Welcome to lpSolveAPI project!

An R interface for lp_solve, a Mixed Integer Linear Programming (MILP) solver with support for pure linear, (mixed) integer/binary, semi-continuous and special ordered sets (SOS) models.

Using lp_solve in R

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. For more information or to download R please visit the R website.

There are currently two R packages based on lp_solve. The lpSolve package provides high-level functions for solving general linear/integer problems, assignment problems and transportation problems. The lpSolveAPI package provides a complete implementation of the lp_solve API. The lpSolveAPI package has a lot more functionality than lpSolve, however, it also has a slightly more difficult learning curve. Both packages are available from CRAN.

Caveat (19.04.2011): the lpSolve package is based on lp_solve version 5.5.0.7 which was released on 27.12.2007. The current version of lp_solve (used in the lpSolveAPI package) is 5.5.2.0 and was released on 22.08.2010.

You can find the project summary page here.

Installation

To install the lpSolve package use the command:

  > install.packages("lpSolve")

and to install the lpSolveAPI package use the command:

  > install.packages("lpSolveAPI")

After the packages have been downloaded and installed, you can load them into your R session using the library function, e.g.,

  > library(lpSolveAPI)

This needs to be done once in each R session (i.e., every time you launch R).

Note

The > shown before each R command is the R prompt. Only the text after > should be entered.

Getting Help

Documentation for the lpSolve and lpSolveAPI packages is provided using R's built-in help system. For example, the command

  > ?make.lp

will display the documentation for the make.lp function. You can list all of the functions in the lpSolveAPI package with the following command.

  > ls("package:lpSolveAPI")

The documentation for each of these functions can be accessed using the ? operator. Note that you must append .lpExtPtr to the names of the generic functions (dim, dimnames, plot, print and solve), otherwise you will get the documentation for the standard generic function.

Building and Solving Linear Programs Using the lpSolveAPI Package

The lpSolveAPI package provides an API for building and solving linear programs that mimics the lp_solve C API. This approach allows greater flexibility but also has a few caveats. The most important is that the lpSolve linear program model objects created by make.lp and read.lp are not actually R objects. Rather, they are pointers to lp_solve 'lprec' structures which are created and store externally. R does not know how to deal with these structures. In particular, R cannot duplicate them. You should never assign an lpSolve linear program model object in R code.

Consider the following example. First we create an empty model x.

  > x <- make.lp(2, 2)

Then we assign x to y.

  > y <- x

Next we set some columns in x.

  > set.column(x, 1, c(1, 2))
  > set.column(x, 2, c(3, 4))

And finally, take a look at y.

  > y
  Model name: 
              C1    C2         
  Minimize     0     0         
  R1           1     3  free  0
  R2           2     4  free  0
  Type      Real  Real         
  upbo       Inf   Inf         
  lowbo        0     0

The changes we made in x appear in y as well. Although x and y are two distinct objects in R, they both refer to the same lp_solve 'lprec' structure.

The safest way to use the lpSolve API is inside an R function - do not return the lpSolve linear program model object.

A brief Example

  > lprec <- make.lp(0, 4)
  > set.objfn(lprec, c(1, 3, 6.24, 0.1))
  > add.constraint(lprec, c(0, 78.26, 0, 2.9), ">=", 92.3)
  > add.constraint(lprec, c(0.24, 0, 11.31, 0), "<=", 14.8)
  > add.constraint(lprec, c(12.68, 0, 0.08, 0.9), ">=", 4)
  > set.bounds(lprec, lower = c(28.6, 18), columns = c(1, 4))
  > set.bounds(lprec, upper = 48.98, columns = 4)
  > RowNames <- c("THISROW", "THATROW", "LASTROW")
  > ColNames <- c("COLONE", "COLTWO", "COLTHREE", "COLFOUR")
  > dimnames(lprec) <- list(RowNames, ColNames)

Lets take a look at what we have done so far.

  > lprec  # or equivalently print(lprec)
  Model name: 
              COLONE    COLTWO  COLTHREE   COLFOUR          
  Minimize         1         3      6.24       0.1          
  THISROW          0     78.26         0       2.9  >=  92.3
  THATROW       0.24         0     11.31         0  <=  14.8
  LASTROW      12.68         0      0.08       0.9  >=     4
  Type          Real      Real      Real      Real          
  Upper          Inf       Inf       Inf     48.98          
  Lower         28.6         0         0        18

Now lets solve the model.

  > solve(lprec)
  [1] 0

  > get.objective(lprec)
  [1] 31.78276

  > get.variables(lprec)
  [1] 28.60000  0.00000  0.00000 31.82759

  > get.constraints(lprec)
  [1]  92.3000   6.8640 391.2928