mipSolve: Solve a LP or a MIP

In lpmodeler: Modeler for linear programs (LP) and mixed integer linear programs (MILP)

Description

Solve a linear program (LP) or a mixed integer program (MIP). Find the values of the objective function and the associated variables using the specified solver.

Usage

mipSolve(p, solver = c("Rsymphony"), ...)

Arguments

p	an object of class lpmodeler
solver	name of the solver to use: Rsymphony (default)
...	other parameters passed to the solver

Value

The object returned by the solver

Author(s)

Cyrille Szymanski <cnszym at gmail.com>

See Also

TODO

Examples

# create and solve the following linear program:
# Simple mixed integer linear program.
# max:   3 x1 + 1 x2 + 3 x3
# s.t.: -1 x1 + 2 x2 +   x3 <= 4
#               4 x2 - 3 x3 <= 2
#          x1 - 3 x2 + 2 x3 <= 3
#          x1 >= 0 (integer)
#          x2 >= 0 (real)
#          x3 >= 0 (integer)
p <- newProblem()
p <- addVariable(p, "I", 3)
p <- addVariable(p, "C", 1)
p <- addVariable(p, "I", 3)
p <- addConstraint(p, "<=", 4, c(-1, 2, 1))
p <- addConstraint(p, "<=", 2, c(0, 4, -3))
p <- addConstraint(p, "<=", 3, c(1, -3, 2))
p <- addConstraint(p, ">=", 0, c(1, 0, 0))
p <- addConstraint(p, ">=", 0, c(0, 1, 0))
p <- addConstraint(p, ">=", 0, c(0, 0, 1))

if(require(Rsymphony))
  mipSolve(p)

Example output

Loading required package: Rsymphony

Warning: Trying to use multiple processors with sequential build...
Starting Preprocessing...
Preprocessing finished...
 	 constraints removed: 3

Solving...


****************************************************
* Optimal Solution Found                           *
****************************************************


Solution Found: Node 0, Level 0
Solution Cost: -26.7500000000
$solution
[1] 5.00 2.75 3.00

$objval
[1] 26.75

$status
TM_OPTIMAL_SOLUTION_FOUND 
                        0 

lpmodeler documentation built on May 2, 2019, 2:46 p.m.