Linp: Solves a linear inverse model using linear programming.
In LIM: Linear Inverse Model examples and solution methods

Description Usage Arguments Details Value Author(s) References See Also Examples

Solves a linear inverse model using linear programming

Input presented either as:

matrices E, F, A, B, G, H (Linp.double) or
as a list (Linp.lim) or
as a lim input file (Linp.limfile)

Linp(...)
## S3 method for class 'lim'
Linp(lim, cost = NULL, ispos = lim$ispos, ...)
## S3 method for class 'limfile'
Linp(file, verbose = TRUE,...)
## S3 method for class 'character'
Linp(...)
## S3 method for class 'double'
Linp(...)

`lim`	a list that contains the linear inverse model specification, as generated by function `setup.limfile`.
`file`	name of the inverse input file.
`verbose`	if `TRUE`: when reading the file, prints warnings and messages to the screen.
`cost`	if not `NULL`, a vector with the coefficients of the cost function (to be minimised).
`ispos`	if `TRUE`: all x-values have to be positive.
`...`	other arguments passed to function linp from package`limSolve`.

Solves the following inverse problem:

\min(∑ {Cost_i*x_i})

\max(∑ {Profit_i*x_i})

subject to

x_i>=0

Ax=B

Gx>=H

and where Cost_i or Profit_i are weighting coefficients

a list containing:

`X`	vector containing the solution of the linear programming problem.
`unconstrained.solution`	vector containing the unconstrained solution of the linear programming problem.
`residualNorm`	scalar, the sum of residuals of equalities and violated inequalities.
`solutionNorm`	scalar, the value of the quadratic function at the solution.
`IsError`	logical, `TRUE` if an error occurred.
`Error`	linp error text.
`type`	linp.

Karline Soetaert <karline.soetaert@nioz.nl>

Michel Berkelaar and others (2005). lpSolve: Interface to Lpsolve v. 5 to solve linear/integer programs. R package version 1.1.9.

linp, the more general function from package lpSolve

Ldei, to solve the linear inverse problem by least distance programming

Lsei, to solve the linear inverse problem by lsei (least squares with equality and inequality constraints)

function linp from packagelimSolve

  # the Blending example
  Linp(LIMBlending)

  # the E coli example: two functions to maximimise
  Linp(LIMEcoli)
 
  # E coli example, but only first function optimised..
  Linp(LIMEcoli, cost = -LIMEcoli$Profit[1,])

  # a foodweb example: need to specify the cost function
  # here just sum of absolute values of flows...
  Linp(LIMRigaAutumn, cost = (rep(1, LIMRigaAutumn$NUnknowns)))