What’s New?#

New book: Hands-On Mathematical Optimization with AMPL in Python#

Hands-On Mathematical Optimization with AMPL in Python

AMPL Community Edition#

Free, full-powered AMPL for personal, academic, or commercial prototyping purposes using AMPL APIs.

  • 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):

APIs included:

What else is included?

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://ampl.com/colab/tags/template.html) as a starting point. Our cloud licenses, including AMPL CE licenses, work on all cloud platforms.

Enhanced Solver Drivers with Automatic Reformulations#

We have released new MP-Based solver drivers for Gurobi, CPLEX, XPRESS, COPT, MOSEK, BARON, among others. They are included in the AMPL distribution bundle.

The new solver 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]

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.

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:

  • Conditional operators: if-then-else; ==>, <==, <==>

  • Logical operators: or, and, not; exists, forall

  • Piecewise linear functions: abs; min, max; <<breakpoints;slopes>>

  • Counting operators: count; atmost, atleast, exactly; numberof

  • Comparison operators: >, <, !=; alldiff

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 (*, /, ^) in 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.

New Solvers#

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 (exp, log; sin, cos, 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.

Snapshot Feature (Save and Restore AMPL Sessions)#

One of the most powerful features recently introduced in AMPL is the snapshot feature. It enables you to save the exact state of an AMPL session and restore it later, allowing you to seamlessly pick up where you left off.

This feature is particularly valuable in several scenarios:

  • Debugging: In complex applications where models and data are loaded dynamically, snapshots make it easy to capture the AMPL state at any point. This allows for quick and consistent reproduction of issues outside the original environment—on another machine or in a standalone session.

  • Warm-starting: A common use case in AMPL involves running the same model across thousands of scenarios in parallel, with slight data perturbations introduced by uncertainty. Instead of reloading models and reprocessing data in Python or another language, you can load a snapshot and apply only the necessary changes. This significantly reduces overhead and speeds up execution.

This technique is especially effective in high-performance applications, such as energy price forecasting. For example, power traders use snapshots to optimize their workflows in Electricity Market Simulations, leveraging AMPL to maximize performance and quickly adapt to shifting market conditions.

Example using it with amplpy#

You can then use AMPL.snapshot to retrieve a session snapshot as a string. The string returned is a compact representation of the AMPL state (model declaration, data, solution loaded, options, etc.)

ampl = AMPL()
...
snapshot = ampl.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

print('First object:')
ampl = AMPL()
ampl.read('diet.mod')
ampl.read_data('diet.dat')
ampl.solve(solver="gurobi")
ampl.snapshot("snapshot.run")  # save the session snapshot to a file

print('Second object:')
ampl2 = AMPL()
ampl2.read("snapshot.run")  # load the snapshot from the snapshot.run file 
ampl2.display('Buy')

print('Third object:')
ampl3 = AMPL()
snapshot = ampl2.snapshot()  # return a string with the session snapshot
ampl3.eval(snapshot)  # load the snapshot from the string
ampl3.solve()

First object:
Gurobi 11.0.0: 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:
Gurobi 11.0.0: optimal solution; objective 88.2
1 simplex iterations

One thing that may also be useful: In the example, there is the line ampl.snapshot("snapshot.run") that writes the snapshot to a file called snapshot.run. This file can be loaded into AMPL (e.g., for debugging) as follows:

$ ampl snapshot.run -
ampl: display Buy;
Buy [*] :=
BEEF   0
 CHK   0
FISH   0
 HAM   0
 MCH  46.6667
 MTL   0
 SPG   0
 TUR   0
;

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.

Important options:

  • email: your e-mail address (it is required by NEOS).

  • 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 20.1.0.0: optimal solution; objective 88.2
1 dual simplex iterations (0 in phase I)