Gurobi Academic

[Kerrie Gingrich]

Wemake it easy for students, faculty, and researchers to work with mathematical optimization. Whether for use in class or research, academics can use Gurobi Optimizer at no cost. Get all the same Gurobi features and performance, with no limits on model size.

I am attempting to suppress the academic license output from Gurobi. I have to solve an LP several times in my code and the print statement is slow and annoying to look at. I have created a working example. I wish to be able to execute the LP in parallel in Julia to increase performance. To suppress the output I use the package Suppressor. This seems to work well when running only using a single processor. However if I wrap the LP in a function and insert this into a loop and attempt to run the model in parallel I get an error:

I am capable of running the code in parallel on MAC when it is not suppressed. Is it just because the hardware in ancient (the cores might not be able to communicate correctly to share information)? or is there a fix to this issue?

I couldn't get these instructions to work. (on Catalina, it seemsopening a new terminal window doesn't give the terminal the proxyinformation... and I was still getting the 'not an academic domain'error) But this led me to an easier fix...

I have faced the same problem in getting an educational license since my college activities are fully remote. My solution (and I think the easiest way) is to open a support ticket and send several documents to validate you are the student.

I am having this very problem at the moment (actually, not hypothetically). I would like to use Mosek with CVX Professional on an academic cluster, but cannot because every node of the cluster has a different host ID. Can you please advise?

Today I read in the forum that if I install Mosek independently and make it work within matlab, then I can just install a regular CVX package and it should detect Mosek and I should be able to use it. Would this be a work around to get CVX working on a cluster? Has anyone tried this?

Gurobi Optimization was founded in 2008 by some of the mostexperienced 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 new integer and binary variable capability added in CVX 2.0.

Important note for academic users: this step must be run from a computerconnected to your university network (a VPN is usually sufficient). Pleaseconsult this pageof the Gurobi documentation for details.

If you complete these steps successfully, cvx_setup will add Gurobi to its solverlist. If you have an academic or dual-solver CVX Professional license, the MOSEK solverwill be added to the solver list as well. If for some reason installation fails, theoutput of cvx_setup will provide diagnostic information that you can use to rectifythe problem. If you are still unable to complete the installation, feel free to contactCVX Support.

If you complete these steps successfully, cvx_setup will show that Gurobi has beenrecognized and added to the solver list. If for some reason installation fails, theoutput of cvx_setup will provide diagnostic information that you can use to rectifythe problem. If you are still unable to complete the installation, feel free to contactCVX Support.

If you encounter problems using CVX and Gurobi, please contactCVX Support first instead of Gurobi Optimization.If we can reproduce your problem, we will determine whether or not it is anissue that is unique to CVX or needs to be forwarded to Gurobi for furtheranalysis.

After the installation you need to restart.

Then, the gurobi installation needs to be activated:

On the gurobi website you can review your licenses under Downloads & Licenses -> Your Gurobi Licenses. If there is nothing, then request a new one (also on the Downloads & Licenses page). Once you have a table entry there, click on it and follow the instructions:

paste the grbgetkey XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXXX command that is mentioned under installation to your search bar and click on Run command. This will open a terminal and ask you to confirm the location for the license file.

there is a bug in this version - which will make it not find the gurobi solver (or the cplex one). The solution to this is copying the relevant .dll file into the ilastik installation (you need administrative privileges for this).:

To download the installer:First, you must register for a free Gurobi account as an academic and log in.If you would like to connect to the Academic Site License:During the configuration process, you will get to a step where you need to create a gurobi.lic file.In that file, the only text you need is TOKENSERVER=GUROBI.EOS.NCSU.EDU. This will point at the College of Engineering's Gurobi token server.To connect to the token server, you will need to be either on campus or connected to the NCSU VPN.

Gurobi is the most powerful and fastest solver that the prioritizr R package can use to solve conservation planning problems (see the Solver benchmarks vignette for further details). This guide will walk you through the process of setting up Gurobi on your computer so that it can be used to solve conservation planning problems. If you encounter any problems while following the instructions below, please refer to 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. If you are an academic that is affiliated with a recognized educational institution, you can take advantage of the special academic license to use Gurobi for no cost. Once you have signed up for a free account you can request a free academic license.

After obtaining a license, you will need to download the Gurobi installer to your computer. To achieve this, visit the Gurobi downloads web page and download the correct version of the installer for your operating system.

Next, we will now check that the license has been successfully activated. To achieve this, we will try running Gurobi directly from the command line. Note that the following commands assume you are using version 8.0.0 of Gurobi, and so you will need to modify the command if you are using a more recent version (e.g., if using version 9.1.2, then use gurobi912 instead of gurobi800 below).

After activating the license, you now need to install the gurobi R package. This is so that you can access the Gurobi software from within the R statistical computing environment, and enable the prioritizr package to interface with the Gurobi software.

Now we will install the gurobi R package. This package is not available on the Comprehensive R Archive Network and is instead distributed with the Gurobi software suite. Specifically, the gurobi R package should be located within the folder where you installed the Gurobi software suite. We will install the gurobi R package by running the following R code within your R session. Note that the following code assumes that you are using version 8.0.0 of Gurobi, and so you will need to modify the code if you are using a more recent version (e.g., if using version 9.1.2, then use gurobi912 instead of gurobi800 below).

Next, you will need to install the slam R package because the gurobi R package needs this package to work. Users of all platforms (i.e., Windows, Linux, and MacOS) can install the package using the following R code.

If you successfully installed the Gurobi software suite and the gurobi R package, you can now try solving conservation planning problems using the prioritzr R package. Although the prioritizr R package should automatically detect that Gurobi has been installed, you can use the function add_gurobi_solver() to manually specify that Gurobi should be used to solve problems. This function is also useful because you can use it to customize the optimization process (e.g., specify the desired optimality gap or set a limit on how much time should be spent searching for a solution).

After running this code, hopefully, you should some information printed on-screen about the optimization process and R should produce a map displaying a solution. If this code does not produce any errors, then you have successfully installed everything and can begin using Gurobi and the prioritizr R package to solve your very own conservation planning problems.

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