IBM ILOG CPLEX Optimization Studio (often informally referred to simply as CPLEX) is an optimization software package. In 2004, the work on CPLEX earned the first INFORMS Impact Prize. CPLEX Optimizer provides flexible, high-performance mathematical programming solvers for linear programming, mixed integer programming, quadratic programming and quadratically constrained programming problems. The NEOS Server optimization solvers represent the state-of-the-art in computational optimization. Optimization problems are solved automatically with minimal input from the user. Here I’ve selected CPLEX and Gurobi, since they are among the leading commercial solvers, and PuLP, which is a powerful open-source modeling package in Python. I’ll provide a side-by-side. There is a tile for ILOG CPLEX Optimization Studio and in it a link 'download'. Clicking this results in a message that the resource is currently unavailable. I tried this on three subsequent.
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Take me there!OMPR (Optimization Modeling Package) is a DSL to model and solve Mixed Integer Linear Programs. It is inspired by the excellent Jump project in Julia.
Here are some problems you could solve with this package:
- What is the cost minimal way to visit a set of clients and return home afterwards?
- What is the optimal conference time table subject to certain constraints (e.g. availability of a projector)?
The Wikipedia article gives a good starting point if you would like to learn more about the topic.
I am always happy to get bug reports or feedback.
Install
CRAN
Development version
To install the current development version use devtools:
Available solver bindings
Package | Description | Build Linux | Build Windows | Test coverage |
---|---|---|---|---|
ompr.roi | Bindings to ROI (GLPK, Symphony, CPLEX etc.) |
Cplex Python
A simple example:
API
These functions currently form the public API. More detailed docs can be found in the package function docs or on the website
DSL
MIPModel()
create an empty mixed integer linear model (the old way)MILPModel()
create an empty mixed integer linear model (an alternative way; experimental, especially suitable for large models)add_variable()
adds variables to a modelset_objective()
sets the objective function of a modelset_bounds()
sets bounds of variablesadd_constraint()
add constraintssolve_model()
solves a model with a given solverget_solution()
returns the column solution (primal or dual) of a solved model for a given variable or group of variablesget_row_duals()
returns the row duals of a solution (only if it is an LP)get_column_duals()
returns the column duals of a solution (only if it is an LP)
Backends
There are currently two backends. A backend is the function that initializes an empty model.
MIPModel()
is the standard MILP ModelMILPModel()
is another backend specifically optimized for linear models and is about 1000 times faster thanMIPModel()
. It has slightly different semantics, as it is vectorized. Currently experimental.
Solver
Solvers are in different packages. ompr.ROI
uses the ROI package which offers support for all kinds of solvers.
with_ROI(solver = 'glpk')
solve the model with GLPK. InstallROI.plugin.glpk
with_ROI(solver = 'symphony')
solve the model with Symphony. InstallROI.plugin.symphony
with_ROI(solver = 'cplex')
solve the model with CPLEX. InstallROI.plugin.cplex
- … See the ROI package for more plugins.
Further Examples
Please take a look at the docs for bigger examples.
Knapsack
Bin Packing
An example of a more difficult model solved by symphony.
License
Currently GPL.
Contributing
Please post an issue first before sending a PR.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
Related Projects
Cplex Download
- CVXR - an excellent package for “object-oriented modeling language for convex optimization”. LP/MIP is a special case.
- ROML follows a similiar approach, but it seems the package is still under initial development.