Parameters grouped by meaning and functionality.
Parameters defining the behavior of the interior-point method for linear, conic and convex problems.
Parameters defining the behavior of the simplex optimizer for linear problems.
Parameters defining the behavior of the primal simplex optimizer for linear problems.
Parameters defining the behavior of the dual simplex optimizer for linear problems.
Parameters defining the behavior of the network simplex optimizer for linear problems.
Parameters defining the behavior of the interior-point method for nonlinear convex problems.
mosek.iparam.log_check_convexity
Controls logging in convexity check on quadratic problems. Set to a positive value to turn logging on.
If a quadratic coefficient matrix is found to violate the requirement of PSD (NSD) then a list of negative (positive) pivot elements is printed. The absolute value of the pivot elements is also shown.
Parameters defining the behavior of the interior-point method for conic problems.
Parameters which define termination and optimality criteria and related information.
Parameters defining the overall solver system environment. This includes system and platform related information and behavior.
Parameters defining the behavior of an optimization task when loading data.
Parameters defining the behavior of data readers and writers.
Parameters controling the behaviour of the problem and solution analyzers.
Parameters defining the behavior of solution reader and writer.
These parameters defines data checking settings and problem data tolerances, i.e. which values are rounded to 0 or infinity, and which values are large or small enough to produce a warning.
mosek.iparam.log_check_convexity
Controls logging in convexity check on quadratic problems. Set to a positive value to turn logging on.
If a quadratic coefficient matrix is found to violate the requirement of PSD (NSD) then a list of negative (positive) pivot elements is printed. The absolute value of the pivot elements is also shown.
These parameters defines that can be used when debugging a problem.
Controls the time between calls to the progress call-back function. Hence, if the value of this parameter is for example 10, then the call-back is called approximately each 10 seconds. A negative value is equivalent to infinity.
In general frequent call-backs may hurt the performance.
This parameter controls when the full convexity check declares a problem to be non-convex. Increasing this tolerance relaxes the criteria for declaring the problem non-convex.
A problem is declared non-convex if negative (positive) pivot elements are detected in the cholesky factor of a matrix which is required to be PSD (NSD). This parameter controles how much this non-negativity requirement may be violated.
If is the pivot element for column i, then the matrix Q is considered to not be PSD if:
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Optimality tolerance for the conic solver.
Optimality tolerance for the conic solver.
Optimality tolerance for the conic solver.
Objective bound.
The termination criteria governed by
is disabled the first n seconds. This parameter specifies the number n. A negative value is identical to infinity i.e. the termination criteria are never checked.
Maximum number of relaxations in branch and bound search.
Maximum number of branches allowed during the branch and bound search.
Relaxed absolute optimality tolerance employed by the mixed-integer optimizer.
The mixed-integer optimizer is terminated when this tolerance is satisfied.
Certain termination criteria is disabled within the mixed-integer optimizer for period time specified by the parameter.
Certain termination criteria is disabled within the mixed-integer optimizer for period time specified by the parameter.
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.
Objective bound.
mosek.iparam.log_check_convexity
Controls logging in convexity check on quadratic problems. Set to a positive value to turn logging on.
If a quadratic coefficient matrix is found to violate the requirement of PSD (NSD) then a list of negative (positive) pivot elements is printed. The absolute value of the pivot elements is also shown.
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 mosek.onoffkey.on, -1 is replaced by 1.
This has siginificance for the results returned by the mosek.Task.solvewithbasis function.
Specifies if the license is kept checked out for the lifetime of the mosek environment (on) or returned to the server immediately after the optimization (off).
By default the license is checked out for the lifetime of the MOSEK environment by the first call to mosek.Task.optimizetrm. The license is checked in when the environment is deleted.
A specific license feature may be checked in when not in use with the function mosek.Env.checkinlicense.
Check-in and check-out of licenses have an overhead. Frequent communication with the license server should be avoided.
Turns on basis identification in case the interior-point optimizer is terminated due to maximum number of iterations.
Turns on basis identification in case the interior-point optimizer is terminated due to a numerical problem.
Controls how many offending columns are detected in the Jacobian of the constraint matrix.
1 means aggressive detection, higher values mean less aggressive detection.
0 means no detection.
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 mosek.iparam.log_cut_second_opt for the second and any subsequent optimizations.
Controls the reduction in the log levels for the second and any subsequent optimizations.
Controls logging in convexity check on quadratic problems. Set to a positive value to turn logging on.
If a quadratic coefficient matrix is found to violate the requirement of PSD (NSD) then a list of negative (positive) pivot elements is printed. The absolute value of the pivot elements is also shown.
Controls the amount of log information.
Controls the amount of log information from the interior-point optimizers.
Controls the amount of log information from the mixed-integer optimizers.
Controls the amount of log information from the simplex optimizers.
Controls the cut level employed by the mixed-integer optimizer at the root node. A negative value means a default value determined by the mixed-integer optimizer is used. By adding the appropriate values from the following table the employed cut types can be controlled.
GUB cover | +2 |
Flow cover | +4 |
Lifting | +8 |
Plant location | +16 |
Disaggregation | +32 |
Knapsack cover | +64 |
Lattice | +128 |
Gomory | +256 |
Coefficient reduction | +512 |
GCD | +1024 |
Obj. integrality | +2048 |
Certain termination criteria is disabled within the mixed-integer optimizer for period time specified by the parameter.
Certain termination criteria is disabled within the mixed-integer optimizer for period time specified by the parameter.
Certain termination criteria is disabled within the mixed-integer optimizer for period time specified by the parameter.
If set to mosek.onoffkey.on, then mosek.Task.sensitivityreport analyzes all bounds and variables instead of reading a specification from the file.
Controls whether crashing is performed in the dual simplex optimizer.
In general if a basis consists of more than (100-this parameter value)% fixed variables, then a crash will be performed.
The dual simplex optimizer can use a so-called restricted selection/pricing strategy to chooses 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.
This parameter controls has large the network component in “relative” terms has to be before it is exploited in a simplex hot-start. The network component should be equal or larger than
max(mosek.iparam.sim_network_detect,mosek.iparam.sim_network_detect_hotstart)
before it is exploited. If this value is larger than 100 the network flow component is never detected or exploited.
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.
The primal simplex optimizer can use a so-called restricted selection/pricing strategy to chooses 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.
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.
Controls the data format when a task is written using mosek.Task.writedata.
If the function mosek.Task.relaxprimal adds new constraints to the problem, then they are prefixed by the value of this parameter.
Separator string for names of constraints and variables generated by mosek.Task.relaxprimal.
If defined mosek.Task.sensitivityreport reads this file as a sensitivity analysis data file specifying the type of analysis to be done.
If this is a nonempty string, then mosek.Task.sensitivityreport writes results to this file.