MINTO

Mixed-Integer Linear Programming

MINTO is a software system that solves mixed-integer linear programs by a branch-and-bound algorithm with linear programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover, the user can enrich the basic algorithm by providing a variety of specialized application routines that can customize MINTO to achieve maximum efficiency for a problem class.

To be as effective and efficient as possible when used as a general purpose mixed-integer optimizer, MINTO attempts to:

• improve the formulation by preprocessing and probing;
• construct feasible solutions;
• generate strong valid inequalities;
• perform variable fixing based on reduced prices;
• control the size of the linear programs by managing active constraints.

To be as flexible and powerful as possible when used to build a special purpose mixed-integer optimizer, MINTO provides various mechanisms for incorporating problem specific knowledge.

System Requirements:

MINTO can be implemented on top of any LP-solver that provides capabilities to solve and modify linear programs and interpret their solutions. The current version can either be built on top of the CPLEX callable library, version 2.0 and up, or on top of the Optimization Subroutine Library (OSL), version 1.2 and up.

Need more info?

MINTO is available through the Georgia Tech Research Institute. For more information, contact Martin Savelsbergh at martin.savelsbergh@isye.gatech.edu.

Visit the MINTO homepage.

References:

A number of papers and reports concerning MINTO are available on the MINTO homepage.

[ Optimization Software Guide | OTC Home Page | NEOS Server | NEOS Guide ]