LPD: Definition of Linear Program
In TSSP: Two-Stage Stochastic Programs

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

This function creates an object of class "CLPD", by which a deterministic program is defined.

1	LPD(obj, A, dir, rhs, bounds = NULL, max = FALSE)

`obj`	`vector`: Coefficients in objective function.
`A`	`matrix`: The lhs-matrix of the constraints.
`dir`	`vector`: The relational operators in the constraints. Allowed are `"<="`, `"=="` or `">="`.
`rhs`	`vector`: The rhs-values of the constraints.
`bounds`	`NULL` or `list`: The lower and upper bounds of the variables. If left `NULL`, the default values of zero and infinity are used, otherwise a `list`-object as expected in `Rglpk_solve_LP()` must be provided.
`max`	`logical`: Whether the objective should be minimized (the default) or maximized.

An object of S4-class "CLPD".

Bernhard Pfaff

Birge, J. R. and Louveaux, F., Introduction to Stochastic Programming, Springer Series in Operations Research and Financial Engineering, Second Edition, 2004, New York: Springer.

CLPD

##
## Birge & Louveaux
## Chapter 5, Section 1a, master-problem
##
obj <- c(100, 150)
A <- matrix(c(1, 1,
              1, 0,
              0, 1),
            ncol = 2, nrow = 3, byrow = TRUE)
dir <- c("<=", ">=", ">=")
rhs <- c(120, 40, 20)
MLP <- LPD(obj, A, dir, rhs)
MLP