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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Created using Sphinx 0. You should never assign an lpSolve linear program model object in R code. This is the simplest way to work with constraints; numerous other ways are possible including replacing the nested list with a 2d numpy array or working with named variable blocks.

Many bookkeeping operations are automatically handled by abstracting similar variables into blocks that can be handled documentaiton a unit with arrays or matrices.

PyLPSolve — PyLPSolve v documentation

To install the lpSolve package use the command: Enter search terms or a module, class or function name. R documenyation not know how to deal with these structures. The lpSolveAPI package has a lot more functionality than lpSolvehowever, it also has a slightly more difficult learning curve. Thus there should be minimal overhead to using this wrapper.


R can be considered as a different implementation of S. In particular, R cannot duplicate them. You can find the project summary page here.

Good coverage by test cases. First we create an empty model x. You can list all of the functions in the lpSolveAPI package with the following command.

For more information or to download R please visit the R website. Written in Cython for speed; all low-level operations are done in compiled and optimized C code. The most important is that the lpSolve linear program model objects created by make. The safest way to use the lpSolve API is inside an R function – do not return the lpSolve linear program model object. LP sizing is handled automatically; a buffering system ensures this is fast and usable.

Consider the following example.

All the elements of the LP are cached until solve is called, with memory documentatiom and proper sizing of the LP in lpsolve handled automatically. Numerous other ways of working with constraints and named blocks of variables are possible.


Welcome to lpSolveAPI project!

This approach allows greater flexibility but also has a few caveats. The focus is on usability and integration with existing python packages used for scientific programming i. Full integration with numpy arrays.

For example, this code is an equivalent way to specify the constraints and objective:. 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.

lp_solve reference guide

PyLPSolve is written in Cythonwith all low-level processing done in optimized and compiled C for speed. There are some important differences, but much code written for S runs unaltered under R.

Both packages are available from CRAN. Note that you must append.