MATLAB资源 MP选择模型 仅供学习参考用代码.rar

MP-Opt-Model ============ [MP-Opt-Model][1] 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][2]. System Requirements ------------------- * [MATLAB][3] version 7.5 (R2007b) or later, or * [GNU Octave][4] version 4 or later * [MP-Test][5], for running the MP-Opt-Model test suite * [MATPOWER Interior Point Solver (MIPS)][6] Installation ------------ **Note to [MATPOWER][2] users:** _MP-Opt-Model and its prerequisites, MIPS and MP-Test, are included when you install [MATPOWER][2]. 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. 1. Clone the repository or download and extract the zip file of the MP-Opt-Model distribution from the [MP-Opt-Model project page][1] 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. 2. Add the following directories to your MATLAB or Octave path: * `<MPOM>/lib` * `<MPOM>/lib/t` 3. At 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][5] and [MIPS][6]) 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 (68 of 288 skipped) t_nlps_master...........ok t_opt_model.............ok t_om_solve_leqs.........ok t_om_solve_nleqs........ok (36 of 194 skipped) t_om_solve_pne..........ok t_om_solve_qps..........ok (81 of 387 skipped) t_om_solve_miqps........ok (14 of 118 skipped) t_om_solve_nlps.........ok All tests successful (3430 passed, 329 skipped of 3759) Elapsed time 4.22 seconds. ``` Sample Usage ------------ 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 <= zmax ``` And suppose the data for the problem is provided as follows. ```matlab %% 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. ```matlab %%----- 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. ```matlab %%----- 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 0.6250 ``` An 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()`][12] and [`opf_execute()`][13] functions in [MATPOWER][2]. Documentation ------------- There are two primary sources of documentation for MP-Opt-Model. The first is the [MP-Opt-Model User's Manual][7]. 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`. [Citing MP-Opt-Model][10] ------------------------- 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][7]. The citation and DOI can be version-specific or general, as appropriate. For version 4.1, use: > R. D. Zimmerman. *MP-Opt-Model User's Manual, Version 4.1*. 2022. [Online]. Available: https://matpower.org/docs/MP-Opt-Model-manual-4.1.pdf doi: [10.5281/zenodo.7434250](https://doi.org/10.5281/zenodo.7434250) 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][11] 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. Contributing ------------ Please see our [contributing guidelines][8] for details on how to contribute to the project or report issues. License ------- MP-Opt-Model is distributed under the [3-clause BSD license][9]. ---- [1]: https://github.com/MATPOWER/mp-opt-model [2]: https://matpower.org/ [3]: https://www.mathworks.com/ [4]: https://www.gnu.org/software/octave/ [5]: https://github.com/MATPOWER/mptest [6]: https://github.com/MATPOWER/mips [7]: docs/MP-Opt-Model-manual.pdf [8]: CONTRIBUTING.md [9]: LICENSE [10]: CITATION [11]: https://doi.org/10.5281/zenodo.3818002 [12]: https://github.com/MATPOWER/matpower/b