Solver options
Here are all the options that can be used in SHOT compiled. Note that the default value is normally a good starting point, so do not change options unless you have a good reason.
Dual strategy
These settings control the various functionality of the dual strategy in SHOT, i.e., the polyhedral outer approximation utilizing the ESH or ECP algorithms.
Name and description | Valid values | Default value |
---|---|---|
Dual.CutStrategy Dual cut strategy 0: ESH 1: ECP | {0,1} | 0 |
Extended supporting hyperplane method
These settings control various aspects of the ESH implementation, including the strategy to obtain the interior point.
Name and description | Valid values | Default value |
---|---|---|
Dual.ESH.InteriorPoint.CuttingPlane.ConstraintSelectionFactor The fraction of violated constraints to generate cutting planes for | [0,1] | 0.25 |
Dual.ESH.InteriorPoint.CuttingPlane.IterationLimit Iteration limit for minimax cutting plane solver | {1,...,∞} | 100 |
Dual.ESH.InteriorPoint.CuttingPlane.IterationLimitSubsolver Iteration limit for minimization subsolver | {0,...,∞} | 100 |
Dual.ESH.InteriorPoint.CuttingPlane.Reuse Reuse valid cutting planes in main dual model | true/false | false |
Dual.ESH.InteriorPoint.CuttingPlane.TerminationToleranceAbs Absolute termination tolerance between LP and linesearch objective | [0,∞] | 1 |
Dual.ESH.InteriorPoint.CuttingPlane.TerminationToleranceRel Relative termination tolerance between LP and linesearch objective | [0,∞] | 1 |
Dual.ESH.InteriorPoint.CuttingPlane.TimeLimit Time limit for minimax solver | [0,∞] | 10 |
Dual.ESH.InteriorPoint.MinimaxObjectiveLowerBound Lower bound for minimax objective variable | [-∞,0] | -1000000000000 |
Dual.ESH.InteriorPoint.MinimaxObjectiveUpperBound Upper bound for minimax objective variable | [-∞,∞] | 0.1 |
Dual.ESH.InteriorPoint.UsePrimalSolution Utilize primal solution as interior point 0: No 1: Add as new 2: Replace old 3: Use avarage | {0,...,3} | 1 |
Dual.ESH.Rootsearch.ConstraintTolerance Constraint tolerance for when not to add individual hyperplanes | [0,∞] | 1e-08 |
Dual.ESH.Rootsearch.UniqueConstraints Allow only one hyperplane per constraint per iteration | true/false | false |
Dual.ESH.Rootsearch.UseMaxFunction Perform rootsearch on max function, otherwise on individual constraints | true/false | false |
Generated hyperplane cuts
These settings control how the cutting planes or supporting hyperplanes are generated.
Name and description | Valid values | Default value |
---|---|---|
Dual.HyperplaneCuts.ConstraintSelectionFactor The fraction of violated constraints to generate supporting hyperplanes / cutting planes for | [0,1] | 0.5 |
Dual.HyperplaneCuts.Delay Add hyperplane cuts to model only after optimal MIP solution | true/false | true |
Dual.HyperplaneCuts.MaxConstraintFactor Rootsearch performed on constraints with values larger than this factor times the maximum value | [1e-06,1] | 0.1 |
Dual.HyperplaneCuts.MaxPerIteration Maximal number of hyperplanes to add per iteration | {0,...,∞} | 200 |
Dual.HyperplaneCuts.ObjectiveRootSearch When to use the objective root search 0: Always 1: IfConvex 2: Never | {0,...,2} | 1 |
Dual.HyperplaneCuts.SaveHyperplanePoints Whether to save the points in the generated hyperplanes list | true/false | false |
Dual.HyperplaneCuts.UseIntegerCuts Add integer cuts for infeasible integer-combinations for binary problems | true/false | false |
MIP solver
These settings control the general functionality of the MIP solver in the dual strategy. Note that solver-specific settings for Cplex, Gurobi and Cbc are available under the "Subsolver" category.
Name and description | Valid values | Default value |
---|---|---|
Dual.MIP.CutOff.InitialValue Initial cutoff value to use | [-∞,∞] | 1.7976931348623157e+308 |
Dual.MIP.CutOff.Tolerance An extra tolerance for the objective cutoff value (to prevent infeasible subproblems) | [-∞,∞] | 1e-05 |
Dual.MIP.CutOff.UseInitialValue Use the initial cutoff value | true/false | false |
Dual.MIP.InfeasibilityRepair.IntegerCuts Allow feasibility repair of integer cuts | true/false | true |
Dual.MIP.InfeasibilityRepair.IterationLimit Max number of infeasible problems repaired without primal objective value improvement | {0,...,∞} | 100 |
Dual.MIP.InfeasibilityRepair.TimeLimit Time limit when reparing infeasible problem | [0,∞] | 10 |
Dual.MIP.InfeasibilityRepair.Use Enable the infeasibility repair strategy for nonconvex problems | true/false | true |
Dual.MIP.NodeLimit Node limit to use for MIP solver in single-tree strategy | [0,∞] | 1.7976931348623157e+308 |
Dual.MIP.NumberOfThreads Number of threads to use in MIP solver: 0: Automatic | {0,...,999} | 0 |
Dual.MIP.OptimalityTolerance The reduced-cost tolerance for optimality in the MIP solver | [1e-09,0.01] | 1e-06 |
Dual.MIP.Presolve.Frequency When to call the MIP presolve 0: Never 1: Once 2: Always | {0,...,2} | 1 |
Dual.MIP.Presolve.RemoveRedundantConstraints Remove redundant constraints (as determined by presolve) | true/false | false |
Dual.MIP.Presolve.UpdateObtainedBounds Update bounds (from presolve) to the MIP model | true/false | true |
Dual.MIP.SolutionLimit.ForceOptimal.Iteration Iterations without dual bound updates for forcing optimal MIP solution | {0,...,∞} | 10000 |
Dual.MIP.SolutionLimit.ForceOptimal.Time Time (s) without dual bound updates for forcing optimal MIP solution | [0,∞] | 1000 |
Dual.MIP.SolutionLimit.IncreaseIterations Max number of iterations between MIP solution limit increases | {0,...,∞} | 50 |
Dual.MIP.SolutionLimit.Initial Initial MIP solution limit | {1,...,∞} | 1 |
Dual.MIP.SolutionLimit.UpdateTolerance The constraint tolerance for when to update MIP solution limit | [0,∞] | 0.001 |
Dual.MIP.SolutionPool.Capacity The maximum number of solutions in the solution pool | {0,...,∞} | 100 |
Dual.MIP.Solver Which MIP solver to use 0: Cplex 1: Gurobi 2: Cbc | {0,...,2} | 1 |
Dual.MIP.UpdateObjectiveBounds Update nonlinear objective variable bounds to primal/dual bounds | true/false | false |
Dual reduction cut
These settings control the added dual reduction cuts from the primal solution that will try to force a better primal solution. This functionality is only used if SHOT cannot deduce that the problem is nonconvex .
Name and description | Valid values | Default value |
---|---|---|
Dual.ReductionCut.MaxIterations Max number of primal cut reduction without primal improvement | {0,...,∞} | 5 |
Dual.ReductionCut.ReductionFactor The factor used to reduce the cutoff value | [0,1] | 0.001 |
Dual.ReductionCut.Use Enable the dual reduction cut strategy for nonconvex problems | true/false | true |
Relaxation strategies
These settings contorl various aspects regarding integer-relaxation of the dual problem.
Name and description | Valid values | Default value |
---|---|---|
Dual.Relaxation.Frequency The frequency to solve an LP problem: 0: Disable | {0,...,∞} | 0 |
Dual.Relaxation.IterationLimit The max number of relaxed LP problems to solve initially | {0,...,∞} | 200 |
Dual.Relaxation.MaxLazyConstraints Max number of lazy constraints to add in relaxed solutions in single-tree strategy | {0,...,∞} | 0 |
Dual.Relaxation.TerminationTolerance Time limit (s) when solving LP problems initially | [-∞,∞] | 0.5 |
Dual.Relaxation.TimeLimit Time limit (s) when solving LP problems initially | [0,∞] | 30 |
Dual.Relaxation.Use Initially solve continuous dual relaxations | true/false | true |
Tree strategy
The single-tree strategy is normally more efficient than the multi-tree one. However, not all MIP solvers support the required lazy constraint callbacks. These settings selects this strategy and controls its behaviour.
Name and description | Valid values | Default value |
---|---|---|
Dual.TreeStrategy The main strategy to use 0: Multi-tree 1: Single-tree | {0,1} | 1 |
Optimization model
These settings control various aspects of SHOT's representation for and handling of the provided optimization model.
Bound tightening
SHOT performs bound tightening to strengthen the internal representation of the problem. These settings control how and when bound tightening is performed.
Name and description | Valid values | Default value |
---|---|---|
Model.BoundTightening.FeasibilityBased.MaxIterations Maximal number of bound tightening iterations | {0,...,∞} | 5 |
Model.BoundTightening.FeasibilityBased.TimeLimit Time limit for bound tightening | [0,∞] | 2 |
Model.BoundTightening.FeasibilityBased.Use Peform feasibility-based bound tightening | true/false | true |
Model.BoundTightening.FeasibilityBased.UseNonlinear Peform feasibility-based bound tightening on nonlinear expressions | true/false | true |
Model.BoundTightening.InitialPOA.ConstraintTolerance Constraint termination tolerance | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.CutStrategy Dual cut strategy 0: ESH 1: ECP | {0,1} | 1 |
Model.BoundTightening.InitialPOA.IterationLimit Iteration limit for POA | {0,...,∞} | 50 |
Model.BoundTightening.InitialPOA.ObjectiveConstraintTolerance Objective constraint termination tolerance | [-∞,∞] | 0.001 |
Model.BoundTightening.InitialPOA.ObjectiveGapAbsolute Absolute objective gap termination level | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.ObjectiveGapRelative Relative objective gap termination level | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.StagnationConstraintTolerance Tolerance factor for when no progress is made | [-∞,∞] | 0.01 |
Model.BoundTightening.InitialPOA.StagnationIterationLimit Limit for iterations without significant progress | {0,...,∞} | 5 |
Model.BoundTightening.InitialPOA.TimeLimit Time limit for initial POA | [-∞,∞] | 5 |
Model.BoundTightening.InitialPOA.Use Create an initial polyhedral outer approximation | true/false | false |
Convexity
These settings control the convexity detection functionality
Name and description | Valid values | Default value |
---|---|---|
Model.Convexity.AssumeConvex Assume that the problem is convex | true/false | false |
Model.Convexity.Quadratics.EigenValueTolerance Convexity tolerance for the eigenvalues of the Hessian matrix for quadratic terms | [0,∞] | 1e-05 |
Automatic reformulations
These settings control the automatic reformulations performed in SHOT.
Name and description | Valid values | Default value |
---|---|---|
Model.Reformulation.Bilinear.AddConvexEnvelope Add convex envelopes (subject to original bounds) to bilinear terms | true/false | false |
Model.Reformulation.Bilinear.IntegerFormulation Reformulate integer bilinear terms 0: No 1: No if nonconvex quadratic terms allowed by MIP solver 2: Yes | {0,...,2} | 1 |
Model.Reformulation.Bilinear.IntegerFormulation.MaxDomain Do not reformulate integer variables in bilinear terms which can assume more than this number of discrete values | {2,...,∞} | 100 |
Model.Reformulation.Constraint.PartitionNonlinearTerms When to partition nonlinear sums in constraints 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Constraint.PartitionQuadraticTerms When to partition quadratic sums in constraints 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Monomials.Extract Extract monomial terms from nonlinear expressions | true/false | true |
Model.Reformulation.Monomials.Formulation How to reformulate binary monomials 0: None 1: Simple 2: Costa and Liberti | {0,...,2} | 1 |
Model.Reformulation.ObjectiveFunction.Epigraph.Use Reformulates a nonlinear objective as an auxiliary constraint | true/false | false |
Model.Reformulation.ObjectiveFunction.PartitionNonlinearTerms When to partition nonlinear sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.ObjectiveFunction.PartitionQuadraticTerms When to partition quadratic sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Quadratics.EigenValueDecomposition.Formulation Which formulation to use in eigenvalue decomposition 0: Term coefficient is included in reformulation 1: Term coefficient remains | {0,1} | 0 |
Model.Reformulation.Quadratics.EigenValueDecomposition.Method Whether to use the eigen value decomposition of convex quadratic functions | true/false | false |
Model.Reformulation.Quadratics.EigenValueDecomposition.Use Whether to use the eigenvalue decomposition of convex quadratic functions | true/false | false |
Model.Reformulation.Quadratics.ExtractStrategy How to extract quadratic terms from nonlinear expressions 0: Do not extract 1: Extract to same objective or constraint 2: Extract to quadratic equality constraint if nonconvex 3: Extract to quadratic equality constraint even if convex | {0,...,3} | 1 |
Model.Reformulation.Quadratics.Strategy How to treat quadratic functions 0: All nonlinear 1: Use quadratic objective 2: Use convex quadratic objective and constraints 3: Use nonconvex quadratic objective and constraints | {0,...,3} | 2 |
Model.Reformulation.Signomials.Extract Extract signomial terms from nonlinear expressions | true/false | true |
Variables
These settings control the maximum variable bounds allowed in SHOT. Projection will be performed onto these intervals. Note that the MIP solvers may have stricter requirements, in which case those may be used.
Name and description | Valid values | Default value |
---|---|---|
Model.Variables.Continuous.MaximumUpperBound Maximum upper bound for continuous variables | [-∞,∞] | 1e+50 |
Model.Variables.Continuous.MinimumLowerBound Minimum lower bound for continuous variables | [-∞,∞] | -1e+50 |
Model.Variables.Integer.MaximumUpperBound Maximum upper bound for integer variables | [-∞,∞] | 2000000000 |
Model.Variables.Integer.MinimumLowerBound Minimum lower bound for integer variables | [-∞,∞] | -2000000000 |
Model.Variables.NonlinearObjectiveVariable.Bound Max absolute bound for the auxiliary nonlinear objective variable | [-∞,∞] | 1000000000000 |
Modeling system
These settings control functionality used in the interfaces to different modeling environments.
GAMS interface
These settings control functionality used in the GAMS interface.
Name and description | Valid values | Default value |
---|---|---|
ModelingSystem.GAMS.QExtractAlg Extraction algorithm for quadratic equations in GAMS interface 0: automatic 1: threepass 2: doubleforward | {0,...,2} | 0 |
Solver output
These settings control how much and what output is shown to the user from the solver.
Name and description | Valid values | Default value |
---|---|---|
Output.Console.DualSolver.Show Show output from dual solver on console | true/false | false |
Output.Console.Iteration.Detail When should the fixed strategy be used 0: Full 1: On objective gap update 2: On objective gap update and all primal NLP calls | {0,...,2} | 1 |
Output.Console.LogLevel Log level for console output 0: Trace 1: Debug 2: Info 3: Warning 4: Error 5: Critical 6: Off | {0,...,6} | 2 |
Output.Console.PrimalSolver.Show Show output from primal solver on console | true/false | false |
Output.Debug.Enable Use debug functionality | true/false | false |
Output.Debug.Path The folder where to save the debug information | string | |
Output.File.LogLevel Log level for file output 0: Trace 1: Debug 2: Info 3: Warning 4: Error 5: Critical 6: Off | {0,...,6} | 2 |
Output.GAMS.AlternateSolutionsFile Name of GAMS GDX file to write alternative solutions to | string | |
Output.OutputDirectory Where to save the output files 0: Problem directory 1: Program directory | {0,1} | 1 |
Output.SaveNumberOfSolutions Save max this number of primal solutions to OSrL or GDX file | {0,...,∞} | 1 |
Primal heuristics
These settings control the primal heuristics used in SHOT.
Fixed-integer (NLP) strategy
The main primal strategy in SHOT is to solve integer-fixed NLP problems. These settings control, e.g., how often NLP problems are solved.
Name and description | Valid values | Default value |
---|---|---|
Primal.FixedInteger.CallStrategy When should the fixed strategy be used 0: Use each iteration 1: Based on iteration or time 2: Based on iteration or time, and for all feasible MIP solutions | {0,...,2} | 2 |
Primal.FixedInteger.CreateInfeasibilityCut Create a cut from an infeasible solution point | true/false | false |
Primal.FixedInteger.DualPointGap.Relative If the objective gap between the MIP point and dual solution is less than this the fixed strategy is activated | [0,∞] | 0.001 |
Primal.FixedInteger.Frequency.Dynamic Dynamically update the call frequency based on success | true/false | true |
Primal.FixedInteger.Frequency.Iteration Max number of iterations between calls | {0,...,∞} | 10 |
Primal.FixedInteger.Frequency.Time Max duration (s) between calls | [0,∞] | 5 |
Primal.FixedInteger.IterationLimit Max number of iterations per call | {0,...,∞} | 10000000 |
Primal.FixedInteger.OnlyUniqueIntegerCombinations Whether to resolve with the same integer combination, e.g. for nonconvex problems with different continuous variable starting points | true/false | true |
Primal.FixedInteger.Solver NLP solver to use 0: Ipopt 1: GAMS 2: SHOT | {0,...,2} | 0 |
Primal.FixedInteger.Source Source of fixed MIP solution point 0: All 1: First 2: All feasible 3: First and all feasible 4: With smallest constraint deviation | {0,...,4} | 3 |
Primal.FixedInteger.SourceProblem Which problem formulation to use for NLP problem 0: Original problem 1: Reformulated problem 2: Both | {0,...,2} | 0 |
Primal.FixedInteger.TimeLimit Time limit (s) per NLP problem | [0,∞] | 10 |
Primal.FixedInteger.Use Use the fixed integer primal strategy | true/false | true |
Primal.FixedInteger.Warmstart Warm start the NLP solver | true/false | true |
Primal root search
SHOT can utilize root searches between the dual solution point and an integer-fixed interior point. This setting controls whether this strategy is used.
Name and description | Valid values | Default value |
---|---|---|
Primal.Rootsearch.Use Use a rootsearch to find primal solutions | true/false | true |
Primal.Tolerance.Integer Integer tolerance for accepting primal solutions | [-∞,∞] | 1e-05 |
Primal.Tolerance.LinearConstraint Linear constraint tolerance for accepting primal solutions | [-∞,∞] | 1e-06 |
Primal.Tolerance.NonlinearConstraint Nonlinear constraint tolerance for accepting primal solutions | [-∞,∞] | 1e-05 |
Primal.Tolerance.TrustLinearConstraintValues Trust that subsolvers (NLP, MIP) give primal solutions that respect linear constraints | true/false | true |
Strategy
Overall strategy parameters used in SHOT.
Name and description | Valid values | Default value |
---|---|---|
Strategy.UseRecommendedSettings Modifies some settings to their recommended values based on the strategy | true/false | true |
Subsolver functionality
These settings allow for more direct control of the different subsolvers utilized in SHOT.
Cbc
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Cbc.AutoScale Whether to scale objective, rhs and bounds of problem if they look odd (experimental) | true/false | false |
Subsolver.Cbc.DeterministicParallelMode Run Cbc with multiple threads in deterministic mode | true/false | false |
Subsolver.Cbc.NodeStrategy Node strategy 0: depth 1: downdepth 2: downfewest 3: fewest 4: hybrid 5: updepth 6: upfewest | {0,...,6} | 4 |
Subsolver.Cbc.Scaling Whether to scale problem 0: automatic 1: dynamic 2: equilibrium 3: geometric 4: off 5: rowsonly | {0,...,5} | 4 |
Subsolver.Cbc.Strategy This turns on newer features 0: easy problems 1: default 2: aggressive | {0,...,2} | 1 |
Cplex
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Cplex.AddRelaxedLazyConstraintsAsLocal Whether to add lazy constraints generated in relaxed points as local or global | true/false | false |
Subsolver.Cplex.FeasOptMode Strategy to use for the feasibility repair 0: Minimize the sum of all required relaxations in first phase only 1: Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations 2: Minimize the number of constraints and bounds requiring relaxation in first phase only 3: Minimize the sum of squares of required relaxations in first phase only 4: Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations | {0,...,4} | 0 |
Subsolver.Cplex.MIPEmphasis Sets the MIP emphasis 0: Balanced 1: Feasibility 2: Optimality 3: Best bound 4: Hidden feasible | {0,...,4} | 1 |
Subsolver.Cplex.MemoryEmphasis Try to conserve memory when possible | {0,1} | 0 |
Subsolver.Cplex.NodeFile Where to store the node file 0: No file 1: Compressed in memory 2: On disk 3: Compressed on disk | {0,...,3} | 1 |
Subsolver.Cplex.NumericalEmphasis Emphasis on numerical stability | {0,1} | 1 |
Subsolver.Cplex.OptimalityTarget Specifies how CPLEX treats nonconvex quadratics 0: Automatic 1: Searches for a globally optimal solution to a convex model 2: Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal 3: Searches for a globally optimal solution to a nonconvex model | {0,...,3} | 0 |
Subsolver.Cplex.ParallelMode Controls how much time and memory should be used when filling the solution pool -1: Opportunistic 0: Automatic 1: Deterministic | {-1,...,1} | 0 |
Subsolver.Cplex.Probe Sets the MIP probing level -1: No probing 0: Automatic 1: Moderate 2: Aggressive 3: Very aggressive | {-1,...,3} | 0 |
Subsolver.Cplex.SolutionPoolGap Sets the relative gap filter on objective values in the solution pool | [0,∞] | 1e+75 |
Subsolver.Cplex.SolutionPoolIntensity Controls how much time and memory should be used when filling the solution pool 0: Automatic 1: Mild 2: Moderate 3: Aggressive 4: Very aggressive | {0,...,4} | 0 |
Subsolver.Cplex.SolutionPoolReplace How to replace solutions in the solution pool when full 0: Replace oldest 1: Replace worst 2: Find diverse | {0,...,2} | 0 |
Subsolver.Cplex.UseGenericCallback Use the new generic callback in the single-tree strategy | true/false | false |
Subsolver.Cplex.WorkDirectory Directory for swap file | string | |
Subsolver.Cplex.WorkMemory Memory limit for when to start swapping to disk | [0,∞] | 0 |
GAMS
Settings for the GAMS NLP solvers.
Name and description | Valid values | Default value |
---|---|---|
Subsolver.GAMS.NLP.OptionsFilename Options file for the NLP solver in GAMS | string | |
Subsolver.GAMS.NLP.Solver NLP solver to use in GAMS (auto: SHOT chooses) | string | auto |
Gurobi
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Gurobi.Heuristics The relative amount of time spent in MIP heuristics. | [0,1] | 0.05 |
Subsolver.Gurobi.MIPFocus MIP focus 0: Automatic 1: Feasibility 2: Optimality 3: Best bound | {0,...,3} | 0 |
Subsolver.Gurobi.NumericFocus MIP focus 0: Automatic 1: Mild 2: Moderate 3: Aggressive | {0,...,3} | 1 |
Subsolver.Gurobi.PoolSearchMode Finds extra solutions 0: No extra effort 1: Try to find solutions 2: Find n best solutions | {0,...,2} | 0 |
Subsolver.Gurobi.PoolSolutions Determines how many MIP solutions are stored | {1,...,2000000000} | 10 |
Subsolver.Gurobi.ScaleFlag Controls model scaling -1: Automatic 0: Off 1: Mild 2: Moderate 3: Aggressive | {-1,...,3} | -1 |
Ipopt
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Ipopt.ConstraintViolationTolerance Constraint violation tolerance in Ipopt | [-∞,∞] | 1e-08 |
Subsolver.Ipopt.LinearSolver Ipopt linear subsolver 0: Default 1: MA27 2: MA57 3: MA86 4: MA97 5: MUMPS | {0,...,5} | 0 |
Subsolver.Ipopt.MaxIterations Maximum number of iterations | {0,...,∞} | 1000 |
Subsolver.Ipopt.RelativeConvergenceTolerance Relative convergence tolerance | [-∞,∞] | 1e-08 |
Root search solver
Settings for the Boost rootsearch functionality.
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Rootsearch.ActiveConstraintTolerance Epsilon constraint tolerance for root search | [0,∞] | 0 |
Subsolver.Rootsearch.MaxIterations Maximal root search iterations | {0,...,∞} | 100 |
Subsolver.Rootsearch.Method Root search method to use 0: TOMS748 1: Bisection | {0,1} | 0 |
Subsolver.Rootsearch.TerminationTolerance Epsilon lambda tolerance for root search | [0,∞] | 1e-16 |
SHOT primal NLP solver
Name and description | Valid values | Default value |
---|---|---|
Subsolver.SHOT.ReuseHyperplanes.Fraction The fraction of generated hyperplanes to reuse. | [0,1] | 0.1 |
Subsolver.SHOT.ReuseHyperplanes.Use Reuse valid generated hyperplanes in main dual model. | true/false | true |
Subsolver.SHOT.UseFBBT Do FBBT on NLP problem. | true/false | true |
Termination
These settings control when SHOT will terminate the solution process.
Name and description | Valid values | Default value |
---|---|---|
Termination.ConstraintTolerance Termination tolerance for nonlinear constraints | [0,∞] | 1e-08 |
Termination.DualStagnation.ConstraintTolerance Min absolute difference between max nonlinear constraint errors in subsequent iterations for termination | [0,∞] | 1e-06 |
Termination.DualStagnation.IterationLimit Max number of iterations without significant dual objective value improvement | {0,...,∞} | 2147483647 |
Termination.IterationLimit Iteration limit for main strategy | {1,...,∞} | 200000 |
Termination.ObjectiveConstraintTolerance Termination tolerance for the nonlinear objective constraint | [0,∞] | 1e-08 |
Termination.ObjectiveGap.Absolute Absolute gap termination tolerance for objective function | [0,∞] | 0.001 |
Termination.ObjectiveGap.Relative Relative gap termination tolerance for objective function | [0,∞] | 0.001 |
Termination.PrimalStagnation.IterationLimit Max number of iterations without significant primal objective value improvement | {0,...,∞} | 50 |
Termination.TimeLimit Time limit (s) for solver | [0,∞] | 1.7976931348623157e+308 |
These settings control the various functionality of the dual strategy in SHOT, i.e., the polyhedral outer approximation utilizing the ESH or ECP algorithms.
Name and description | Valid values | Default value |
---|---|---|
Dual.CutStrategy Dual cut strategy 0: ESH 1: ECP | {0,1} | 0 |
Extended supporting hyperplane method
These settings control various aspects of the ESH implementation, including the strategy to obtain the interior point.
Name and description | Valid values | Default value |
---|---|---|
Dual.ESH.InteriorPoint.CuttingPlane.ConstraintSelectionFactor The fraction of violated constraints to generate cutting planes for | [0,1] | 0.25 |
Dual.ESH.InteriorPoint.CuttingPlane.IterationLimit Iteration limit for minimax cutting plane solver | {1,...,∞} | 100 |
Dual.ESH.InteriorPoint.CuttingPlane.IterationLimitSubsolver Iteration limit for minimization subsolver | {0,...,∞} | 100 |
Dual.ESH.InteriorPoint.CuttingPlane.Reuse Reuse valid cutting planes in main dual model | true/false | false |
Dual.ESH.InteriorPoint.CuttingPlane.TerminationToleranceAbs Absolute termination tolerance between LP and linesearch objective | [0,∞] | 1 |
Dual.ESH.InteriorPoint.CuttingPlane.TerminationToleranceRel Relative termination tolerance between LP and linesearch objective | [0,∞] | 1 |
Dual.ESH.InteriorPoint.CuttingPlane.TimeLimit Time limit for minimax solver | [0,∞] | 10 |
Dual.ESH.InteriorPoint.MinimaxObjectiveLowerBound Lower bound for minimax objective variable | [-∞,0] | -1000000000000 |
Dual.ESH.InteriorPoint.MinimaxObjectiveUpperBound Upper bound for minimax objective variable | [-∞,∞] | 0.1 |
Dual.ESH.InteriorPoint.UsePrimalSolution Utilize primal solution as interior point 0: No 1: Add as new 2: Replace old 3: Use avarage | {0,...,3} | 1 |
Dual.ESH.Rootsearch.ConstraintTolerance Constraint tolerance for when not to add individual hyperplanes | [0,∞] | 1e-08 |
Dual.ESH.Rootsearch.UniqueConstraints Allow only one hyperplane per constraint per iteration | true/false | false |
Dual.ESH.Rootsearch.UseMaxFunction Perform rootsearch on max function, otherwise on individual constraints | true/false | false |
Generated hyperplane cuts
These settings control how the cutting planes or supporting hyperplanes are generated.
Name and description | Valid values | Default value |
---|---|---|
Dual.HyperplaneCuts.ConstraintSelectionFactor The fraction of violated constraints to generate supporting hyperplanes / cutting planes for | [0,1] | 0.5 |
Dual.HyperplaneCuts.Delay Add hyperplane cuts to model only after optimal MIP solution | true/false | true |
Dual.HyperplaneCuts.MaxConstraintFactor Rootsearch performed on constraints with values larger than this factor times the maximum value | [1e-06,1] | 0.1 |
Dual.HyperplaneCuts.MaxPerIteration Maximal number of hyperplanes to add per iteration | {0,...,∞} | 200 |
Dual.HyperplaneCuts.ObjectiveRootSearch When to use the objective root search 0: Always 1: IfConvex 2: Never | {0,...,2} | 1 |
Dual.HyperplaneCuts.SaveHyperplanePoints Whether to save the points in the generated hyperplanes list | true/false | false |
Dual.HyperplaneCuts.UseIntegerCuts Add integer cuts for infeasible integer-combinations for binary problems | true/false | false |
MIP solver
These settings control the general functionality of the MIP solver in the dual strategy. Note that solver-specific settings for Cplex, Gurobi and Cbc are available under the "Subsolver" category.
Name and description | Valid values | Default value |
---|---|---|
Dual.MIP.CutOff.InitialValue Initial cutoff value to use | [-∞,∞] | 1.799e+308 |
Dual.MIP.CutOff.Tolerance An extra tolerance for the objective cutoff value (to prevent infeasible subproblems) | [-∞,∞] | 1e-05 |
Dual.MIP.CutOff.UseInitialValue Use the initial cutoff value | true/false | false |
Dual.MIP.InfeasibilityRepair.IntegerCuts Allow feasibility repair of integer cuts | true/false | true |
Dual.MIP.InfeasibilityRepair.IterationLimit Max number of infeasible problems repaired without primal objective value improvement | {0,...,∞} | 100 |
Dual.MIP.InfeasibilityRepair.TimeLimit Time limit when reparing infeasible problem | [0,∞] | 10 |
Dual.MIP.InfeasibilityRepair.Use Enable the infeasibility repair strategy for nonconvex problems | true/false | true |
Dual.MIP.NodeLimit Node limit to use for MIP solver in single-tree strategy | [0,∞] | 1.799e+308 |
Dual.MIP.NumberOfThreads Number of threads to use in MIP solver: 0: Automatic | {0,...,999} | 0 |
Dual.MIP.OptimalityTolerance The reduced-cost tolerance for optimality in the MIP solver | [1e-09,0.01] | 1e-06 |
Dual.MIP.Presolve.Frequency When to call the MIP presolve 0: Never 1: Once 2: Always | {0,...,2} | 1 |
Dual.MIP.Presolve.RemoveRedundantConstraints Remove redundant constraints (as determined by presolve) | true/false | false |
Dual.MIP.Presolve.UpdateObtainedBounds Update bounds (from presolve) to the MIP model | true/false | true |
Dual.MIP.SolutionLimit.ForceOptimal.Iteration Iterations without dual bound updates for forcing optimal MIP solution | {0,...,∞} | 10000 |
Dual.MIP.SolutionLimit.ForceOptimal.Time Time (s) without dual bound updates for forcing optimal MIP solution | [0,∞] | 1000 |
Dual.MIP.SolutionLimit.IncreaseIterations Max number of iterations between MIP solution limit increases | {0,...,∞} | 50 |
Dual.MIP.SolutionLimit.Initial Initial MIP solution limit | {1,...,∞} | 1 |
Dual.MIP.SolutionLimit.UpdateTolerance The constraint tolerance for when to update MIP solution limit | [0,∞] | 0.001 |
Dual.MIP.SolutionPool.Capacity The maximum number of solutions in the solution pool | {0,...,∞} | 100 |
Dual.MIP.Solver Which MIP solver to use 0: Cplex 1: Gurobi 2: Cbc | {0,...,2} | 1 |
Dual.MIP.UpdateObjectiveBounds Update nonlinear objective variable bounds to primal/dual bounds | true/false | false |
Dual reduction cut
These settings control the added dual reduction cuts from the primal solution that will try to force a better primal solution. This functionality is only used if SHOT cannot deduce that the problem is nonconvex .
Name and description | Valid values | Default value |
---|---|---|
Dual.ReductionCut.MaxIterations Max number of primal cut reduction without primal improvement | {0,...,∞} | 5 |
Dual.ReductionCut.ReductionFactor The factor used to reduce the cutoff value | [0,1] | 0.001 |
Dual.ReductionCut.Use Enable the dual reduction cut strategy for nonconvex problems | true/false | true |
Relaxation strategies
These settings contorl various aspects regarding integer-relaxation of the dual problem.
Name and description | Valid values | Default value |
---|---|---|
Dual.Relaxation.Frequency The frequency to solve an LP problem: 0: Disable | {0,...,∞} | 0 |
Dual.Relaxation.IterationLimit The max number of relaxed LP problems to solve initially | {0,...,∞} | 200 |
Dual.Relaxation.MaxLazyConstraints Max number of lazy constraints to add in relaxed solutions in single-tree strategy | {0,...,∞} | 0 |
Dual.Relaxation.TerminationTolerance Time limit (s) when solving LP problems initially | [-∞,∞] | 0.5 |
Dual.Relaxation.TimeLimit Time limit (s) when solving LP problems initially | [0,∞] | 30 |
Dual.Relaxation.Use Initially solve continuous dual relaxations | true/false | true |
Tree strategy
The single-tree strategy is normally more efficient than the multi-tree one. However, not all MIP solvers support the required lazy constraint callbacks. These settings selects this strategy and controls its behaviour.
Name and description | Valid values | Default value |
---|---|---|
Dual.TreeStrategy The main strategy to use 0: Multi-tree 1: Single-tree | {0,1} | 1 |
Optimization model
These settings control various aspects of SHOT's representation for and handling of the provided optimization model.
Bound tightening
SHOT performs bound tightening to strengthen the internal representation of the problem. These settings control how and when bound tightening is performed.
Name and description | Valid values | Default value |
---|---|---|
Model.BoundTightening.FeasibilityBased.MaxIterations Maximal number of bound tightening iterations | {0,...,∞} | 5 |
Model.BoundTightening.FeasibilityBased.TimeLimit Time limit for bound tightening | [0,∞] | 2 |
Model.BoundTightening.FeasibilityBased.Use Peform feasibility-based bound tightening | true/false | true |
Model.BoundTightening.FeasibilityBased.UseNonlinear Peform feasibility-based bound tightening on nonlinear expressions | true/false | true |
Model.BoundTightening.InitialPOA.ConstraintTolerance Constraint termination tolerance | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.CutStrategy Dual cut strategy 0: ESH 1: ECP | {0,1} | 1 |
Model.BoundTightening.InitialPOA.IterationLimit Iteration limit for POA | {0,...,∞} | 50 |
Model.BoundTightening.InitialPOA.ObjectiveConstraintTolerance Objective constraint termination tolerance | [-∞,∞] | 0.001 |
Model.BoundTightening.InitialPOA.ObjectiveGapAbsolute Absolute objective gap termination level | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.ObjectiveGapRelative Relative objective gap termination level | [-∞,∞] | 0.1 |
Model.BoundTightening.InitialPOA.StagnationConstraintTolerance Tolerance factor for when no progress is made | [-∞,∞] | 0.01 |
Model.BoundTightening.InitialPOA.StagnationIterationLimit Limit for iterations without significant progress | {0,...,∞} | 5 |
Model.BoundTightening.InitialPOA.TimeLimit Time limit for initial POA | [-∞,∞] | 5 |
Model.BoundTightening.InitialPOA.Use Create an initial polyhedral outer approximation | true/false | false |
Convexity
These settings control the convexity detection functionality
Name and description | Valid values | Default value |
---|---|---|
Model.Convexity.AssumeConvex Assume that the problem is convex | true/false | false |
Model.Convexity.Quadratics.EigenValueTolerance Convexity tolerance for the eigenvalues of the Hessian matrix for quadratic terms | [0,∞] | 1e-05 |
Automatic reformulations
These settings control the automatic reformulations performed in SHOT.
Name and description | Valid values | Default value |
---|---|---|
Model.Reformulation.Bilinear.AddConvexEnvelope Add convex envelopes (subject to original bounds) to bilinear terms | true/false | false |
Model.Reformulation.Bilinear.IntegerFormulation Reformulate integer bilinear terms 0: No 1: No if nonconvex quadratic terms allowed by MIP solver 2: Yes | {0,...,2} | 1 |
Model.Reformulation.Bilinear.IntegerFormulation.MaxDomain Do not reformulate integer variables in bilinear terms which can assume more than this number of discrete values | {2,...,∞} | 100 |
Model.Reformulation.Constraint.PartitionNonlinearTerms When to partition nonlinear sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Constraint.PartitionQuadraticTerms When to partition quadratic sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Monomials.Extract Extract monomial terms from nonlinear expressions | true/false | true |
Model.Reformulation.Monomials.Formulation How to reformulate binary monomials 0: None 1: Simple 2: Costa and Liberti | {0,...,2} | 1 |
Model.Reformulation.ObjectiveFunction.Epigraph.Use Reformulates a nonlinear objective as an auxiliary constraint | true/false | false |
Model.Reformulation.ObjectiveFunction.PartitionNonlinearTerms When to partition nonlinear sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.ObjectiveFunction.PartitionQuadraticTerms When to partition quadratic sums in objective function 0: Always 1: If result is convex 2: Never | {0,...,2} | 1 |
Model.Reformulation.Quadratics.EigenValueDecomposition.Formulation Which formulation to use in eigenvalue decomposition 0: Term coefficient is included in reformulation 1: Term coefficient remains | {0,1} | 0 |
Model.Reformulation.Quadratics.EigenValueDecomposition.Method Whether to use the eigen value decomposition of convex quadratic functions | true/false | false |
Model.Reformulation.Quadratics.EigenValueDecomposition.Use Whether to use the eigenvalue decomposition of convex quadratic functions | true/false | false |
Model.Reformulation.Quadratics.ExtractStrategy How to extract quadratic terms from nonlinear expressions 0: Do not extract 1: Extract to same objective or constraint 2: Extract to quadratic equality constraint if nonconvex 3: Extract to quadratic equality constraint even if convex | {0,...,3} | 1 |
Model.Reformulation.Quadratics.Strategy How to treat quadratic functions 0: All nonlinear 1: Use quadratic objective 2: Use convex quadratic objective and constraints 3: Use nonconvex quadratic objective and constraints | {0,...,3} | 2 |
Model.Reformulation.Signomials.Extract Extract signomial terms from nonlinear expressions | true/false | true |
Variables
These settings control the maximum variable bounds allowed in SHOT. Projection will be performed onto these intervals. Note that the MIP solvers may have stricter requirements, in which case those may be used.
Name and description | Valid values | Default value |
---|---|---|
Model.Variables.Continuous.MaximumUpperBound Maximum upper bound for continuous variables | [-∞,∞] | 1e+50 |
Model.Variables.Continuous.MinimumLowerBound Minimum lower bound for continuous variables | [-∞,∞] | -1e+50 |
Model.Variables.Integer.MaximumUpperBound Maximum upper bound for integer variables | [-∞,∞] | 2000000000 |
Model.Variables.Integer.MinimumLowerBound Minimum lower bound for integer variables | [-∞,∞] | -2000000000 |
Model.Variables.NonlinearObjectiveVariable.Bound Max absolute bound for the auxiliary nonlinear objective variable | [-∞,∞] | 1000000000000 |
Modeling system
These settings control functionality used in the interfaces to different modeling environments.
GAMS interface
These settings control functionality used in the GAMS interface.
Name and description | Valid values | Default value |
---|---|---|
ModelingSystem.GAMS.QExtractAlg Extraction algorithm for quadratic equations in GAMS interface 0: automatic 1: threepass 2: doubleforward | {0,...,2} | 0 |
Solver output
These settings control how much and what output is shown to the user from the solver.
Name and description | Valid values | Default value |
---|---|---|
Output.Console.DualSolver.Show Show output from dual solver on console | true/false | false |
Output.Console.Iteration.Detail When should the fixed strategy be used 0: Full 1: On objective gap update 2: On objective gap update and all primal NLP calls | {0,...,2} | 1 |
Output.Console.LogLevel Log level for console output 0: Trace 1: Debug 2: Info 3: Warning 4: Error 5: Critical 6: Off | {0,...,6} | 2 |
Output.Console.PrimalSolver.Show Show output from primal solver on console | true/false | false |
Output.Debug.Enable Use debug functionality | true/false | false |
Output.Debug.Path The folder where to save the debug information | string | |
Output.File.LogLevel Log level for file output 0: Trace 1: Debug 2: Info 3: Warning 4: Error 5: Critical 6: Off | {0,...,6} | 2 |
Output.GAMS.AlternateSolutionsFile Name of GAMS GDX file to write alternative solutions to | string | |
Output.OutputDirectory Where to save the output files 0: Problem directory 1: Program directory | {0,1} | 1 |
Output.SaveNumberOfSolutions Save max this number of primal solutions to OSrL or GDX file | {0,...,∞} | 1 |
Primal heuristics
These settings control the primal heuristics used in SHOT.
Fixed-integer (NLP) strategy
The main primal strategy in SHOT is to solve integer-fixed NLP problems. These settings control, e.g., how often NLP problems are solved.
Name and description | Valid values | Default value |
---|---|---|
Primal.FixedInteger.CallStrategy When should the fixed strategy be used 0: Use each iteration 1: Based on iteration or time 2: Based on iteration or time, and for all feasible MIP solutions | {0,...,2} | 2 |
Primal.FixedInteger.CreateInfeasibilityCut Create a cut from an infeasible solution point | true/false | false |
Primal.FixedInteger.DualPointGap.Relative If the objective gap between the MIP point and dual solution is less than this the fixed strategy is activated | [0,∞] | 0.001 |
Primal.FixedInteger.Frequency.Dynamic Dynamically update the call frequency based on success | true/false | true |
Primal.FixedInteger.Frequency.Iteration Max number of iterations between calls | {0,...,∞} | 10 |
Primal.FixedInteger.Frequency.Time Max duration (s) between calls | [0,∞] | 5 |
Primal.FixedInteger.IterationLimit Max number of iterations per call | {0,...,∞} | 10000000 |
Primal.FixedInteger.OnlyUniqueIntegerCombinations Whether to resolve with the same integer combination, e.g. for nonconvex problems with different continuous variable starting points | true/false | true |
Primal.FixedInteger.Solver NLP solver to use 0: Ipopt 1: GAMS 2: SHOT | {0,...,2} | 0 |
Primal.FixedInteger.Source Source of fixed MIP solution point 0: All 1: First 2: All feasible 3: First and all feasible 4: With smallest constraint deviation | {0,...,4} | 3 |
Primal.FixedInteger.SourceProblem Which problem formulation to use for NLP problem 0: Original problem 1: Reformulated problem 2: Both | {0,...,2} | 0 |
Primal.FixedInteger.TimeLimit Time limit (s) per NLP problem | [0,∞] | 10 |
Primal.FixedInteger.Use Use the fixed integer primal strategy | true/false | true |
Primal.FixedInteger.Warmstart Warm start the NLP solver | true/false | true |
Primal root search
SHOT can utilize root searches between the dual solution point and an integer-fixed interior point. This setting controls whether this strategy is used.
Name and description | Valid values | Default value |
---|---|---|
Primal.Rootsearch.Use Use a rootsearch to find primal solutions | true/false | true |
Primal.Tolerance.Integer Integer tolerance for accepting primal solutions | [-∞,∞] | 1e-05 |
Primal.Tolerance.LinearConstraint Linear constraint tolerance for accepting primal solutions | [-∞,∞] | 1e-06 |
Primal.Tolerance.NonlinearConstraint Nonlinear constraint tolerance for accepting primal solutions | [-∞,∞] | 1e-05 |
Primal.Tolerance.TrustLinearConstraintValues Trust that subsolvers (NLP, MIP) give primal solutions that respect linear constraints | true/false | true |
Strategy
Overall strategy parameters used in SHOT.
Name and description | Valid values | Default value |
---|---|---|
Strategy.UseRecommendedSettings Modifies some settings to their recommended values based on the strategy | true/false | true |
Subsolver functionality
These settings allow for more direct control of the different subsolvers utilized in SHOT.
Cbc
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Cbc.AutoScale Whether to scale objective, rhs and bounds of problem if they look odd (experimental) | true/false | false |
Subsolver.Cbc.DeterministicParallelMode Run Cbc with multiple threads in deterministic mode | true/false | false |
Subsolver.Cbc.NodeStrategy Node strategy 0: depth 1: downdepth 2: downfewest 3: fewest 4: hybrid 5: updepth 6: upfewest | {0,...,6} | 4 |
Subsolver.Cbc.Scaling Whether to scale problem 0: automatic 1: dynamic 2: equilibrium 3: geometric 4: off 5: rowsonly | {0,...,5} | 4 |
Subsolver.Cbc.Strategy This turns on newer features 0: easy problems 1: default 2: aggressive | {0,...,2} | 1 |
Cplex
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Cplex.AddRelaxedLazyConstraintsAsLocal Whether to add lazy constraints generated in relaxed points as local or global | true/false | false |
Subsolver.Cplex.FeasOptMode Strategy to use for the feasibility repair 0: Minimize the sum of all required relaxations in first phase only 1: Minimize the sum of all required relaxations in first phase and execute second phase to find optimum among minimal relaxations 2: Minimize the number of constraints and bounds requiring relaxation in first phase only 3: Minimize the sum of squares of required relaxations in first phase only 4: Minimize the sum of squares of required relaxations in first phase and execute second phase to find optimum among minimal relaxations | {0,...,4} | 0 |
Subsolver.Cplex.MIPEmphasis Sets the MIP emphasis 0: Balanced 1: Feasibility 2: Optimality 3: Best bound 4: Hidden feasible | {0,...,4} | 1 |
Subsolver.Cplex.MemoryEmphasis Try to conserve memory when possible | {0,1} | 0 |
Subsolver.Cplex.NodeFile Where to store the node file 0: No file 1: Compressed in memory 2: On disk 3: Compressed on disk | {0,...,3} | 1 |
Subsolver.Cplex.NumericalEmphasis Emphasis on numerical stability | {0,1} | 1 |
Subsolver.Cplex.OptimalityTarget Specifies how CPLEX treats nonconvex quadratics 0: Automatic 1: Searches for a globally optimal solution to a convex model 2: Searches for a solution that satisfies first-order optimality conditions, but is not necessarily globally optimal 3: Searches for a globally optimal solution to a nonconvex model | {0,...,3} | 0 |
Subsolver.Cplex.ParallelMode Controls how much time and memory should be used when filling the solution pool -1: Opportunistic 0: Automatic 1: Deterministic | {-1,...,1} | 0 |
Subsolver.Cplex.Probe Sets the MIP probing level -1: No probing 0: Automatic 1: Moderate 2: Aggressive 3: Very aggressive | {-1,...,3} | 0 |
Subsolver.Cplex.SolutionPoolGap Sets the relative gap filter on objective values in the solution pool | [0,∞] | 1e+75 |
Subsolver.Cplex.SolutionPoolIntensity Controls how much time and memory should be used when filling the solution pool 0: Automatic 1: Mild 2: Moderate 3: Aggressive 4: Very aggressive | {0,...,4} | 0 |
Subsolver.Cplex.SolutionPoolReplace How to replace solutions in the solution pool when full 0: Replace oldest 1: Replace worst 2: Find diverse | {0,...,2} | 0 |
Subsolver.Cplex.UseGenericCallback Use the new generic callback in the single-tree strategy | true/false | false |
Subsolver.Cplex.WorkDirectory Directory for swap file | string | |
Subsolver.Cplex.WorkMemory Memory limit for when to start swapping to disk | [0,∞] | 0 |
GAMS
Settings for the GAMS NLP solvers.
Name and description | Valid values | Default value |
---|---|---|
Subsolver.GAMS.NLP.OptionsFilename Options file for the NLP solver in GAMS | string | |
Subsolver.GAMS.NLP.Solver NLP solver to use in GAMS (auto: SHOT chooses) | string | auto |
Gurobi
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Gurobi.Heuristics The relative amount of time spent in MIP heuristics. | [0,1] | 0.05 |
Subsolver.Gurobi.MIPFocus MIP focus 0: Automatic 1: Feasibility 2: Optimality 3: Best bound | {0,...,3} | 0 |
Subsolver.Gurobi.NumericFocus MIP focus 0: Automatic 1: Mild 2: Moderate 3: Aggressive | {0,...,3} | 1 |
Subsolver.Gurobi.PoolSearchMode Finds extra solutions 0: No extra effort 1: Try to find solutions 2: Find n best solutions | {0,...,2} | 0 |
Subsolver.Gurobi.PoolSolutions Determines how many MIP solutions are stored | {1,...,2000000000} | 10 |
Subsolver.Gurobi.ScaleFlag Controls model scaling -1: Automatic 0: Off 1: Mild 2: Moderate 3: Aggressive | {-1,...,3} | -1 |
Ipopt
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Ipopt.ConstraintViolationTolerance Constraint violation tolerance in Ipopt | [-∞,∞] | 1e-08 |
Subsolver.Ipopt.LinearSolver Ipopt linear subsolver 0: Default 1: MA27 2: MA57 3: MA86 4: MA97 5: MUMPS | {0,...,5} | 0 |
Subsolver.Ipopt.MaxIterations Maximum number of iterations | {0,...,∞} | 1000 |
Subsolver.Ipopt.RelativeConvergenceTolerance Relative convergence tolerance | [-∞,∞] | 1e-08 |
Root search solver
Settings for the Boost rootsearch functionality.
Name and description | Valid values | Default value |
---|---|---|
Subsolver.Rootsearch.ActiveConstraintTolerance Epsilon constraint tolerance for root search | [0,∞] | 0 |
Subsolver.Rootsearch.MaxIterations Maximal root search iterations | {0,...,∞} | 100 |
Subsolver.Rootsearch.Method Root search method to use 0: TOMS748 1: Bisection | {0,1} | 0 |
Subsolver.Rootsearch.TerminationTolerance Epsilon lambda tolerance for root search | [0,∞] | 1e-16 |
SHOT primal NLP solver
Name and description | Valid values | Default value |
---|---|---|
Subsolver.SHOT.ReuseHyperplanes.Fraction The fraction of generated hyperplanes to reuse. | [0,1] | 0.1 |
Subsolver.SHOT.ReuseHyperplanes.Use Reuse valid generated hyperplanes in main dual model. | true/false | true |
Subsolver.SHOT.UseFBBT Do FBBT on NLP problem. | true/false | true |
Termination
These settings control when SHOT will terminate the solution process.
Name and description | Valid values | Default value |
---|---|---|
Termination.ConstraintTolerance Termination tolerance for nonlinear constraints | [0,∞] | 1e-08 |
Termination.DualStagnation.ConstraintTolerance Min absolute difference between max nonlinear constraint errors in subsequent iterations for termination | [0,∞] | 1e-06 |
Termination.DualStagnation.IterationLimit Max number of iterations without significant dual objective value improvement | {0,...,∞} | 2147483647 |
Termination.IterationLimit Iteration limit for main strategy | {1,...,∞} | 200000 |
Termination.ObjectiveConstraintTolerance Termination tolerance for the nonlinear objective constraint | [0,∞] | 1e-08 |
Termination.ObjectiveGap.Absolute Absolute gap termination tolerance for objective function | [0,∞] | 0.001 |
Termination.ObjectiveGap.Relative Relative gap termination tolerance for objective function | [0,∞] | 0.001 |
Termination.PrimalStagnation.IterationLimit Max number of iterations without significant primal objective value improvement | {0,...,∞} | 50 |
Termination.TimeLimit Time limit (s) for solver | [0,∞] | 1.799e+308 |
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