Reference · 2.0 development

Solver settings

Generated from the SHOTpy build used for this documentation. Backend availability and defaults can depend on build configuration.

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 2: Only external (through callback)	{0,…,2}	0
Dual.TreeStrategy The main strategy to use 0: Multi-tree 1: Single-tree	{0,1}	1

Convex Bounding

These settings control the convex bounding strategy that solves a MIP problem with all hyperplane cuts generated for convex constraints so far and ignoring those generated for nonconvex constraints. This will give a dual bound for the nonconvex problem.

Name and description	Valid values	Default value
Dual.ConvexBounding.IdleIterations How often the convex bounding strategy should be executed. 0 = Every iteration with generated hyperplanes for nonconvexities, 1 = Every second iteration, etc.	{0,…,∞}	10
Dual.ConvexBounding.Use Enable the convex bounding strategy for nonconvex problems	true/false	true

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.Strategy Strategy for finding ESH interior points 0: Use internal strategy 1: Only external (through callback)	{0,1}	0
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.NonfinitePointRetreatFactor The smallest fraction of the distance to a point where the constraint is finite that the point a hyperplane is generated in is moved, when the constraint is not finite in it	[1e-12,0.5]	0.001
Dual.HyperplaneCuts.ObjectiveRootSearch When to use the objective root search 0: Always 1: IfConvex 2: Never	{0,…,2}	1
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.CutResetLimit Max number of times the cuts of nonconvex constraints are removed without primal objective value improvement when the infeasibility repair loops	{0,…,∞}	3
Dual.MIP.InfeasibilityRepair.IntegerCuts Allow feasibility repair of integer cuts	true/false	false
Dual.MIP.InfeasibilityRepair.IterationLimit Max number of infeasible problems repaired without primal objective value improvement	{0,…,∞}	100
Dual.MIP.InfeasibilityRepair.RelaxCutsViolatedByPrimal Relax the cuts of nonconvex constraints that cut off a known primal solution	true/false	true
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.RandomSeed Random seed for the pseudorandom choices in the MIP solver: 0: The default of the solver used	{0,…,∞}	0
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 3: Highs	{0,…,3}	3
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 convex .

Name and description	Valid values	Default value
Dual.ReductionCut.MaxIterations Max number of primal cut reduction without primal improvement	{0,…,∞}	20
Dual.ReductionCut.ReductionFactor The factor used to reduce the cutoff value	[0,1]	0.001
Dual.ReductionCut.Strategy The reduction cut strategy to use 0: Fraction 1: GoldenRatio	{0,1}	0
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 Stop solving relaxed problems when the maximum constraint deviation is at most this value	[-∞,∞]	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

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.DirectionalSolves Maximum number of additional solves minimizing or maximizing a variable in a convex constraint without cuts	{0,…,∞}	10
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 all the problems solved for the 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.AbsoluteValue.MaximumBigM The largest big-M used to bound an absolute value from above with a binary variable; above it, the absolute value is only bounded from below	[0,∞]	100000000
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.EpigraphStrategy How to choose between objective-function and epigraph-constraint form for the objective. ‘Unchanged’ performs no epigraph or anti-epigraph reformulation, keeping the objective function exactly as given; ‘Objective function’ always keeps (or folds back to) a direct objective function; ‘Epigraph constraint’ always introduces an epigraph auxiliary-variable constraint for nonlinear or quadratic objectives 0: Unchanged 1: Objective function 2: Epigraph constraint	{0,…,2}	0
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.Decomposition.Formulation Placement of the original term cofficient when decomposing a quadratic term 0: Coefficient is included in reformulation 1: Coefficient remains	{0,1}	0
Model.Reformulation.Quadratics.Decomposition.LDLMaximumFill Skip the LDL decomposition if L would have more than this times the nonzeros of the quadratic matrix, and over a million (0 = no limit)	[0,∞]	100
Model.Reformulation.Quadratics.Decomposition.Method Whether to use the eigenvalue decomposition of convex quadratic functions 0: No decomposition 1: Eigenvalue decomposition 2: LDL decomposition	{0,…,2}	2
Model.Reformulation.Quadratics.Decomposition.Tolerance Terms with corresponding eigenvalues or diagonal elements in D-matrix smaller than this value will be ignored	[0,∞]	1e-07
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

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.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
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

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.Enumeration.MaxCombinations Max number of combinations of the discrete variables for the exhaustive search to be performed	{1,…,∞}	50
Primal.FixedInteger.Enumeration.TimeLimit Time limit (s) for all NLP problems in the exhaustive search	[0,∞]	20
Primal.FixedInteger.Enumeration.UseAsFallback Solve NLP problems for all combinations of the discrete variables if the objective gap could not be closed	true/false	true
Primal.FixedInteger.Enumeration.UseInitially Solve NLP problems for all combinations of the discrete variables before the dual strategy	true/false	false
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 3: Uno	{0,…,3}	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}	1
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.UseOnContinuousProblems Also solve the NLP problem from the dual solutions of a nonconvex problem without discrete variables	true/false	true
Primal.FixedInteger.UseStartingPoint Solve an NLP problem from a given starting point, e.g. the levels of the variables in GAMS or the initial values in AMPL, before the first dual problem. Off by default until benchmarked on all of MINLPLib	true/false	false
Primal.FixedInteger.Warmstart Warm start the NLP solver	true/false	true
Primal.PolishSolution.NumberOfPoints The number of solutions of the dual problem, from the last iterations, that a final NLP problem is solved from to try to improve the solution. More than one can find a better local solution of a nonconvex problem, and zero disables it	{0,…,∞}	1

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 solution tolerances

These settings sets various tolerances for accepting primal solutions.

Name and description	Valid values	Default value
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.

Highs

Name and description	Valid values	Default value
Subsolver.Highs.DebugLevel Debug level for HiGHS internal assertions 0: off 1: low 2: medium 3: high	{0,…,3}	0
Subsolver.Highs.MIPAllowRestart Whether MIP restart is permitted	true/false	true
Subsolver.Highs.MIPDetectSymmetry Whether MIP symmetry should be detected	true/false	true
Subsolver.Highs.MIPHeuristicEffort Effort spent for MIP heuristics	[0,1]	0.05
Subsolver.Highs.MIPHeuristicRunShifting Run Shifting heuristic	true/false	false
Subsolver.Highs.MIPHeuristicRunZiRound Run Zi Round heuristic	true/false	false
Subsolver.Highs.Parallel Use parallelization 0: off 1: choose 2: on	{0,…,2}	1
Subsolver.Highs.Presolve Use presolve 0: off 1: choose 2: on	{0,…,2}	1
Subsolver.Highs.RunCrossover Run crossover 0: off 1: choose 2: on	{0,…,2}	0

Ipopt

Name and description	Valid values	Default value
Subsolver.Ipopt.ConstraintViolationTolerance Constraint violation tolerance in Ipopt	[-∞,∞]	1e-08
Subsolver.Ipopt.HessianApproximation The Hessian of the Lagrangian used by Ipopt. Automatic uses a limited-memory approximation if the exact Hessian of a nonlinear expression with at least 64 variables is nearly dense, since it is then expensive 0: Exact 1: Limited memory 2: Automatic	{0,…,2}	2
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.OptionsFile An Ipopt options file with any Ipopt options, read instead of ipopt.opt in the working directory. Options set by SHOT, e.g. the linear solver, are not changed by it	string
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.IterationLimit Iteration limit for main strategy	{1,…,∞}	200000
Termination.ObjectiveConstraintTolerance Termination tolerance for the nonlinear objective constraint	[0,∞]	1e-08
Termination.TimeLimit Time limit (s) for solver	[0,∞]	1.7976931348623157e+308
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,…,∞}	1000
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