# crispLP: Solves a crisp linear programming problem. In FuzzyLP: Fuzzy Linear Programming

## Description

`crispLP` use the classic solver (simplex) to solve a crisp linear programming problem:

Max f(x) or Min f(x)

s.t.: Ax<=b

## Usage

 `1` ```crispLP(objective, A, dir, b, maximum = TRUE, verbose = TRUE) ```

## Arguments

 `objective` A vector (c1, c2, …, cn) with the objective function coefficients f(x)=c1*x1+…+cn*xn. `A` Technological matrix of Real Numbers. `dir` Vector of strings with the direction of the inequalities, of the same length as `b`. Each element of the vector must be one of "=", ">=", "<=", "<" or ">". `b` Vector with the right hand side of the constraints. `maximum` `TRUE` to maximize the objective function, `FALSE` to minimize the objective function. `verbose` `TRUE` to show aditional screen info, `FALSE` to hide aditional screen info.

## Value

`crispLP` returns the solution if the solver has found it or NULL if not.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## maximize: 3*x1 + x2 ## s.t.: 1.875*x1 - 1.5*x2 <= 4 ## 4.75*x1 + 2.125*x2 <= 14.5 ## x1, x2 are non-negative real numbers obj <- c(3, 1) A <- matrix(c(1.875, 4.75, -1.5, 2.125), nrow = 2) dir <- c("<=", "<=") b <- c(4, 14.5) max <- TRUE crispLP(obj, A, dir, b, maximum = max, verbose = TRUE) ```

FuzzyLP documentation built on April 11, 2021, 5:06 p.m.