Gurobi is the most powerful and fastest solver that the prioritizr R package can use to solve conservation planning problems. This vignette will walk you through the process of setting up Gurobi on your computer so that you can use it to solve conservation planning problems. If you encounter any problems while following the instructions below, check out the official Gurobi documentation. Gurobi is a commercial computer program. This means that users will need to obtain a license for Gurobi before they can use it. Although academics can obtain a special license at no cost, individuals that are not affiliated with a recognized educational institution may need to purchase a license to use Gurobi.

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The Gurobi suite of optimization products include state-of-the-art simplex and parallel barrier solvers for linear programming LP and quadratic programming QP , parallel barrier solver for quadratically constrained programming QCP , as well as parallel mixed-integer linear programming MILP , mixed-integer quadratic programming MIQP and mixed-integer quadratically constrained programming MIQCP solvers. The Gurobi MIP solver includes shared memory parallelism, capable of simultaneously exploiting any number of processors and cores per processor.

The implementation is deterministic: two separate runs on the same model will produce identical solution paths. While numerous solving options are available, Gurobi automatically calculates and sets most options at the best values for specific problems. Starting with GAMS distribution For example, the following message is sent to the log when attempting to solve a model that requires a license:.

Please consult our support wiki for details. It comes free of charge with any GAMS system. The above statement should appear before the solve statement. If Gurobi was specified as the default solver during GAMS installation, the above statement is not necessary. Gurobi can solve LP and convex QP problems using several alternative algorithms, while the only choice for solving convex QCP is the parallel barrier algorithm.

The majority of LP problems solve best using Gurobi's state-of-the-art dual simplex algorithm, while most convex QP problems solve best using the parallel barrier algorithm. Certain types of LP problems benefit from using the parallel barrier or the primal simplex algorithms, while for some types of QP, the dual or primal simplex algorithm can be a better choice.

If you are solving LP problems on a multi-core system, you should also consider using the concurrent optimizer. It runs different optimization algorithms on different cores, and returns when the first one finishes. The infeasibility finder takes an infeasible linear program and produces an irreducibly inconsistent set of constraints IIS. An IIS is a set of constraints and variable bounds which is infeasible but becomes feasible if any one member of the set is dropped.

The infeasibility finder is activated by the option IIS. See section Feasible Relaxation for details. In particular, objective ranging and constraint ranging give information about how much an objective coefficient or a right-hand-side and variable bounds can change without changing the optimal basis. In other words, they give information about how sensitive the optimal basis is to a change in the objective function or the bounds and right-hand side.

Sensitivity analysis is activated by the option Sensitivity. The Gurobi presolve can sometimes diagnose a problem as being infeasible or unbounded. The rerun without presolve is controlled by the option ReRun. In default mode only problems that are small i. In case of multiple solves in a row and slow performance of the second and subsequent solves, the user is advised to set the GAMS BRatio option to 1.

The methods used to solve pure integer and mixed integer programming problems require dramatically more mathematical computation than those for similarly sized pure linear or quadratic programs. Many relatively small integer programming models take enormous amounts of time to solve. Because a single mixed integer problem generates many subproblems, even small mixed integer problems can be very compute intensive and require significant amounts of physical memory.

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. If this process succeeds, the solution will be treated as an integer solution of the current problem.

The Infeasibility Finder identifies the causes of infeasibility by means of inconsistent set of constraints IIS. However, you may want to go beyond diagnosis to perform automatic correction of your model and then proceed with delivering a solution. 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. In essence, the feasible relaxation tries to suggest the least change that would achieve feasibility.

By default all equations are candidates for relaxation and weighted equally but none of the variables can be relaxed. This default behavior can be modified by assigning relaxation preferences to variable bounds and constraints.

These preferences can be conveniently specified with the. The input value denotes the users willingness to relax a constraint or bound. The larger the preference, the more likely it will be that a given bound or constraint will be relaxed. More precisely, the reciprocal of the specified value is used to weight the relaxation of that constraint or bound.

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.

It is not necessary to specify a unique preference for each bound or range. 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 syntax is:. First we turn the feasible relaxtion on. Futhermore, we specify that all variables v i,j have preference of 1, except variables over set element i1 , which have a preference of 2.

The variable over set element i1 and j2 has preference 0. Note that preferences are assigned in a procedural fashion so that preferences assigned later overwrite previous preferences. The same syntax applies for assigning preferences to equations as demonstrated above.

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. The parameter FeasOptMode allows different strategies in finding feasible relaxation in one or two phases.

In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change.

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. 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. Please check description of parameter FeasOptMode for details.

The Gurobi Optimizer provides a wide variety of parameters that allow you to control the operation of the optimization engines. The level of control varies from extremely coarse-grained e. While these parameters provide a tremendous amount of user control, the immense space of possible options can present a significant challenge when you are searching for parameter settings that improve performance on a particular model.

The purpose of the Gurobi tuning tool is to automate this search. The Gurobi tuning tool performs multiple solves on your model, choosing different parameter settings for each, in a search for settings that improve runtime. The longer you let it run, the more likely it is to find a significant improvement. A number of tuning-related parameters allow you to control the operation of the tuning tool. The most important is probably TuneTimeLimit , which controls the amount of time spent searching for an improving parameter set.

Other parameters include TuneTrials which attempts to limit the impact of randomness on the result , TuneResults which limits the number of results that are returned , and TuneOutput which controls the amount of output produced by the tool. While parameter settings can have a big performance effect for many models, they aren't going to solve every performance issue. One reason is simply that there are many models for which even the best possible choice of parameter settings won't produce an acceptable result.

Another limitation of automated tuning is that performance on a model can experience significant variations due to random effects particularly for MIP models.

This is the nature of search. The Gurobi algorithms often have to choose from among multiple, equally appealing alternatives. Seemingly innocuous changes to the model such as changing the order of the constraint or variables , or subtle changes to the algorithm such as modifying the random number seed can lead to different choices.

Often times, breaking a single tie in a different way can lead to an entirely different search. We've seen cases where subtle changes in the search produce X performance swings. While the tuning tool tries to limit the impact of these effects, the final result will typically still be heavily influenced by such issues.

The bottom line is that automated performance tuning is meant to give suggestions for parameters that could produce consistent, reliable improvements on your models. It is not meant to be a replacement for efficient modeling or careful performance testing. The Gurobi Compute Server allows you to use one or more servers to offload all of your Gurobi computations.

Gurobi compute servers support queuing and load balancing. You can set a limit on the number of simultaneous jobs each compute server will run. When this limit has been reached, subsequent jobs will be queued. If you have multiple compute servers, the current job load is automatically balanced among the available servers. However, jobs can be given different priorities CSPriority. Jobs with higher priorities are then selected from the queue before jobs with lower priorities. Contact suppo. Relevant options are ComputeServer and options starting with CS.

Gurobi Optimizer implements a number of distributed algorithms that allow you to use multiple machines to solve a problem faster. Available distributed algorithms are:. These distributed parallel algorithms are designed to be almost entirely transparent to the user. The user simply modifies a few parameters, and the work of distributing the computation to multiple machines is handled behind the scenes by Gurobi.

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. You can provide either machine names or IP addresses, and they should be comma-separated. You can provide the worker access password through the WorkerPassword parameter. All servers in the worker pool must have the same access password. 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.

These parameters are used to indicate how many distinct tuning, concurrent, or distributed MIP jobs should be started on the available workers. 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. Instead, it will launch as many jobs as it can up to the requested value , and it will run with these jobs.

These distributed algorithms have been designed to be nearly indistinguishable from the single machine versions.

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## Gurobi Installation Guide

The Gurobi suite of optimization products include state-of-the-art simplex and parallel barrier solvers for linear programming LP and quadratic programming QP , parallel barrier solver for quadratically constrained programming QCP , as well as parallel mixed-integer linear programming MILP , mixed-integer quadratic programming MIQP and mixed-integer quadratically constrained programming MIQCP solvers. The Gurobi MIP solver includes shared memory parallelism, capable of simultaneously exploiting any number of processors and cores per processor. The implementation is deterministic: two separate runs on the same model will produce identical solution paths. While numerous solving options are available, Gurobi automatically calculates and sets most options at the best values for specific problems. Starting with GAMS distribution For example, the following message is sent to the log when attempting to solve a model that requires a license:.

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## Documentation

Gurobi Optimization was founded in by some of the most experienced and respected members of the optimization community. The Gurobi solver quickly became an industry performance leader in linear, quadratic, and mixed-integer programming. Gurobi is a fantastic solver for use with CVX, particularly with the integer and binary variable capability added in CVX 2. Academic users : information about obtaining a license can be found on the Gurobi Academic Program page. Please contact CVX Sales for more information about either option, and Gurobi Sales for pricing information for standalone Gurobi licenses. Download the the appropriate CVX bundle from the CVX download page and following the regular installation instructions at Installation. The standard bundles include a CVX-specific version of the Gurobi version 9.