mit-drl / goop

Generalized Mixed Integer Optimization in Go

Goop

General Linear Optimization in Go. goop provides general interface for solving mixed integer linear optimization problems using a variety of back-end solvers including LPSolve and Gurobi.

Quickstart

We are going to start with a simple example showing how goop can be used to solve integer linear programs. The example below seeks to maximize the following MIP:

maximize    x +   y + 2 z
subject to  x + 2 y + 3 z <= 4
            x +   y       >= 1
x, y, z binary

This is is the same example implemented here. Below we have implemented the model using goop and have optimized the model using the supported Gurobi solver.

package main

import (
    "fmt"
    "github.com/mit-drl/goop"
    "github.com/mit-drl/goop/solvers"
)

func main() {
    // Instantiate a new model
    m := goop.NewModel()

    // Add your variables to the model
    x := m.AddBinaryVar()
    y := m.AddBinaryVar()
    z := m.AddBinaryVar()

    // Add your constraints
    m.AddConstr(goop.Sum(x, y.Mult(2), z.Mult(3)).LessEq(goop.K(4)))
    m.AddConstr(goop.Sum(x, y).GreaterEq(goop.One))

    // Set a linear objective using your variables
    obj := goop.Sum(x, y, z.Mult(2))
    m.SetObjective(obj, goop.SenseMaximize)

    // Optimize the variables according to the model
    sol, err := m.Optimize(solvers.NewGurobiSolver())

    // Check if there is an error from the solver. No error should be returned
    // for this model
    if err != nil {
    	panic("Should not have an error")
    }

    // Print out the solution
    fmt.Println("x =", sol.Value(x))
    fmt.Println("y =", sol.Value(y))
    fmt.Println("z =", sol.Value(z))

    // Output:
    // x = 1
    // y = 0
    // z = 1
}

Installation

1. First get the code

mkdir -p $GOPATH/github.com/mit-drl && cd $GOPATH/github.com/mit-drl
git clone https://github.com/mit-drl/goop && cd goop

2. Next build install the dependencies

./install.sh

3. Follow the [instructions](#Solver Notes) for your solver of choice. Note, currently only Gurobi is supported

4. Finally build the library

go build

Note that due to a quirk with Gurobi, if you are using Ubuntu < 16.04, you must build with

go build -tags pre_xenial

5. (Optional) Test our installation

govendor test -v +local

Solver Notes

We currently have bindings for Gurobi and LPSolve. Please follow the instructions below for using these specific solvers.

Gurobi

• You must have Gurobi installed and have a valid license.
• The GUROBI_HOME environment variable must be set to the home directory of your Gurobi installation

LPSolve

LPSolve is installed using the normal install procedure and should work out of the box.