R topics documented: lp. Details can be found in the lpSolve docu- current version is maintained at Repository/R-Forge/DateTimeStamp Date/Publication NeedsCompilation yes. R topics documented: . Caveat (): the lpSolve package is based on lp_solve version Documentation for the lpSolve and lpSolveAPI packages is provided using R’s.

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To install the lpSolve package use the command: For more information or to download R please visit the R website. Note that you must append. Both packages are available from CRAN. You can list all of the functions in the lpSolveAPI package with the following command.

PyLPSolve — PyLPSolve v documentation

There are some important differences, but much code written for S runs unaltered under R. Numerous other ways of working with constraints and named blocks of variables are possible.

R does not know how to deal with these structures. Good coverage by test cases. Lspolve safest way to use the lpSolve API is inside an R function – do not return the lpSolve linear program model object.


Full integration with numpy arrays. Written in Cython for speed; all low-level operations are done in compiled and optimized C code.

PyLPSolve is written in Cythonwith all low-level processing done in optimized and compiled C for speed. R can be considered as a different implementation of S. This is the simplest way to work with constraints; numerous other lpsokve are possible including replacing the nested list with a 2d numpy array or working with named variable blocks.

Welcome to lpSolveAPI project!

Many bookkeeping operations are automatically handled by abstracting similar variables into blocks that can be handled as a unit with arrays or matrices. The most important is that the lpSolve linear program model objects created by make. One unique feature is a convenient bookkeeping system that allows the user to specify blocks of variables by string tags, or other index block methods, then work with these blocks instead of individual indices.

Consider the following example. The focus is on usability and integration with existing python packages used for scientific programming i.


lp_solve reference guide

LP sizing is handled automatically; a buffering system ensures this is fast and usable. First we create an empty model x.

Thus there should be minimal overhead to using this wrapper. You can find the project summary page here. Enter search terms or a module, class or function name. This approach allows greater flexibility but also has a few caveats. Created using Sphinx 0. You should never assign an lpSolve linear program model object in R code. All the elements of the LP are cached until solve is called, with memory management and proper sizing of the LP in lpsolve handled automatically.

For example, this code is an equivalent way to specify the constraints and objective:. In particular, R cannot duplicate them.

The lpSolveAPI package has a lot more functionality than lpSolvehowever, it also has a slightly more difficult learning curve.