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bariterlimit (integer): Barrier iteration limit , barorder (integer): Barrier ordering algorithm , barqcpconvtol (real): Barrier QCP convergence tolerance . Enables or disables quad precision computation in simplex. solnpool (string): Controls export of alternate MIP solutions . Does it make sense to say that if someone was hired for an academic position, that means they were the "best"? Sensitivity analysis is activated by the option Sensitivity. funcmaxval (real): Maximum value for x and y variables in function constraints . Concurrent optimizers run multiple solvers on multiple threads simultaneously, and choose the one that finishes first. 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). I'm currently working on using Gurobi Python API to solve a large-scale LP. Thanks for contributing an answer to Stack Overflow! By default, the simplex algorithms print a log line roughly every five seconds, although log lines can be delayed when solving models with particularly expensive iterations. You only need a GAMS/Gurobi link license when you solve your problems in the Gurobi Instant Cloud. 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 . Algorithm used for MIP node relaxations. Following the workforce application the specifications of the objectives would be done as follows: With the default setting GUROBI will solve the blended objective. 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. Specifically, if x or y participate in a function constraint, any bound larger than funcMaxVal (in absolute value) will be truncated. At this point, the distributed strategy transitions from a concurrent approach to a distributed approach. You can provide either machine names or IP addresses, and they should be comma-separated. Use the WorkerPool parameter to provide the list of available machines. Once you have saved your gurobi.lic file, you need to make GAMS/Gurobi aware of that license via environment variable GRB_LICENSE_FILE. The MIP solver will append _n.sol to the value of the parameter to form the name of the file that contains solution number n. For example, setting the parameter to value solutions/mymodel will create files mymodel_0.sol, mymodel_1.sol, etc., in directory solutions. Scheduling problems arise in a wide range of applications, and solving large-scale problems efficiently can require expert knowledge and insights. For example, if you have 6 threads available and you set ConcurrentMIP to 2, the concurrent MIP solver will allocate 3 threads to each independent solve. However, you may want to go beyond diagnosis to perform automatic correction of your model and then proceed with delivering a solution. Controls the automatic reformulation of SOS1 constraints. That is, it attempts to find a feasible solution that requires minimal change. The MIP engine will terminate (with an optimal result) when the gap between the lower and upper objective bound is less than MipGap times the upper bound. writeparams (string): Write Gurobi parameter file , writeprob (string): Save the problem instance , zerohalfcuts (integer): Zero-half cut generation , zeroobjnodes (integer): Zero objective heuristic control . Note: Only affects non-convex quadratic models. funcpieces (integer): Sets strategy for PWL function approximation , gomorypasses (integer): Root Gomory cut pass limit . This option works in combination with the Partition number for variables. More precisely, given a constraint y =l= Mb, where y is a non-negative continuous variable, b is a binary variable, and M is a constant that captures the largest possible value of y, the constraint is intended to enforce the relationship that y must be zero if b is zero. It marks the relaxed right hand side values and bounds in the solution listing. If the M value is large, then the M b upper bound on the y variable can be substantial. If you follow through the steps on the Gurobi web site, you eventually get the names of the machines Gurobi has started for you in the cloud. Sensitivity Analysis AlsocalledPost-optimality analysis. Another limitation of automated tuning is that performance on a model can experience significant variations due to random effects (particularly for MIP models). In its second phase, it finds an optimal solution (using the original objective) among those that require only as much relaxation as it found necessary in the first phase. The priorities are only passed on to Gurobi if the model attribute priorOpt is turned on. This parameter specifies the largest big-M that can be introduced by presolve when performing this reformulation. The syntax for dot options is explained in the Introduction chapter of the Solver Manual. tunecriterion (integer): Specify tuning criterion . It comes free of charge with any GAMS system. The environmentVariables section in the GAMS configuration file (available as of GAMS version 31.1.0) is a convenient way to set the GRB_LICENSE_FILE environment variable. 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. Manually raising (throwing) an exception in Python. optimalitytol (real): Dual feasibility tolerance . Gurobi can detect that continuous variables are implied discrete variables and can utilize priorities. 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. 1:54. With Feasopt turned on, a minimum-cost relaxation of the right hand side values of constraints or bounds on variables is computed in order to make an infeasible model feasible. 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. It will cause Gurobi to use quick-start steepest edge pricing and will use the primal simplex algorithm. A non-negative value indicates the maximum number of constraint aggregation passes performed during cut generation. Connect and share knowledge within a single location that is structured and easy to search. Note that this is not given as an increase to the current RHS. 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. Setting the parameter to 0 turns it off, and setting it to 1 forces it on. If VarHint option is active, GAMS/Gurobi will pass variable levels rounded to the nearest integer as hints to Gurobi if their level is within TryInt of an integer. The main differences are in the progress section. Choose a value of 3 to use the best objective bound. Thus, for example, to enable the heuristic at the beginning and end of the root cut loop (and nowhere else), you would set the 8 bit and the 4 bit to 1, which would correspond to a parameter value of 12. Several options are available for the metric used to determine what constitutes a minimum-cost relaxation which can be set by option FeasOptMode. There are several diffent ways to perform this reformulation; they differ in their size and strength. They are essential to the model, and the solver is forced to apply them whenever a solution would otherwise not satisfy them. Sets a limit on the amount of diagonal perturbation that the optimizer is allowed to automatically perform on the Q matrix in order to correct minor PSD violations. Setting the parameter to 2 causes the MIP to do a systematic search for the n best solutions. Modifies the random number seed. Please find attached an example model. Modified 5 years, 6 months ago. This behavior can be relaxed through a pair of attributes: ObjNRelTol and ObjNAbsTol. Note that the reformulation of SOS1 constraints is also influenced by the PreSOS1BigM parameter. Overrides the Cuts parameter. By default, Gurobi chooses the parameter settings used for each independent solve automatically. Setting this option and providing some partitions enables the partitioning heuristic, which uses large-neighborhood search to try to improve the current incumbent solution. Non-negative values are node limits. While this may appear equivalent to asking for 10 solutions and simply ignoring those with objective worse than 110, the solve will typically complete significantly faster with this parameter set, since the solver does not have to expend effort looking for solutions beyond the requested gap. This array contains one entry for each row of A." The option PoolSolutions, PoolSearchModel, and PoolGap control the search for alternative solutions. They both only apply when all the variables in the SOS1 are non-negative. The following line will print the name, dual value and slack for each constraint to the screen: If you want to query for attributes programmatically, please see. RHS value and the objective value (profit) are appended to two lists for further analysis. It will typically find multiple sub-optimal solutions along the way, which can be retrieved later. This default behavior can be modified by assigning relaxation preferences to variable bounds and constraints. projimpliedcuts (integer): Projected implied bound cut generation , psdtol (real): Positive semi-definite tolerance . Determines how many MIP solutions are stored. A MIP start GDX file can be created, for example, by using the command line option savepoint. Option 1 uses an incremental model. Once you've set up the worker pool through the appropriate parameters, the last step to use a distributed algorithm is to set the TuneJobs, ConcurrentJobs, or DistributedMIPJobs parameter. With a value of 3, lazy constraints that cut off the relaxation solution are also pulled in. Your GAMS system directory contains the grbgetkey program, so to make sure it is found you should open the terminal via GAMS Studio or add your GAMS system directory to the path variable. Another important approximation comes because you cannot It is a bit slower than the default algorithm. Option 2 focuses on a formulation whose LP relaxation is easier to solve, while option 3 has better branching behaviour. With the default setting (PoolSearchMode=0), the MIP solver tries to find an optimal solution to the model. You can provide the worker access password through the WorkerPassword parameter. Products The default value of -1 chooses a reformulation for each SOS1 constraint automatically. Note that this parameter has no effect if you aren't using dual simplex. You may need to experiment to find a good setting for your model. GAMS needs to identify the various objective functions, therefore the objective variables can only appear in the blended objective functions and in the particular objective defining equation. Controls the presolve dependent row reduction, which eliminates linearly dependent constraints from the constraint matrix. The rerun without presolve is controlled by the option ReRun. The Cuts parameter provides global cut control, affecting the generation of all cuts. Specifically, you may have two solutions that take identical values on the integer variables but where some continuous variables differ. Note that optimization may not stop immediately upon hitting the work limit. 2. tunejobs (integer): Enables distributed tuning . These start vectors are fed to the crossover procedure. Note that if memory is tight on an LP model, you should consider using the dual simplex method (Method=1). The state-of-the-art solver for linear programming (LP), quadratic and quadratically constrained programming (QP and QCP), and mixed-integer programming (MILP, MIQP, and MIQCP). The main challenge you face when working with multiple, competing objectives is deciding how to manage the tradeoffs between them. SOS1 constraints are often handled more efficiently using a binary representation. presos2encoding (integer): Controls SOS2 reformulation . Only the simplex and barrier algorithms are available for continuous QP models. The default -1 value chooses automatically. Options 0 and 1 of this parameter encode an SOS2 constraint using a formulation whose size is linear in the the number of SOS members. To shut off the reformulation entirely you should set that parameter to 0. presparsify (integer): Presolve sparsify reduction . Options 1 and 2 attempt to linearize quadratic constraints or a quadratic objective, potentially transforming an MIQP or MIQCP model into an MILP. It's asking sort of the flip. In order to provide the maximum amount of memory to the solver this option dumps the internal representation of the model instance temporarily to disk and frees memory. Optimization terminates when the first solve completes. Note that if your machines have similarly performing cores but different numbers of cores, we suggest that you use the Threads parameter to make sure that all machines use the same number of cores. With setting 0, an error is reported if the original user model contains non-convex quadratic constructs. 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. Only primal and dual simplex are available for solving the root of an MIQP model. This parameter limits the bounds on the variables that participate in function constraints. concurrentjobs (integer): Enables distributed concurrent solver . The default value is chosen automatically, depending on problem characteristics. When using a distributed algorithm (the distributed concurrent MIP solver or distributed tuning), this parameter allows you to specify the password for the workers listed in the WorkerPool parameter. If you want to avoid this source of potential confusion, you should set the optimality gap to 0 when using PoolSearchMode=2. Is there a way to retrieve this kind of information and to store it in a log file? Smaller reformulations add fewer variables and constraints to the model. Note that barrier is not an option for MIQP node relaxations. Normally the GAMS log file is directed to the computer screen. The default setting (-1) chooses automatically. Does Python have a string 'contains' substring method? Values of the parameter FeasOptMode indicate two aspects: (1) whether to stop in phase one or continue to phase two and (2) how to measure the minimality of the relaxation (as a sum of required relaxations; as the number of constraints and bounds required to be relaxed; as a sum of the squares of required relaxations). All constrains must be satisfied to a tolerance of FeasibilityTol. When this happens, GAMS/Gurobi can, in order to get better diagnostic information, rerun the problem with presolve turned off. Larger values increase the chances that an SOS2 constraint will be reformulated, but very large values (e.g., 1e8) can lead to numerical issues. Similar to SolnPool this option stores multiple alternative solutions to a MIP problem, but in a single GDX file. The default value of -1 chooses automatically. The best-known example is probably a trickle flow, where a continuous variable that is meant to be zero when an associated binary variable is zero instead takes a non-trivial value. The first and simplest is PoolSolutions, which controls the size of the solution pool. Limits the total amount of memory (in GB, i.e., \(10^9\) bytes) available to Gurobi. I found it but did not understand how to use: "objval: The objective value of the computed solution. Hints will affect the heuristics that Gurobi uses to find feasible solutions, and the branching decisions that Gurobi makes to explore the MIP search tree. With a value of 2, simplex will use the crushed start vector on the presolved model to refine the crash basis. Instead, we can write a Python function to change RHS values. The input value denotes the users willingness to relax a constraint or bound. Thank you! lpwarmstart (integer): Warm start usage in simplex . These moves are performed to improve the integrality of the current relaxation solution. How do I check whether a file exists without exceptions? Sets the strategy for handling non-convex quadratic objectives or non-convex quadratic constraints. You should generally only use it if other means, including exploration of the tree with default settings, fail to produce a feasible solution. 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. Use 0 to disable these cuts, 1 for moderate cut generation, or 2 for aggressive cut generation. tunetrials (integer): Perform multiple runs on each parameter set to limit the effect of random noise . 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. Gurobi Optimizer Questions; Can't view the form? When Gurobi's Method parameter requests the barrier solver, primal and dual start vectors are prioritized over basis statuses (but only if you provide both). This pragmatic choice can produce a bit of confusion when finding multiple optimal solutions. The user may specify a preference value less than or equal to 0 (zero), which denotes that the corresponding constraint or bound must not be relaxed. Optimization terminates when the first solve completes. The most important is probably TuneTimeLimit, which controls the amount of time spent searching for an improving parameter set. Instead, it will launch as many jobs as it can (up to the requested value), and it will run with these jobs. Instead, the tuner switches into the cleanup phase (see TuneCleanup parameter). I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? Distributed MIP tries to create a single, unified view of node numbers, but with multiple machines processing nodes independently, possibly at different rates, some inconsistencies are inevitable. After running the grbgetkey command, you will be asked where you would like to save your Gurobi license. Option 1 focuses on getting a strong LP relaxation. In the current release, the default Automatic (-1) setting will typically choose non-deterministic concurrent (Method=3) for an LP, barrier (Method=2) for a QP or QCP, and dual (Method=1) for the MIP root node. normadjust (integer): Simplex pricing norm . strongcgcuts (integer): Strong-CG cut generation . Enables distributed parallel tuning, which can significantly increase the performance of the tuning tool. Asking for help, clarification, or responding to other answers. If you have a general sense of the likely values for variables, you should provide them through variable hints. When relaxing a constraint in a feasibility relaxation, it is sometimes necessary to introduce a big-M value. It can be quite useful on models where the root relaxation is particularly expensive. In contrast to the TimeLimit, work limits are deterministic. Specifically, if you ask for the n best solutions, the optimality gap plays a similar role as it does in the default case, but the implications may be a bit harder to understand. In fact, it is conventional to use only the values 0 (zero) and 1 (one) except when your knowledge of the problem suggests assigning explicit preferences. The Gurobi algorithms often have to choose from among multiple, equally appealing alternatives. Then each binary variable is set % to 1-X, where X is its value in the optimal solution, and % the impact on the objective function value is reported. Values of the parameter FeasOptMode indicate two aspects: (1) whether to stop in phase one or continue to phase two and (2) how to measure the relaxation (as a sum of required relaxations; as the number of constraints and bounds required to be relaxed; as a sum of the squares of required relaxations). The integralityFocus parameter provides a better alternative. The default value of 0 indicates an automatic choice that depends on model characteristics. The MIP solver can perform a solution improvement heuristic using user-provided partition information. The NumericFocus parameter controls the degree to which the code attempts to detect and manage numerical issues. nodefilestart (real): Memory threshold for writing MIP tree nodes to disk . However, if you are looking for the n best solutions, you have to prove that the model has no solution better than the n-th best. tunetargetmipgap (real): A target gap to be reached . Note that preferences are assigned in a procedural fashion so that preferences assigned later overwrite previous preferences. Sensitivity analysis is activated by the option Sensitivity. One approach for doing so is to build your model with explicit slack variables and other modeling constructs, so that an infeasible outcome is never a possibility. This parameter controls how many of these sets should be retained when tuning is complete. Each of these models is sent to a distributed worker for processing. This parameter allows you to specify the node count at which the MIP solver switches to a solution improvement strategy. The GAMS definition is: \begin{equation*} |BP - BF| < |BP|*\mbox{OptCR} \end{equation*}. Management aper: ssential Ingredients for athematical Optimization uccess4 Setting this parameter to 1 requests that the solver work harder to try to avoid solutions that exploit integrality tolerances. When optimizing for one objective, you only consider solutions that would not degrade the objective values of higher-priority objectives. A value of -3 shuts off MIP start processing entirely. The header for the standard MIP logging looks like this: By contrast, the distributed MIP header looks like this: You'll note that columns three through five show different information. The GAMS/Gurobi option UseBasis can be used to ignore or force a basis/solution passed on by GAMS (it overrides BRatio). Option 1 uses an incremental model. Gurobi also includes node counts from one of the independent solves, as well as elapsed times, to give some indication of forward progress. This option only works with SolveLink=0 and only for models without quadratic constraints. Controls infeasibility proof cut generation. branchdir (integer): Branch direction preference . This GAMS option is overridden by the GAMS/Gurobi option NodeLimit. Otherwise, the line will be interpreted as an option name and value separated by any amount of white space (blanks or tabs). The OptCR option asks Gurobi to stop when, \begin{equation*} |BP - BF| < |BF|*\mbox{OptCR} \end{equation*}. The parameter PartitionPlace controls the use of the heursitic. There are several diffent ways to perform this reformulation; they differ in their size and strength. It will only rarely choose to do so. predual (integer): Presolve dualization . The default setting (-1) chooses the aggregation automatically; setting 0 computes the average of all individual results; setting 1 takes the maximum. If you set LPWarmStart to 2, crossover will be invoked on the presolved model using crushed start vectors. Instructs GAMS/Gurobi to read the option file. constr.pi: Dual value (also known as the shadow price ). This GAMS option is overridden by the GAMS/Gurobi option TimeLimit. Making statements based on opinion; back them up with references or personal experience. intfeastol (real): Integer feasibility tolerance . Supported opertators are: +, -, *, /, ^, %, !=, ==, <, <=, >, >=, !, &&, ||, (, ), abs, ceil, exp, floor, log, log10, pow, sqrt. The solver and model status returned to GAMS will be NORMAL COMPLETION and NO SOLUTION. For distributed MIP, you can adjust strategies, adjust tolerances, set limits, etc. These parameters are used to indicate how many distinct tuning, concurrent, or distributed MIP jobs should be started on the available workers. The Gurobi Optimizer provides a wide variety of parameters that allow you to control the operation of the optimization engines. Finally, a bare-bone interface to the LP and MIP solver of Gurobi is available under the name OSIGUROBI. precrush (boolean): Allows presolve to translate constraints on the original model to equivalent constraints on the presolved model . Controls lift-and-project cut generation. A sequence of file names specifying existing problem files may follow the option file name. Click Data - What if Analysis - Data Tables Data Table Dialog Box Opens Up. Gurobi Python API: model.addVars () too slow. constr.RHS: Right-hand side value. bestobjstop (real): Best objective value to stop . The next three columns provide information on the most recently explored node in the tree. With option nonConvex Gurobi can also solve nonconvex (MI)QP and (MI)QCP problems using a spatial branch-and-bound method. This option allows more control over how the branch-and-cut tree is explored. These distributed algorithms have been designed to be nearly indistinguishable from the single machine versions. Optimization will terminate if the engine determines that the optimal objective value for the model is worse than the specified cutoff. The syntax for dot options is explained in the Introduction chapter of the Solver Manual. An asterisk (*) at the beginning of a line causes the entire line to be ignored. rev2022.11.3.43005. The file name gets completed by the solution number. Gurobi Optimizer implements a number of distributed algorithms that allow you to use multiple machines to solve a problem faster.



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