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

## Description

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

## Usage

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

## Arguments

 `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.

## Value

An object of S4-class `"CLPD"`.

Bernhard Pfaff

## References

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`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## ## 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 ```