AMPL Community Edition¶
Free, full-powered AMPL for commercial, academic and personal use.
License never expires
Unlimited variables & constraints
Get your free license at: https://ampl.com/ce
Solvers permanently available:
Open-source solvers: HiGHS, CBC, Couenne, Ipopt, Bonmin.
NEOS solvers (including commercial solvers), directly in AMPL through the kestrel interface.
Commercial solver trials readily available (30 days each):
Nonlinear: Artleys Knitro, CONOPT, LOQO, MINOS, SNOPT
Global: BARON, LGO, LINDO Global, OCTERACT
What else is included?
AMPL Model Colaboratory powered by our Python API!
Enhanced solver interfaces (e.g., Gurobi, COPT, XPRESS, HiGHS)
Data Connectors (e.g., .xlsx, .csv, ODBC) and Function Libraries (e.g., GSL)
It works with Docker Containers and Cloud Functions (e.g., AWS Lambda, Azure Functions, etc.)
Since it is a cloud license it can be used on continuous integration and continuous delivery (CI/CD) platforms.
Using AMPL in Google Colab, Kaggle, and similar platforms¶
We have a set of Jupyter Notebooks available on our Model Colaboratory.
You can use our template (https://colab.ampl.com/en/latest/tag-template.html) as a starting point. Our cloud licenses, including AMPL CE licenses, work on all cloud platforms.
Snapshot feature (save and restore AMPL sessions)¶
In your AMPL bundle you should find
x-ampl, the development version of AMPL where experimental features are enabled. One of such features is the snapshot command which allows saving the AMPL session in such a way that you can restore the state of AMPL using it.
Example using it with amplpy¶
You need to be using at least amplpy 0.8.0 (you can install it with
python -m pip install amplpy==0.8.0). With this version of amplpy it is possible to pass an additional argument to Environment that allows specifing the executable name as follows:
ampl = AMPL(Environment('', 'x-ampl'))
You can then use
AMPL.get_output to retrieve the output of the new command “snapshot;” as a string. The string returned is a compact representation of the AMPL state (model declaration, data, solution loaded, options, etc.)
snapshot = ampl.get_output('snapshot;') print(snapshot)
This string can then be passed to another AMPL object using
AMPL.eval. The following example produces the output below:
from amplpy import AMPL, Environment print('First object:') ampl = AMPL(Environment('', 'x-ampl')) ampl.read('diet.mod') ampl.read_data('diet.dat') ampl.option['solver'] = 'gurobi' ampl.solve() print('Second object:') ampl2 = AMPL(Environment('', 'x-ampl')) snapshot = ampl.get_output('snapshot;') print(snapshot, file=open('snapshot.run', 'w')) ampl2.eval(snapshot) ampl2.display('Buy') print('Third object:') ampl3 = AMPL(Environment('', 'x-ampl')) ampl3.eval(ampl2.get_output('snapshot;')) ampl3.display('_VARS;') ampl3.eval('option solver;')
First object: Gurobi 9.1.2: optimal solution; objective 88.2 1 simplex iterations Second object: Buy [*] := BEEF 0 CHK 0 FISH 0 HAM 0 MCH 46.6667 MTL 0 SPG 0 TUR 0 ; Third object: set _VARS := Buy; option solver gurobi;
One thing that may also be useful: In the example, there is the line
print(snapshot, file=open('snapshot.run', 'w')) that writes the snapshot to a file called
snapshot.run. This file can be loaded into AMPL (e.g., for debugging) as follows:
$ ampl ampl: include "snapshot.run"; ampl: display Buy; Buy [*] := BEEF 0 CHK 0 FISH 0 HAM 0 MCH 46.6667 MTL 0 SPG 0 TUR 0 ;
The snapshot feature is not finished and it is still being perfected. If you encounter any issues, please let us know.
Enhanced solver drivers: gurobi, copt, highs¶
We have released
gurobi, the enhanced
copt, an interface to Cardinal Optimizer,
highs, an interface to HiGHS.
They are included in the AMPL distribution bundle.
The drivers have the following features:
Full support of logical expressions and constraints, as described in the AMPL page on Logic and Constraint Programming Extensions.
Algebraic expressions beyond linear and quadratic.
Choice between conversions in the driver vs. native solver support.
[Modeling Guide] [gurobi options] [copt options] [highs options]
New AMPL-solver interface library¶
We’re rolling out a new AMPL-solver interface library that significantly expands the range of model expressions that can be used with popular solvers. Initially, the new library is being used to implement AMPL interfaces to two notable new solvers, COPT and HiGHS, and to provide greatly enhanced support for Gurobi’s generalized constraints. Extensions to other solvers will be released soon.
NEW SOLVER INTERFACE¶
Modeling languages aim to let you describe optimization models to a computer in much the same way that you describe models to other people. The newly extended “MP” interface brings AMPL closer to this goal, by allowing expanded use of a variety of convenient expressions in objectives and constraints. Notable examples include:
Piecewise linear functions:
These operators can be applied to general forms of AMPL expressions, and
thus can be used together in objective and constraint specifications. The
new interface also helps solvers to accept nonlinear operators (
a broader variety of circumstances.
A public MP Library repository on GitHub links to a modeling guide and documentation of the source code. See also the slides from our presentation of the new interface at this summer’s EURO and ICCOPT conferences, or attend updated presentations at the INFORMS Annual Meeting, October 15-19.
The first implementations using the MP interface library are now available in our regular distributions through the AMPL Portal.
An entirely new MP-based interface greatly expands the variety of AMPL
expressions that can be used with the Gurobi solver. The new implementation uses the solver’s native “generalized
constraints” where possible, but can be switched to use alternative
transformations built into MP. Common univariate nonlinear functions (
tan) are also supported, using Gurobi’s native
piecewise-linear approximation facilities.
Two relatively new linear/quadratic MIP solvers – COPT and HiGHS – are now also supported by AMPL, exclusively through the MP interface. Both are in active development and appear in recent benchmark listings. COPT, a product of Cardinal Operations, has joined the lineup of commercial solvers that we distribute. HiGHS, a free open-source solver, has evolved from a project at the University of Edinburgh. They appear as “copt” and “highs” in AMPL distributions.
Currently supported MIP solvers such as Xpress and CPLEX are also planned to have versions with the new interface. Also we will soon be distributing the MOSEK solver with an MP interface.
Using remote solvers from NEOS with gokestrel¶
To simplify the work of comparing and testing solvers, we have made AMPL and solver resources available online in collaboration with the NEOS Server project, under the auspices of the Wisconsin Institutes for Discovery at the University of Wisconsin, Madison.
Thanks to gokestrel, our new Kestrel driver, instead of specifying a solver installed on your computer or local network, you invoke Kestrel, a “client” program that sends your problem to a solver running on one of the NEOS Server’s remote computers. The results from the NEOS Server are eventually returned through Kestrel to AMPL, where you can view and manipulate them locally in the usual way.
kestrel_options: allows you to specify among other things, the solver to use
As an example, here is how you might invoke Kestrel from a local AMPL session, using CPLEX as your remote solver:
ampl: model diet.mod; ampl: data diet.dat; ampl: option solver kestrel; ampl: option email "***@***.***"; ampl: option kestrel_options "solver=cplex"; ampl: option cplex_options "display=2"; ampl: solve; Connecting to: neos-server.org:3333 Job XXXX submitted to NEOS, password='xxxx' Check the following URL for progress report: https://neos-server.org/neos/cgi-bin/nph-neos-solver.cgi?admin=results&jobnumber=XXXX&pass=xxxx Job XXXX dispatched password: xxxx ---------- Begin Solver Output ----------- Condor submit: 'neos.submit' Condor submit: 'watchdog.submit' Job submitted to NEOS HTCondor pool. CPLEX 188.8.131.52: optimal solution; objective 88.2 1 dual simplex iterations (0 in phase I)