MP-Opt-Model is a package of MATLAB/Octave M-files for constructing and solving mathematical programming and optimization problems. It provides an easy-to-use, object-oriented interface for building and solving your model. It also includes a unified interface for calling numerous LP, QP, mixed-integer and nonlinear solvers, with the ability to switch solvers simply by changing an input option.
It is based on code that was originally developed by Ray D. Zimmerman of Cornell University as part of MATPOWER.
Note to MATPOWER users: MP-Opt-Model and its prerequisites, MIPS and MP-Test, are included when you install MATPOWER. There is generally no need to install MP-Opt-Model separately. You can skip directly to step 3 to verify.
Installation and use of MP-Opt-Model requires familiarity with the basic operation of MATLAB or Octave, including setting up your MATLAB/Octave path.
Clone the repository or download and extract the zip file of the MP-Opt-Model
distribution from the MP-Opt-Model project page to the location of your
choice. The files in the resulting mp-opt-model or mp-opt-modelXXX directory,
where XXX depends on the version of MP-Opt-Model, should not need to be
modified, so it is recommended that they be kept separate from your
own code. We will use <MPOM> to denote the path to this directory.
Add the following directories to your MATLAB or Octave path:
<MPOM>/lib<MPOM>/lib/tAt the MATLAB/Octave prompt, type test_mp_opt_model to run the test suite and
verify that MP-Opt-Model is properly installed and functioning. (Note: The
tests require functioning installations of both MP-Test and
MIPS) The result should resemble the following:
>> test_mp_opt_model
t_have_fcn..............ok
t_nested_struct_copy....ok
t_nleqs_master..........ok (30 of 150 skipped)
t_pnes_master...........ok
t_qps_master............ok (100 of 432 skipped)
t_miqps_master..........ok (78 of 318 skipped)
t_nlps_master...........ok
t_opt_model.............ok
t_om_solve_leqs.........ok
t_om_solve_nleqs........ok (36 of 195 skipped)
t_om_solve_pne..........ok
t_om_solve_qps..........ok (81 of 388 skipped)
t_om_solve_miqps........ok (20 of 131 skipped)
t_om_solve_nlps.........ok
All tests successful (3461 passed, 345 skipped of 3806)
Elapsed time 2.28 seconds.Suppose we have the following constrained 4-dimensional quadratic programming (QP) problem with two 2-dimensional variables, y and z, and two constraints, one equality and the other inequality, along with lower bounds on all of the variables.
min 1/2 [y; z]' * Q * [y; z]
y,z
subject to:
A1 * [y; z] = b1
A2 * y <= u2
y >= ymin
z <= zmaxAnd suppose the data for the problem is provided as follows.
%% variable initial values
y0 = [1; 0];
z0 = [0; 1];
%% variable lower bounds
ymin = [0; 0];
zmax = [0; 2];
%% constraint data
A1 = [ 6 1 5 -4 ]; b1 = 4;
A2 = [ 4 9 ]; u2 = 2;
%% quadratic cost coefficients
Q = [ 8 1 -3 -4;
1 4 -2 -1;
-3 -2 5 4;
-4 -1 4 12 ];Below, we will show two approaches to construct and solve the problem.
The first method, based on the the Optimization Model class opt_model,
allows you to add variables, constraints and costs to the model
individually. Then opt_model automatically assembles and solves the
full model automatically.
%%----- METHOD 1 -----
%% build model
om = opt_model;
om.add_var('y', 2, y0, ymin);
om.add_var('z', 2, z0, [], zmax);
om.add_lin_constraint('lincon1', A1, b1, b1);
om.add_lin_constraint('lincon2', A2, [], u2, {'y'});
om.add_quad_cost('cost', Q, []);
%% solve model
[x, f, exitflag, output, lambda] = om.solve();The second method requires you to construct the parameters for the full problem manually, then call the solver function directly.
%%----- METHOD 2 -----
%% assemble model parameters manually
xmin = [ymin; -Inf(2,1)];
xmax = [ Inf(2,1); zmax];
x0 = [y0; z0];
A = [ A1; A2 0 0];
l = [ b1; -Inf ];
u = [ b1; u2 ];
%% solve model
[x, f, exitflag, output, lambda] = qps_master(Q, [], A, l, u, xmin, xmax, x0);The above examples are included in <MPOM>/lib/t/qp_ex1.m along with
some commands to print the results, yielding the output below for
each approach:
f = 1.875 exitflag = 1
var bound shadow prices
x lambda.lower lambda.upper
0.5000 0.0000 0.0000
0.0000 5.1250 0.0000
-0.0000 0.0000 8.7500
-0.2500 0.0000 0.0000
constraint shadow prices
lambda.mu_l lambda.mu_u
1.2500 0.0000
0.0000 0.6250An options struct can be passed to the solve method or the
qps_master function to select a specific solver, control the level of
progress output, or modify a solver's default parameters.
Both approaches can be applied to each of the types of problems that MP-Opt-Model handles, namely, LP, QP, MILP, MIQP, NLP and nonlinear equations.
There are also examples in the test files in <MPOM>/lib/t, as well as in
the opf_setup() and opf_execute() functions in MATPOWER.
There are two primary sources of documentation for MP-Opt-Model.
The first is the MP-Opt-Model User's Manual. It can be found in
your MP-Opt-Model distribution at <MPOM>/docs/MP-Opt-Model-manual.pdf
and the latest version is always available at:
https://github.com/MATPOWER/mp-opt-model/blob/master/docs/MP-Opt-Model-manual.pdf.
And second is the built-in help command. As with the built-in
functions and toolbox routines in MATLAB and Octave, you can type help
followed by the name of a command or M-file to get help on that particular
function. Many of the M-files in MP-Opt-Model have such documentation and this
should be considered the main reference for the calling options for each
function, e.g.: qps_master, miqps_master, and nlps_master.
We request that publications derived from the use of MP-Opt-Model explicitly acknowledge that fact by citing the MP-Opt-Model User's Manual. The citation and DOI can be version-specific or general, as appropriate. For version 4.2, use:
R. D. Zimmerman. MP-Opt-Model User's Manual, Version 4.2. 2024. [Online]. Available: https://matpower.org/docs/MP-Opt-Model-manual-4.2.pdf
doi: 10.5281/zenodo.11177079
For a version non-specific citation, use the following citation and DOI, with \<YEAR> replaced by the year of the most recent release:
R. D. Zimmerman. MP-Opt-Model User's Manual. \<YEAR>. [Online]. Available: https://matpower.org/docs/MP-Opt-Model-manual.pdf
doi: 10.5281/zenodo.3818002
A list of versions of the User's Manual with release dates and version-specific DOI's can be found via the general DOI at https://doi.org/10.5281/zenodo.3818002.
Please see our contributing guidelines for details on how to contribute to the project or report issues.
MP-Opt-Model is distributed under the 3-clause BSD license.