GUROBI Simplex Parameter Options

You can change the Simplex options for GUROBI by choosing GUROBI parameters from the Options menu and then pressing the Simplex tab. This will display the dialog box shown below:

Figure 4.62: The Simplex Tab in the GUROBI Options Dialog Box

List of Options

Option Name MPL Name Solver Param ParamNr Type Default Min Max
Simplex Pricing SimplexPricingAlg SIMPLEXPRICING 4 list -1 -1 3
Sifting SiftingLevel SIFTING 27 list -1 -1 2
Sifting Method SiftMethod SIFTMETHOD 28 list -1 -1 2
Feasibility Tolerance FeasibilityTol FEASIBILITYTOL 5 real 1e-6 1e-9 0.01
Optimality Tolerance OptimalityTol OPTIMALITYTOL 6 real 1e-6 1e-9 0.01
Markowitz Tolerance MarkowitzTol MARKOWITZTOL 7 real 0.0078125 1e-4 0.999
Perturbation Value PerturbValue PERTURBVALUE 2 real 0.0002 0.0 0.01
Pricing Norm NormAdjust NORMADJUST 7 int -1 -1 3
Use Scaling ScalingFlag SCALEFLAG 4 flag 1 0 1
Objective Scaling ObjectScale OBJSCALE 3 real -1 -1 MAXREAL
Quad Precision QuadPrecision QUAD 6 list -1 -1 1
Infeasible/Unbounded InfUnbdInfo INFUNBDINFO 21 flag 0 0 1


Description of Options

Simplex Pricing

Determines simplex variable pricing strategy.

Automatic (-1) GUROBI decides pricing strategy.
Partial Pricing (0) Uses partial pricing.
Steepest Edge (1) Use Steepest Edge pricing.
Devex (2) Use Devex pricing.
Quick-Start Steepest Edge (3) Use Quick-Start Steepest Edge pricing.

Sifting

Enables the sifting within dual simplex. Sifting is often useful for LP models where the number of variables is man times larger than the number of constraints.

Automatic (-1) GUROBI decides pricing strategy.
Off (0) No Sifting.
Moderate (1) Moderate Sifting.
Aggressive (2) Aggressive Sifting.

Sifting Method

Sets the LP method used to solve sifting sub-problems.

Automatic (-1) GUROBI decides pricing strategy.
Primal Simplex (0) Uses Primal Simplex.
Dual Simplex (1) Uses Dual Simplex.
Barrier (2) uses Barrier.

Feasibility Tolerance

Primal feasibility tolerance. All constrains must be satisfied to a tolerance of Feasibility Tolerance.

Optimality Tolerance

Dual feasibility tolerance. Reduced costs must all be smaller than Optimality Tolerancel in the improving direction in order for a model to be declared optimal.

Markowitz Tolerance

Threshold pivoting tolerance. Used to limit numerical error in the simplex algorithm. A larger value may avoid numerical problems in rare situations, but it will also harm performance.

Perturbation Value

Magnitude of simplex perturbation (when required).

Pricing Norm

Chooses the pricing norm variants.. The default value of -1 chooses automatically.

Use Scaling

Enables or disables model scaling. Default is On.

Objective Scaling

Divides the model objective by the specified value to avoid numerical errors that may result from very large objective coefficients. The default value of 0 decides on the scaling automatically. A value less than zero uses the maximum coefficient to the specified power as the scaling (so ObjScale=-0.5 would scale by the square root of the largest objective coefficient).

Quad Precision

Enables whether quad precision computation is used in simplex.

Infeasible/Unbounded

Enables or disables whether simplex (and crossover will compute additional information when a model is determined to be infeasible or unbounded.


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