_TYPE = "SENSITIVITY_TYPE_BASIS")- Groups
- MSK_IPAR_SIM_BASIS_FACTOR_USE¶
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
prob$iparam <- list(SIM_BASIS_FACTOR_USE = "ON")- Groups
- MSK_IPAR_SIM_DEGEN¶
Controls how aggressively degeneration is handled.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_DEGEN = "SIM_DEGEN_FREE")- Groups
- MSK_IPAR_SIM_DETECT_PWL¶
Not in use.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_DETECT_PWL = "ON")- Groups
- MSK_IPAR_SIM_DUAL_CRASH¶
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
prob$iparam <- list(SIM_DUAL_CRASH = 90)- Groups
- MSK_IPAR_SIM_DUAL_PHASEONE_METHOD¶
An experimental feature.
- Default
0
- Accepted
[0; 10]
- Example
prob$iparam <- list(SIM_DUAL_PHASEONE_METHOD = 0)- Groups
- MSK_IPAR_SIM_DUAL_RESTRICT_SELECTION¶
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
prob$iparam <- list(SIM_DUAL_RESTRICT_SELECTION = 50)- Groups
- MSK_IPAR_SIM_DUAL_SELECTION¶
Controls the choice of the incoming variable, known as the selection strategy, in the dual simplex optimizer.
- MSK_IPAR_SIM_EXPLOIT_DUPVEC¶
Controls if the simplex optimizers are allowed to exploit duplicated columns.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_EXPLOIT_DUPVEC = "SIM_EXPLOIT_DUPVEC_OFF")- Groups
- MSK_IPAR_SIM_HOTSTART¶
Controls the type of hot-start that the simplex optimizer perform.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_HOTSTART = "SIM_HOTSTART_FREE")- Groups
- MSK_IPAR_SIM_HOTSTART_LU¶
Determines if the simplex optimizer should exploit the initial factorization.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_HOTSTART_LU = "ON")- Groups
- MSK_IPAR_SIM_MAX_ITERATIONS¶
Maximum number of iterations that can be used by a simplex optimizer.
- Default
10000000
- Accepted
[0; +inf]
- Example
prob$iparam <- list(SIM_MAX_ITERATIONS = 10000000)- Groups
- MSK_IPAR_SIM_MAX_NUM_SETBACKS¶
Controls how many set-backs are allowed within a simplex optimizer. A set-back is an event where the optimizer moves in the wrong direction. This is impossible in theory but may happen due to numerical problems.
- Default
250
- Accepted
[0; +inf]
- Example
prob$iparam <- list(SIM_MAX_NUM_SETBACKS = 250)- Groups
- MSK_IPAR_SIM_NON_SINGULAR¶
Controls if the simplex optimizer ensures a non-singular basis, if possible.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_NON_SINGULAR = "ON")- Groups
- MSK_IPAR_SIM_PRIMAL_CRASH¶
Controls whether crashing is performed in the primal simplex optimizer. In general, if a basis consists of more than (100-this parameter value)% fixed variables, then a crash will be performed.
- Default
90
- Accepted
[0; +inf]
- Example
prob$iparam <- list(SIM_PRIMAL_CRASH = 90)- Groups
- MSK_IPAR_SIM_PRIMAL_PHASEONE_METHOD¶
An experimental feature.
- Default
0
- Accepted
[0; 10]
- Example
prob$iparam <- list(SIM_PRIMAL_PHASEONE_METHOD = 0)- Groups
- MSK_IPAR_SIM_PRIMAL_RESTRICT_SELECTION¶
The primal simplex optimizer can use a so-called restricted selection/pricing strategy to choose the outgoing variable. Hence, if restricted selection is applied, then the primal simplex optimizer first choose a subset of all the potential incoming variables. Next, for some time it will choose the incoming variable only among the subset. From time to time the subset is redefined. A larger value of this parameter implies that the optimizer will be more aggressive in its restriction strategy, i.e. a value of 0 implies that the restriction strategy is not applied at all.
- Default
50
- Accepted
[0; 100]
- Example
prob$iparam <- list(SIM_PRIMAL_RESTRICT_SELECTION = 50)- Groups
- MSK_IPAR_SIM_PRIMAL_SELECTION¶
Controls the choice of the incoming variable, known as the selection strategy, in the primal simplex optimizer.
- MSK_IPAR_SIM_REFACTOR_FREQ¶
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
prob$iparam <- list(SIM_REFACTOR_FREQ = 0)- Groups
- MSK_IPAR_SIM_REFORMULATION¶
Controls if the simplex optimizers are allowed to reformulate the problem.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_REFORMULATION = "SIM_REFORMULATION_OFF")- Groups
- MSK_IPAR_SIM_SAVE_LU¶
Controls if the LU factorization stored should be replaced with the LU factorization corresponding to the initial basis.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_SAVE_LU = "OFF")- Groups
- MSK_IPAR_SIM_SCALING¶
Controls how much effort is used in scaling the problem before a simplex optimizer is used.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_SCALING = "SCALING_FREE")- Groups
- MSK_IPAR_SIM_SCALING_METHOD¶
Controls how the problem is scaled before a simplex optimizer is used.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_SCALING_METHOD = "SCALING_METHOD_POW2")- Groups
- MSK_IPAR_SIM_SEED¶
Sets the random seed used for randomization in the simplex optimizers.
- Default
23456
- Accepted
[0; 32749]
- Example
prob$iparam <- list(SIM_SEED = 23456)- Groups
- MSK_IPAR_SIM_SOLVE_FORM¶
Controls whether the primal or the dual problem is solved by the primal-/dual-simplex optimizer.
- Default
- Accepted
- Example
prob$iparam <- list(SIM_SOLVE_FORM = "SOLVE_FREE")- Groups
- MSK_IPAR_SIM_STABILITY_PRIORITY¶
Controls how high priority the numerical stability should be given.
- Default
50
- Accepted
[0; 100]
- Example
prob$iparam <- list(SIM_STABILITY_PRIORITY = 50)- Groups
- MSK_IPAR_SIM_SWITCH_OPTIMIZER¶
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
prob$iparam <- list(SIM_SWITCH_OPTIMIZER = "OFF")- Groups
- MSK_IPAR_SOL_FILTER_KEEP_BASIC¶
If turned on, then basic and super basic constraints and variables are written to the solution file independent of the filter setting.
- Default
- Accepted
- Example
prob$iparam <- list(SOL_FILTER_KEEP_BASIC = "OFF")- Groups
- MSK_IPAR_SOL_FILTER_KEEP_RANGED¶
If turned on, then ranged constraints and variables are written to the solution file independent of the filter setting.
- Default
- Accepted
- Example
prob$iparam <- list(SOL_FILTER_KEEP_RANGED = "OFF")- Groups
- MSK_IPAR_SOL_READ_NAME_WIDTH¶
When a solution is read by MOSEK and some constraint, variable or cone names contain blanks, then a maximum name width much be specified. A negative value implies that no name contain blanks.
- Default
-1
- Accepted
[-inf; +inf]
- Example
prob$iparam <- list(SOL_READ_NAME_WIDTH = -1)- Groups
- MSK_IPAR_SOL_READ_WIDTH¶
Controls the maximal acceptable width of line in the solutions when read by MOSEK.
- Default
1024
- Accepted
[80; +inf]
- Example
prob$iparam <- list(SOL_READ_WIDTH = 1024)- Groups
- MSK_IPAR_SOLUTION_CALLBACK¶
Indicates whether solution callbacks will be performed during the optimization.
- Default
- Accepted
- Example
prob$iparam <- list(SOLUTION_CALLBACK = "OFF")- Groups
- MSK_IPAR_TIMING_LEVEL¶
Controls the amount of timing performed inside MOSEK.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(TIMING_LEVEL = 1)- Groups
- MSK_IPAR_WRITE_BAS_CONSTRAINTS¶
Controls whether the constraint section is written to the basic solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_BAS_CONSTRAINTS = "ON")- Groups
- MSK_IPAR_WRITE_BAS_HEAD¶
Controls whether the header section is written to the basic solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_BAS_HEAD = "ON")- Groups
- MSK_IPAR_WRITE_BAS_VARIABLES¶
Controls whether the variables section is written to the basic solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_BAS_VARIABLES = "ON")- Groups
- MSK_IPAR_WRITE_COMPRESSION¶
Controls whether the data file is compressed while it is written. 0 means no compression while higher values mean more compression.
- Default
9
- Accepted
[0; +inf]
- Example
prob$iparam <- list(WRITE_COMPRESSION = 9)- Groups
- MSK_IPAR_WRITE_DATA_PARAM¶
If this option is turned on the parameter settings are written to the data file as parameters.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_DATA_PARAM = "OFF")- Groups
- MSK_IPAR_WRITE_FREE_CON¶
Controls whether the free constraints are written to the data file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_FREE_CON = "ON")- Groups
- MSK_IPAR_WRITE_GENERIC_NAMES¶
Controls whether generic names should be used instead of user-defined names when writing to the data file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_GENERIC_NAMES = "OFF")- Groups
- MSK_IPAR_WRITE_GENERIC_NAMES_IO¶
Index origin used in generic names.
- Default
1
- Accepted
[0; +inf]
- Example
prob$iparam <- list(WRITE_GENERIC_NAMES_IO = 1)- Groups
- MSK_IPAR_WRITE_IGNORE_INCOMPATIBLE_ITEMS¶
Controls if the writer ignores incompatible problem items when writing files.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_IGNORE_INCOMPATIBLE_ITEMS = "OFF")- Groups
- MSK_IPAR_WRITE_INT_CONSTRAINTS¶
Controls whether the constraint section is written to the integer solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_INT_CONSTRAINTS = "ON")- Groups
- MSK_IPAR_WRITE_INT_HEAD¶
Controls whether the header section is written to the integer solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_INT_HEAD = "ON")- Groups
- MSK_IPAR_WRITE_INT_VARIABLES¶
Controls whether the variables section is written to the integer solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_INT_VARIABLES = "ON")- Groups
- MSK_IPAR_WRITE_JSON_INDENTATION¶
When set, the JSON task and solution files are written with indentation for better readability.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_JSON_INDENTATION = "OFF")- Groups
- MSK_IPAR_WRITE_LP_FULL_OBJ¶
Write all variables, including the ones with 0-coefficients, in the objective.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_LP_FULL_OBJ = "ON")- Groups
- MSK_IPAR_WRITE_LP_LINE_WIDTH¶
Maximum width of line in an LP file written by MOSEK.
- Default
80
- Accepted
[40; +inf]
- Example
prob$iparam <- list(WRITE_LP_LINE_WIDTH = 80)- Groups
- MSK_IPAR_WRITE_MPS_FORMAT¶
Controls in which format the MPS is written.
- MSK_IPAR_WRITE_MPS_INT¶
Controls if marker records are written to the MPS file to indicate whether variables are integer restricted.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_MPS_INT = "ON")- Groups
- MSK_IPAR_WRITE_SOL_BARVARIABLES¶
Controls whether the symmetric matrix variables section is written to the solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_SOL_BARVARIABLES = "ON")- Groups
- MSK_IPAR_WRITE_SOL_CONSTRAINTS¶
Controls whether the constraint section is written to the solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_SOL_CONSTRAINTS = "ON")- Groups
- MSK_IPAR_WRITE_SOL_HEAD¶
Controls whether the header section is written to the solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_SOL_HEAD = "ON")- Groups
- MSK_IPAR_WRITE_SOL_IGNORE_INVALID_NAMES¶
Even if the names are invalid MPS names, then they are employed when writing the solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_SOL_IGNORE_INVALID_NAMES = "OFF")- Groups
- MSK_IPAR_WRITE_SOL_VARIABLES¶
Controls whether the variables section is written to the solution file.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_SOL_VARIABLES = "ON")- Groups
- MSK_IPAR_WRITE_TASK_INC_SOL¶
Controls whether the solutions are stored in the task file too.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_TASK_INC_SOL = "ON")- Groups
- MSK_IPAR_WRITE_XML_MODE¶
Controls if linear coefficients should be written by row or column when writing in the XML file format.
- Default
- Accepted
- Example
prob$iparam <- list(WRITE_XML_MODE = "WRITE_XML_MODE_ROW")- Groups
13.3.3 String parameters¶
- sparam¶
The enumeration type containing all string parameters.
- MSK_SPAR_BAS_SOL_FILE_NAME¶
Name of the
bassolution file.- Accepted
Any valid file name.
- Example
prob$sparam <- list(BAS_SOL_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_DATA_FILE_NAME¶
Data are read and written to this file.
- Accepted
Any valid file name.
- Example
prob$sparam <- list(DATA_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_DEBUG_FILE_NAME¶
MOSEK debug file.
- Accepted
Any valid file name.
- Example
prob$sparam <- list(DEBUG_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_INT_SOL_FILE_NAME¶
Name of the
intsolution file.- Accepted
Any valid file name.
- Example
prob$sparam <- list(INT_SOL_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_ITR_SOL_FILE_NAME¶
Name of the
itrsolution file.- Accepted
Any valid file name.
- Example
prob$sparam <- list(ITR_SOL_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_MIO_DEBUG_STRING¶
For internal debugging purposes.
- Accepted
Any valid string.
- Example
prob$sparam <- list(MIO_DEBUG_STRING = "somevalue")- Groups
- MSK_SPAR_PARAM_COMMENT_SIGN¶
Only the first character in this string is used. It is considered as a start of comment sign in the MOSEK parameter file. Spaces are ignored in the string.
- Default
%%
- Accepted
Any valid string.
- Example
prob$sparam <- list(PARAM_COMMENT_SIGN = "%%")- Groups
- MSK_SPAR_PARAM_READ_FILE_NAME¶
Modifications to the parameter database is read from this file.
- Accepted
Any valid file name.
- Example
prob$sparam <- list(PARAM_READ_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_PARAM_WRITE_FILE_NAME¶
The parameter database is written to this file.
- Accepted
Any valid file name.
- Example
prob$sparam <- list(PARAM_WRITE_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_READ_MPS_BOU_NAME¶
Name of the BOUNDS vector used. An empty name means that the first BOUNDS vector is used.
- Accepted
Any valid MPS name.
- Example
prob$sparam <- list(READ_MPS_BOU_NAME = "somevalue")- Groups
- MSK_SPAR_READ_MPS_OBJ_NAME¶
Name of the free constraint used as objective function. An empty name means that the first constraint is used as objective function.
- Accepted
Any valid MPS name.
- Example
prob$sparam <- list(READ_MPS_OBJ_NAME = "somevalue")- Groups
- MSK_SPAR_READ_MPS_RAN_NAME¶
Name of the RANGE vector used. An empty name means that the first RANGE vector is used.
- Accepted
Any valid MPS name.
- Example
prob$sparam <- list(READ_MPS_RAN_NAME = "somevalue")- Groups
- MSK_SPAR_READ_MPS_RHS_NAME¶
Name of the RHS used. An empty name means that the first RHS vector is used.
- Accepted
Any valid MPS name.
- Example
prob$sparam <- list(READ_MPS_RHS_NAME = "somevalue")- Groups
- MSK_SPAR_REMOTE_OPTSERVER_HOST¶
URL of the remote optimization server in the format
(http|https)://server:port. If set, all subsequent calls to any MOSEK function that involves synchronous optimization will be sent to the specified OptServer instead of being executed locally. Passing empty string deactivates this redirection.- Accepted
Any valid URL.
- Example
prob$sparam <- list(REMOTE_OPTSERVER_HOST = "somevalue")- Groups
- MSK_SPAR_REMOTE_TLS_CERT¶
List of known server certificates in PEM format.
- Accepted
PEM files separated by new-lines.
- Example
prob$sparam <- list(REMOTE_TLS_CERT = "somevalue")- Groups
- MSK_SPAR_REMOTE_TLS_CERT_PATH¶
Path to known server certificates in PEM format.
- Accepted
Any valid path.
- Example
prob$sparam <- list(REMOTE_TLS_CERT_PATH = "somevalue")- Groups
- MSK_SPAR_SENSITIVITY_FILE_NAME¶
If defined, MOSEK reads this file as a sensitivity analysis data file specifying the type of analysis to be done.
- Accepted
Any valid string.
- Example
prob$sparam <- list(SENSITIVITY_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_SENSITIVITY_RES_FILE_NAME¶
- Accepted
Any valid string.
- Example
prob$sparam <- list(SENSITIVITY_RES_FILE_NAME = "somevalue")- Groups
- MSK_SPAR_SOL_FILTER_XC_LOW¶
A filter used to determine which constraints should be listed in the solution file. A value of \(0.5\) means that all constraints having
xc[i]>0.5should be listed, whereas+0.5means that all constraints havingxc[i]>=blc[i]+0.5should be listed. An empty filter means that no filter is applied.- Accepted
Any valid filter.
- Example
prob$sparam <- list(SOL_FILTER_XC_LOW = "somevalue")- Groups
- MSK_SPAR_SOL_FILTER_XC_UPR¶
A filter used to determine which constraints should be listed in the solution file. A value of
0.5means that all constraints havingxc[i]<0.5should be listed, whereas-0.5means all constraints havingxc[i]<=buc[i]-0.5should be listed. An empty filter means that no filter is applied.- Accepted
Any valid filter.
- Example
prob$sparam <- list(SOL_FILTER_XC_UPR = "somevalue")- Groups
- MSK_SPAR_SOL_FILTER_XX_LOW¶
A filter used to determine which variables should be listed in the solution file. A value of “0.5” means that all constraints having
xx[j]>=0.5should be listed, whereas “+0.5” means that all constraints havingxx[j]>=blx[j]+0.5should be listed. An empty filter means no filter is applied.- Accepted
Any valid filter.
- Example
prob$sparam <- list(SOL_FILTER_XX_LOW = "somevalue")- Groups
- MSK_SPAR_SOL_FILTER_XX_UPR¶
A filter used to determine which variables should be listed in the solution file. A value of “0.5” means that all constraints having
xx[j]<0.5should be printed, whereas “-0.5” means all constraints havingxx[j]<=bux[j]-0.5should be listed. An empty filter means no filter is applied.- Accepted
Any valid file name.
- Example
prob$sparam <- list(SOL_FILTER_XX_UPR = "somevalue")- Groups
- MSK_SPAR_STAT_KEY¶
Key used when writing the summary file.
- Accepted
Any valid string.
- Example
prob$sparam <- list(STAT_KEY = "somevalue")- Groups
- MSK_SPAR_STAT_NAME¶
Name used when writing the statistics file.
- Accepted
Any valid XML string.
- Example
prob$sparam <- list(STAT_NAME = "somevalue")- Groups
- MSK_SPAR_WRITE_LP_GEN_VAR_NAME¶
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
prob$sparam <- list(WRITE_LP_GEN_VAR_NAME = "xmskgen")- Groups
