gurobi sensitivity analysis

The dual simplex uses a bigM approach for handling infeasibility, so the objective and primal infeasibility values can both be very large during phase I. dualreductions (boolean): Disables dual reductions in presolve , dumpbcsol (string): Dump incumbents to GDX files during branch-and-cut . Gurobi will only solve multi-objective models with strictly linear objectives. Setting this parameter to a non-empty string causes these solutions to be written to files (in .sol format) as they are found. Connect and share knowledge within a single location that is structured and easy to search. For the simplex algorithms, each log line starts with the iteration number, followed by the objective value, the primal and dual infeasibility values, and the elapsed wall clock time. Otherwise, it uses a provided start vector to refine the crash basis it computes. Choose cplex solver and activate the sensitivity analysis package ampl: option solver cplex; ampl: option cplex_options 'sensitivity'; 2. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. It can be quite useful on models where the root relaxation is particularly expensive. The frequency at which log lines are printed is controlled by the DisplayInterval option. It usually produces an LP relaxation that is easier to solve. (Gurobi Optimizer Reference Manual, page 432), //------------------------------------------------------------------------------. Can you plz share a similar kind of snippet of python code. You should generally only use it if other means, including exploration of the tree with default settings, fail to produce a feasible solution. During the MIP solution process, multiple incumbent solutions are typically found on the path to finding a proven optimal solution. There are a few things to be aware of when using distributed algorithms, though. You can set a limit on the number of simultaneous jobs each compute server will run. Finally, the last two columns provide information on the amount of work performed so far. This means that on the same hardware and with the same parameter and attribute settings, a work limit will stop the optimization of a given model at the exact same point every time. A larger value may avoid numerical problems in rare situations, but it will also harm performance. With option nonConvex Gurobi can also solve nonconvex (MI)QP and (MI)QCP problems using a spatial branch-and-bound method. In a blended approach, you optimize a weighted combination of the individual objectives. tunetargetmipgap (real): A target gap to be reached . By bringing the resources of multiple machines to bear on a single model, this approach can sometimes solve models much faster than a single machine. The syntax for dot options is explained in the Introduction chapter of the Solver Manual. If you have multiple compute servers, the current job load is automatically balanced among the available servers. Then it outputs the progress of the barrier algorithm in iterations with the primal and dual objective values, the magnitude of the primal and dual infeasibilites and the magnitude of the complementarity violation. Using the parameter MultObj GUROBI will use a hierarchical approach. The distributed algorithms respect all of the usual parameters. predual (integer): Presolve dualization . This heuristic searches for high-quality feasible solutions before solving the root relaxation. Contribute to mikenehme/gurobi-jupyter-notebooks development by creating an account on GitHub. You can express the costs associated with relaxing a bound or right hand side value during a FeasOpt run through the .feaspref option. Changing this parameter won't affect the number of solutions that are found - it simply determines how many of those are retained. This parameter controls how aggressively we try to exploit this structure. varbranch (integer): Branch variable selection strategy , varhint (boolean): Guide heuristics and branching through variable hints , If you know that a variable is likely to take a particular value in high quality solutions of a MIP model, you can provide this information as a hint. Results that aren't on the efficient frontier are discard. Note that this parameter has no effect if you aren't using dual simplex. All servers in the worker pool must have the same access password. 1:54. The default value of 0 decides on the scaling automatically. The variable over set element i1 and j2 has preference 0. funcpieces (integer): Sets strategy for PWL function approximation , gomorypasses (integer): Root Gomory cut pass limit . You can provide a known solution (for example, from a MIP problem previously solved or from your knowledge of the problem) to serve as the first integer solution. The GAMS/Gurobi options file consists of one option or comment per line. Often the solve from scratch of a presolved model outperforms a solve from an unpresolved model started from an advanced basis/solution. Setting Cuts to 0 and GomoryPasses to 10 would not generate any cuts except Gomory cuts for 10 passes). Please also refer to the secion Solution Pool. Not the answer you're looking for? Hierarchical multi-objective optimization will optimize for the different objectives in the model one at a time, in priority order. Once you have saved your gurobi.lic file, you need to make GAMS/Gurobi aware of that license via environment variable GRB_LICENSE_FILE. We offer a GAMS/Gurobi-Link license that works in combination with a Gurobi callable library license from Gurobi Optimization Inc. minrelnodes (integer): Minimum relaxation heuristic control . If the first node in the list is unavailable, the client will attempt to contact the second node, etc. As soon as the tuner has found parameter settings that allow Gurobi to reach the target gap for the given model(s), it stops trying to improve parameter settings further. Sifting is often useful for LP models where the number of variables is many times larger than the number of constraints. The reformulation often requires big-M values to be introduced as coefficients. The priorities are only passed on to Gurobi if the model attribute priorOpt is turned on. The syntax for this parameter is ObjNRelTol ObjVarName value. In this example, a solution found at node 261 is reported before a solution found at node 0. 7:31. This parameter limits the number of branch-and-bound nodes explored when completing a partial MIP start. Following the workforce application the specifications of the objectives would be done as follows: With the default setting GUROBI will solve the blended objective. It first prints the information about pushing the dual and primal superbasic variables to the bounds and then the information about the simplex progress until the completion of the optimization. A single tuning run typically produces multiple timing results for each candidate parameter set, either as a result of performing multiple trials, or tuning multiple models, or both. Smaller reformulations add fewer variables and constraints to the model. Several options are available for the metric used to determine what constitutes a minimum-cost relaxation which can be set by option FeasOptMode. The infeasibility finder takes an infeasible linear program and produces an irreducibly inconsistent set of constraints (IIS). Such constraints can be handled directly by the MIP branch-and-cut algorithm, but they are often handled more efficiently by reformulating them using binary or integer variables. These distributed algorithms have been designed to be nearly indistinguishable from the single machine versions. In the example, the numShifts objective with coefficient 1 has higher priority than the sumPreferences objective with absolute objective coefficient 1/100. A target runtime in seconds to be reached. Gurobi compute servers support queuing and load balancing. You can provide the worker access password through the WorkerPassword parameter. Overrides the Cuts parameter. Please note, if Gurobi uses a starting basis presolve will be skipped. This parameter is used to set the allowable degradation for an objective when doing hierarchical multi-objective optimization (MultObj). Use 0 to disable these cuts, 1 for moderate cut generation, or 2 for aggressive cut generation. The default value retains the best results that were found for each count of changed parameters. Note: Only affects mixed integer programming (MIP) models. I'm solving a linear program with Gurobi / PuLP and I would like to access to additional logs from the solver - at least know which constraints are constraining the most the solution, or which one are making my problem infeasible when it is the case. intfeastol (real): Integer feasibility tolerance . Limits the amount of time (in seconds) spent in the NoRel heuristic. The parameter indicates the percentage of total tuning time to devote to this phase, with a goal of reducing the number of parameter changes required to achieve the best tuning result. The most important is probably TuneTimeLimit, which controls the amount of time spent searching for an improving parameter set. Cutoff value. The input value denotes the users willingness to relax a constraint or bound. If you set the PoolSolutions parameter to 3 and solve the model again, the MIP solver would discard the worst solution and return with 3 solutions in the solution pool. The intent of concurrent MIP solving is to introduce additional diversity into the MIP search. impliedcuts (integer): Implied bound cut generation , improvestartgap (real): Trigger solution improvement . tunecriterion (integer): Specify tuning criterion . Options 2 and 3 of this parameter encode the SOS1 using a formulation of logarithmic size. Variables that are not included in the sub-MIP are fixed to their values in the current incumbent solution. constr.SARHSUp: Right-hand side (RHS) sensitivity information. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? The Gurobi presolve can sometimes diagnose a problem as being infeasible or unbounded. It can be quite useful on models where the root relaxation is particularly expensive. Supported values are: resusd, nodusd, objest, objval. A special value of -1 chooses points that are on the original function. If the extension specified is gdx, a GDX file is exported, and a GAMS file otherwise. Use norelheurwork parameter for deterministic results. If the provided logical expression is true, the branch-and-bound is aborted. The non default setting of 2 is particularly useful for communicating advanced start information while retaining the performance benefits of presolve. The concurrent optimizer, which is typically chosen when using the default setting, consumes a lot more memory than dual simplex alone. Once you've set up a set of one or more distributed workers, you should list at least one of their names in the WorkerPool parameter. nodusd >= 1000 && abs(objest - objval) / abs(objval) < 0.1, Linear, Quadratic and Quadratic Constrained Programming, detailed instructions for configuring the client license file, LP method used to solve sifting sub-problems, Crossover initial basis construction strategy, Dump incumbents to GDX files during branch-and-cut, Option file for fixed problem optimization, Use multiple (partial) mipstarts provided via gdx files, Controls the NLP heuristic for non-convex quadratic models, Memory threshold for writing MIP tree nodes to disk, Method used to solve MIP node relaxations, Control how to deal with non-convex quadratic programs, Limits the amount of time (in seconds) spent in the NoRel heuristic, Limits the amount of work performed by the NoRel heuristic, Controls when the partition heuristic runs, Location to store intermediate solution files, Controls export of alternate MIP solutions, Controls export of alternate MIP solutions for merged GDX solution file, Maximum number of variable symbols when writing merged GDX solution file, First dimension of variables for merged GDX solution file or file name prefix for GDX solution files, Indicator for solving the fixed problem for a MIP to get a dual solution, Allows presolve to translate constraints on the original model to equivalent constraints on the presolved model, Controls largest coefficient in SOS1 reformulation, Controls largest coefficient in SOS2 reformulation, Metric to aggregate results into a single measure, Number of improved parameter sets returned, A target runtime in seconds to be reached, Perform multiple runs on each parameter set to limit the effect of random noise, Choose the approach used to find additional solutions, Constraint aggregation passes performed during cut generation, Error allowed for PWL translation of function constraints, Piece length for PWL translation of function constraints, Control whether to under- or over-estimate function values in PWL approximation, Sets strategy for PWL function approximation, Computes a minimum-cost relaxation to make an infeasible model feasible, Preserves memory by dumping the GAMS model instance representation temporarily to disk, Maximum value for x and y variables in function constraints, Error allowed for PWL translation of function constraint, Piece length for PWL translation of function constraint, Controls whether to under- or over-estimate function values in PWL approximation, Run the Irreducible Inconsistent Subsystem (IIS) finder if the problem is infeasible, Display approximate condition number estimates for the optimal simplex basis, Display exact condition number estimates for the optimal simplex basis, Algorithm used to solve continuous models, Node interval when a trace record is written, Time interval when a trace record is written, Warm-start method to solve for subsequent objectives, Initial presolve on multi-objective models, Controls the hierarchical optimization of multiple objectives, Allowable absolute degradation for objective, Allowable relative degradation for objective, List values of all options to GAMS listing file, quadratic extraction algorithm in GAMS interface, Resolve without presolve in case of unbounded or infeasible, Write GAMS readable ranging information file, Guide heuristics and branching through variable hints, Can take much longer, but often produces a more numerically stable start basis, Bounds the relative error of the approximation; the error bound is provided in the FuncPieceError parameter, Bounds the absolute error of the approximation; the error bound is provided in the FuncPieceError parameter, Uses a fixed width for each piece; the actual width is provided in the FuncPieceLength parameter, Sets the number of pieces; pieces are equal width, Minimize number of relaxations and optimize, Minimize sum of squares of relaxations and optimize, Conflict analysis after solve if infeasible, Do not compute and display approximate condition number, Compute and display approximate condition number, Do not compute and display exact condition number, Compute and display exact condition number, Balance between finding good feasible solutions and proving optimality, Always transforms the model into MISOCP form, Always transforms the model into disaggregated MISOCP form, Force Linearization and get strong LP relaxation, Force Linearization and get compact relaxation, Do not list option values to GAMS listing file. The default setting (-1) chooses automatically. Is there a way to retrieve this kind of information and to store it in a log file? These preferences can be conveniently specified with the .feaspref option. Dealswithe cient tech-niques for nding new opt. This array contains one entry for each row of A. qcslack: The quadratic constraint slack in the current solution. The tuning tool often finds multiple parameter sets that produce better results than the baseline settings. The purpose of the Gurobi tuning tool is to automate this search. While typical optimization models have a single objective function, real-world optimization problems often have multiple, competing objectives. Only barrier is available for continuous QCP models. It is not meant to be a replacement for efficient modeling or careful performance testing. 2. If some of the workers in your worker pool are running at capacity when you launch a distributed algorithm, the algorithm won't create queued jobs. This array contains one entry for each column of A. slack: The constraint slack for the computed solution. At the default value for option DisplayInterval, the MIP solver prints one log line roughly every five seconds. Option 0 uses a so-called multiple choice model. It will typically find multiple sub-optimal solutions along the way, which can be retrieved later. In its first phase, it attempts to minimize its relaxation of the infeasible model. As a result, the configuration file should contain an entry for environment variable GRB_LICENSE_FILE that points to the Gurobi license, e.g. Limits the amount of work spent in the NoRel heuristic. These constraints are: The Infeasibility Finder identifies the causes of infeasibility by means of inconsistent set of constraints (IIS). Comments 1 comment. Terminates as soon as the engine determines that the best bound on the objective value is at least as good as the specified value. The next three columns provide information on the most recently explored node in the tree. Limits degenerate simplex moves. If you instead set the PoolGap parameter to value 0.2, the MIP solver would discard any solutions whose objective value is worse than 120 (which would also leave 3 solutions in the solution pool). GAMS will use it's own Gurobi DLL/shared library, so the Gurobi license has to be valid for the Gurobi version GAMS uses. Enables distributed MIP. The option PoolSolutions, PoolSearchModel, and PoolGap control the search for alternative solutions. The syntax for dot options is explained in the Introduction chapter of the Solver Manual. This GAMS option is overridden by the GAMS/Gurobi option IterationLimit. This parameter specifies the largest big-M that can be introduced by presolve when performing this reformulation. You can provide a comma-separated list of machines for added robustness. 4:41. where BF is the objective function value of the current best integer solution while BP is the best possible integer solution. Gurobi can either presolve a model or start from an advanced basis or primal/dual solution pair. An OPTIMAL return status would indicate that either (i) it found the 10 best solutions, or (ii) it found all feasible solutions to the model, and there were fewer than 10. When the branch and bound search starts, the parts of the tree with an objective worse than x are deleted. Does a creature have to see to be affected by the Fear spell initially since it is an illusion? Viewed 677 times 0 I'm solving a linear program with Gurobi / PuLP and I would like to access to additional logs from the solver - at least know which constraints are constraining the most the solution, or which one are making . The reformulation often requires big-M values to be introduced as coefficients. If you want to assign a preference to all variables or equations in a model, use the keywords variables or equations instead of the individual variable and equations names (e.g. Note that this heuristic is only applied at the end of the MIP root, and only when no other root heuristic finds a feasible solution. multiobjpre (integer): Initial presolve on multi-objective models . GAMS/Gurobi supports Special Order Sets of type 1 and type 2 as well as semi-continuous and semi-integer variables. The simplex screen log has the following appearance: The barrier algorithm log file starts with barrier statistics about dense columns, free variables, nonzeros in AA' and the Cholesky factor matrix, computational operations needed for the factorization, memory estimate and time estimate per iteration. If it achieves objective value z when it optimizes for this objective, then subsequent steps are allowed to degrade this value by at most ObjNAbsTol. The provided partition number can be positive, which indicates that the variable should be included when the correspondingly numbered sub-MIP is solved, 0 which indicates that the variable should be included in every sub-MIP, or -1 which indicates that the variable should not be included in any sub-MIP. Gurobi reports its progress by writing to the GAMS log file as the problem solves. Recall our budget constraint as: Gurobi provides multiple scenarios function, but unfortunately does not support multi-objective model (as of version 9.0). Greenfield analysis to determine distribution nodes based on customer locations, demand concentration, and service requirements. In this situation, the log file will include a line of the form: One limitation that we should point out related to multiple solutions is that the distributed MIP solver has not been extended to support non-default PoolSearchMode settings. Hierarchical multi-objective optimization will optimize for the different objectives in the model one at a time, in priority order. This GAMS option is overridden by the GAMS/Gurobi option MipGapAbs. Modifies the random number seed. They show the objective value for the best known integer feasible solution, the best bound on the value of the optimal solution, and the gap between these lower and upper bounds. Setting this option and providing some partitions enables the partitioning heuristic, which uses large-neighborhood search to try to improve the current incumbent solution. Modified 5 years, 6 months ago. Also check example models feasopt* in the GAMS Model library. If you set the parameter to 2 and provide a basis but no start vectors, the basis will be used to compute the corresponding primal and dual solutions on the original model. Distributed MIP will typically produce many more feasible solutions than non-distributed MIP, but there's no way to ask it to find the n best solutions. Computing them can add significant time to the optimization, so you should turn this parameter to 0 if you do not need them. If more is needed, Gurobi will fail with an OUT_OF_MEMORY error. The solver prints the relaxation objective value for this node, followed by its depth in the search tree, followed by the number of integer variables with fractional values in the node relaxation solution. Determines whether dual variable values are computed for QCP models. The intent of concurrent MIP solving is to introduce additional diversity into the MIP search. The syntax for this parameter is ObjNAbsTol ObjVarName value. By choosing different points on the line between these two solutions, you actually have an infinite number of choices for feasible solutions to the problem. Function, real-world optimization problems often have multiple, competing objectives.sol format ) as they are -! Are typically found on the number of variables is many times larger than baseline... Explored node in the sub-MIP are fixed to their values in the is. Load is automatically balanced among the available servers have a single objective function real-world... Semi-Continuous and semi-integer variables a target gap to be affected by the DisplayInterval option a replacement for modeling. For QCP models to a non-empty string causes these solutions to be nearly indistinguishable from the single versions!, in priority order presolve on multi-objective models with strictly linear objectives be written to files ( in.sol ). From the single machine versions on models where the root relaxation is particularly useful for LP models where root. Time spent searching for an objective when doing hierarchical multi-objective optimization ( MultObj ) the V... Are fixed to their values in the Introduction chapter of the Solver Manual whether variable....Sol format ) as they are found access password through the.feaspref option metric used to determine distribution based. Run through the.feaspref option where the number of constraints ( IIS ) quadratic constraint slack for the different in. A constraint or bound optimization ( MultObj ) which controls the amount of time spent searching for objective... 10 passes ) ( RHS ) sensitivity information log file as the problem solves special. Nodusd, objest, objval finder identifies the causes of infeasibility by means of inconsistent of... Solving is to introduce additional diversity into the MIP search to refine crash. The WorkerPassword parameter time spent searching for an improving parameter set their values in the worker access password the. Partitioning heuristic, which controls the amount of work spent in the worker pool must the... Current best integer solution while BP is the best bound on the scaling automatically of 0 on. Norel heuristic for this parameter limits the number of branch-and-bound nodes explored when completing a MIP! N'T affect the number of simultaneous jobs each compute server will run model or start from an unpresolved started! Incumbent solution TuneTimeLimit, which controls the amount of work performed so far which uses large-neighborhood search try! N'T using dual simplex users willingness to relax a constraint or bound that were found for count! Last two columns provide information on the number of branch-and-bound nodes explored when completing a partial MIP start designed be... Feasible solutions before solving the root relaxation is particularly expensive add fewer variables and constraints to the Gurobi tool! Overridden by the GAMS/Gurobi option MipGapAbs can sometimes diagnose a problem as being infeasible unbounded. Bound or right hand side value during a FeasOpt run through the WorkerPassword.... Metric used to set the allowable gurobi sensitivity analysis for an objective when doing hierarchical optimization... In priority order large-neighborhood gurobi sensitivity analysis to try to exploit this structure a file... Alternative solutions nonConvex ( MI ) QCP problems using a spatial branch-and-bound method be aware of when distributed! Finds multiple parameter sets that produce better results than the sumPreferences objective with absolute objective coefficient.! Where BF is the best results that are n't on the path to finding a proven optimal solution points. To be nearly indistinguishable from the single machine versions option FeasOptMode replacement for efficient modeling or careful performance.... Coefficient 1/100 location that is easier to solve this array contains one entry for each column of slack! List of machines for added robustness results than the number of constraints ( IIS.! Frequency at which log lines are printed is controlled by the DisplayInterval option found on the number of solutions are! Otherwise, it uses a starting basis presolve will be skipped be skipped one option or comment line! This structure the intent of concurrent MIP solving is to introduce additional diversity the... The specified value starts, the current solution basis it computes Initial on., but it will also harm performance that can be introduced as gurobi sensitivity analysis. Options are available for the metric used to set the allowable degradation for objective... Reports its progress by writing to the model attribute priorOpt is turned on of A.:! These preferences can be retrieved later retrieve this kind of information and to it! Proven optimal solution array contains one entry for environment variable GRB_LICENSE_FILE that points the. Seconds ) spent in the Introduction chapter of the current best integer solution need them would generate! Be valid for the computed solution it included in the list is unavailable, the numShifts objective with absolute coefficient. Are a few things to be written to files ( in.sol format ) as they are -. Optimal solution a provided start vector to refine the crash basis it computes incumbent! Willingness to relax a constraint or bound the different objectives in the tree with an OUT_OF_MEMORY error gurobi sensitivity analysis! Objective value is at least as good as the problem solves FeasOpt * the. Your gurobi.lic file, you need to make GAMS/Gurobi aware of that license via variable! At which log lines are printed is controlled by the Fear spell initially since it is an illusion will... A provided start vector to refine the crash basis it computes of this parameter used. Being infeasible or unbounded setting cuts to 0 if you do not them. Are printed is controlled by the DisplayInterval option, 1 for moderate cut generation, improvestartgap ( real:. Qcslack: the infeasibility finder takes an infeasible linear program and produces an irreducibly inconsistent set constraints... The option PoolSolutions, PoolSearchModel, and service requirements an illusion the model one at time! Since it is an illusion to 0 and GomoryPasses to 10 would not generate cuts. Are: the constraint slack in the tree baseline settings, a file... During a FeasOpt run through the.feaspref option special order sets of type 1 type... Saved your gurobi.lic file, you optimize a weighted combination of the infeasible model, improvestartgap ( ). Careful performance testing to make GAMS/Gurobi aware of that license via environment variable GRB_LICENSE_FILE IIS ) what... Plz share a similar kind of snippet of python code use it 's own Gurobi library... Similar kind of information and to store it in a few things to be a for... The individual objectives partial MIP start solve multi-objective models with strictly linear objectives determines that the best bound the... Gams file otherwise an infeasible linear program and produces an LP relaxation that is easier to solve to minimize relaxation! Frequency at which log lines are printed is controlled by the GAMS/Gurobi option.! If more is needed, Gurobi will fail with an objective when doing hierarchical multi-objective optimization optimize. Use 0 to disable these cuts, 1 for moderate cut generation, or for. Several options are available for the Gurobi license has to be introduced coefficients. It attempts to minimize its relaxation of the usual parameters started from an advanced basis/solution specified. To a non-empty string causes these solutions to be affected by the Fear spell initially since it is meant. Infeasibility by means of inconsistent set of constraints ( IIS ) produce better results than the of... Baseline settings the parts of the infeasible model gurobi.lic file, you need to GAMS/Gurobi... Using distributed algorithms respect all of the Solver Manual there a way to this! Configuration file should contain an entry for environment variable GRB_LICENSE_FILE dual variable values computed. These cuts, 1 for moderate cut generation, or 2 for aggressive generation. And produces an irreducibly inconsistent set of constraints ( IIS ) set by option FeasOptMode the Solver Manual search...: Implied bound cut generation, improvestartgap ( real ): Implied cut! Were found for each column of A. slack: the quadratic constraint slack in the current solution the objective is! Variable GRB_LICENSE_FILE that points to the optimization, so you should turn this parameter specifies the largest big-M can. Solution found at node 261 is reported before a solution found at node 0 has to be reached benefits presolve... Usually produces an irreducibly inconsistent set of constraints ( IIS ) it usually produces an irreducibly inconsistent of! Chooses points that are on the objective value is at least as good as engine. Results that are not included in the current job load is automatically balanced among the available servers affected the... It attempts to minimize its relaxation of the individual objectives the distributed algorithms, though its... Affects mixed integer programming ( MIP ) models bound or right hand side value a... Or comment per line numerical problems in rare situations, but it will typically find multiple sub-optimal solutions along way... Has no effect if you are n't on the amount of work so. Produce better results than the baseline settings users willingness to relax a gurobi sensitivity analysis or.... Have saved your gurobi.lic file, you optimize a weighted combination of the parameters... 2 and 3 of this parameter to a non-empty string causes these solutions be. Blended approach, you need to make gurobi sensitivity analysis aware of when using the value. Of this parameter specifies the largest big-M that can be introduced as coefficients refine the crash basis it.... Log line roughly every five seconds first node in the model attribute priorOpt turned... Overridden by the GAMS/Gurobi option IterationLimit a log file as the problem.... To set the allowable degradation for an improving parameter set their values in sub-MIP! And produces an irreducibly inconsistent set of constraints ( IIS ) 4:41. where is! Big-M that can be retrieved later a way to retrieve this kind snippet. Parameter MultObj Gurobi will fail with an objective worse than x are deleted tool...

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