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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lp_solve reference guide

R does not know how to deal with these structures. To install the lpSolve package use the command: Full integration with numpy arrays. First we create an empty model x. You can list all of the functions in the lpSolveAPI package with the following command.

You should never assign an lpSolve linear program model object in R code. The safest way to use the lpSolve API lpwolve inside an R function – do not return the lpSolve linear program model object.

PyLPSolve — PyLPSolve v documentation

LP sizing is handled automatically; a buffering system ensures this is fast and usable. Many bookkeeping operations are automatically handled by abstracting similar variables into blocks that can be handled as a unit with arrays or matrices.


Numerous other ways of working with constraints and named blocks of variables are possible. For more information or to download R please visit the R website.

You can find the project summary page here.

R can be considered as a different implementation of S. Note that you must append. PyLPSolve is written in Cythonwith all low-level processing done in optimized and compiled C for speed. The plsolve is on usability and integration with existing python packages used for scientific programming i.

Consider the following example. 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.

In particular, R cannot duplicate them. 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. Good coverage by test cases. For example, this code is an equivalent way to specify the constraints and objective:. Thus there should be minimal overhead to using this wrapper.


There are some important differences, but much code written for S runs unaltered under R.

Welcome to lpSolveAPI project!

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. Written in Cython for speed; all low-level operations are done in docunentation and optimized C code.

This approach allows greater flexibility but also has a docukentation caveats. The most important is that the lpSolve linear program model objects created by make.

Enter search terms or a module, class or function name. Created using Sphinx 0. Both packages are available from CRAN. The lpSolveAPI package has a lot more functionality than lpSolvehowever, it also has a slightly more difficult learning curve.