Gurobi#

Gurobi is a powerful commercial suite of optimization products, which include simplex, parallel and nonlinear barrier, and primal-dual hybrid gradient (PDHG) solvers to handle linear programming (LP), quadratic programming (QP), and nonlinear programming (NLP); also quadratically constrained (QCP, SOCP) and the mixed-integer variations thereof (MILP, MIQP, MIQCP, MISOCP, MINLP). The (MI)NLP capability is provided via nonlinear expressions (formulas). The MP Library used by the AMPL Gurobi driver supports automatic reformulation for many expression types; the modeling guide can be found here.

Product Page | Modeling guide | Features | Options | Changes

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Our enhanced Gurobi driver is now the default. The new driver provides significantly extended modeling support for logical and nonlinear operators natively through Gurobi’s built-in nonlinear and “general constraints” and through linearizations performed by the MP Library. There are two binaries in this package: gurobi [options] is the new version, gurobiasl [options] is the legacy version. If you are upgrading an existing installation and encounter any issues with the new version please report them to support@ampl.com.

How to use it#

How to install using amplpy:

# Install Python API for AMPL:
$ python -m pip install amplpy --upgrade

# Install AMPL & solver modules:
$ python -m amplpy.modules install gurobi # install Gurobi

# Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
$ python -m amplpy.modules activate <your-license-uuid>

How to use:

from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="gurobi",
           gurobi_options="option1=value1 option2=value2")

Learn more about what we have to offer to implement and deploy Optimization in Python.

AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:

ampl: option solver gurobi;                  # Select solver
ampl: option gurobi_options 'option1=value1 option2=value2';
ampl: solve;

At a glance#

Resources#

Features#

Gurobi options#

Full list of AMPL/Gurobi options:

Many solver parameters can be changed directly from AMPL, by specifying them as a space-separated string in the AMPL option gurobi_options, or mp_options. A list of all supported solver options is available here or can be obtained by executing the solver driver with the -= command line parameter:

import os
exit_code = os.system("gurobi -=")

AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:

ampl: shell "gurobi -=";

$ gurobi -=

Solver options can have multiple aliases, to accomodate for different user types. The main nomenclature is given first in the -= output, then followed by aliases in brackets, see for example the listing for lim:iter:

lim:iter (iterlim, iterlimit)
      Iteration limit (default: no limit).

The main nomenclature contains a prefix (lim: in this case) to help categorize and find the options relevant to a context. To list only the options with a specific prefix (lim: for this example), run:

gurobi -=lim:

More details on solver options: Features guide.

Specify solver options and solve#

After formulating the model in AMPL, execute the following to select gurobi as the solver and pass the two options: return_mipgap=3 and outlev=1.

ampl.option["show_stats"] = 2
ampl.solve(solver="gurobi",
    mp_options="return_mipgap=3 outlev=1",     # or: gurobi_options
    verbose=True)

AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:

ampl: option solver gurobi;                    # Select solver
ampl: option mp_options 'return_mipgap=3 outlev=1';
ampl: solve [>output.log];                     # Solve [redirect output]

$ [auxfiles=rc] ampl -obmodel snapshot.run     # Produce an NL file
                                               # [with name files]
$ mp_options='lim:time=100' gurobi model       # Version 1
$ gurobi_options='kappa_exact=1' gurobi model  # Version 2
$ gurobi model lim:time=100 wantsol=7          # Version 3

Retrieve solutions#

The outcome of the last optimization is stored in the AMPL parameter solve_result_num and the related message in solve_result.

print(ampl.solve_result, ampl.solve_result_num)
ROI_Values = ampl.var['ROI_Level'].to_pandas()

ampl.param["a"] = ampl2.var["x"].get_values()
ampl.eval(r" x >(“obj” & n & “.out”) ;")       #save solution in a .out file

AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:

ampl: display solve_result_num, solve_result;
ampl: display _varname, _var, _conname, _con.slack;
ampl: display _logconname, min {i in 1.._nlogcons} _logcon[i].val;
                                               # Logical constraints
ampl: display _cconname, _ccon.slack;          # Complementarities

$ gurobi model lim:time=100 wantsol=6          # Print solution
$ gurobi model timing=2 wantsol=1              # Save .sol file
                                               # to be explored in AMPL

Gurobi solve result codes can be obtained by running gurobi -! or ampl: shell "gurobi -!";:

Solve result table for Gurobi 13.0.1
	  0- 99	solved: optimal for an optimization problem,
		feasible for a satisfaction problem 
	100-199	solved? solution candidate returned but error likely 
	    120	locally optimal solution
	    150	solved? MP solution check failed (option sol:chk:fail) 
	200-299	infeasible 
	    220	locally infeasible solution
	300-349	unbounded, feasible solution returned 
	350-399	unbounded, no feasible solution returned 
	400-449	limit, feasible: stopped, e.g., on iterations or Ctrl-C 
	    401	interrupted, feasible solution
	    402	time limit, feasible solution
	    403	iteration limit, feasible solution
	    404	node limit, feasible solution
	    405	bestobjstop or bestbndstop reached, feasible solution
	    408	solution limit
	    409	work limit, feasible solution
	    410	soft memory limit, feasible solution
	450-469	limit, problem is either infeasible or unbounded.
		Disable dual reductions or run IIS finder for definitive answer.
	470-499	limit, no solution returned 
	    471	interrupted, without a feasible solution
	    472	time limit, without a feasible solution
	    473	iteration limit, without a feasible soluton
	    474	node limit, without a feasible soluton
	    475	objective cutoff
	    477	bestbndstop reached, without a feasible solution
	    479	work limit, without a feasible solution
	    480	soft memory limit, without a feasible solution
	500-999	failure, no solution returned 
	    550	failure: numeric issue, no feasible solution
	    601	Could not talk to Gurobi Instant Cloud or Gurobi Server.
	    602	Job rejected by Gurobi Instant Cloud or Gurobi Server.
	    603	No license for specified Gurobi Instant Cloud or Gurobi Server.
	    604	Surprise failure while starting the cloud/server environment.
	    605	Bad value for cloudid or cloudkey, or Gurobi Cloud out of reach.

For general information, see MP result codes guide.

Handle infeasibility#

Irreducible inconsistent set (IIS)#

When a model is infeasible, one useful information kind is the IIS, which are subsets of constraints that are incompatible. See the details here.

Feasibility relaxation#

Another approach to tackle infeasibilities is the feasibility relaxation to find a solution which only penalizes infeasibilities. See the details here.

Gurobi compute server and Gurobi cloud#

Gurobi supports solving your model on other machines in two alternative ways:

  • Compute server - where a computer (or a cluster) can be configured to the specific task of solving models via Gurobi

  • Gurobi cloud - where the compute server is by Gurobi itself

To use a compute server, the option tech:server must be set, together with appropriate values for the tech:server_* options. For Gurobi instant cloud, the options tech:cloudid and tech:cloudkey must be set, and optionally the other options tech:cloud*, for example:

# Solve with compute server
option gurobi_options "tech:server=192.168.1.55";
solve;

# Solve with Gurobi instant cloud
option gurobi_options "tech:cloudid=mygurobiid tech:cloudkey=myprivatekey";
solve;

Changelog#