MINTO - Mixed INTeger Optimizer

Introduction

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.

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Using MINTO with AMPL

MINTO has also recently been equipped with the ability to act as a solver directly through the AMPL modeling language. Here is a table of minto options that can be set through AMPL. This solver is provided as a public service through NEOS.

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MINTO Availability

MINTO is available from this website (CORAL). By downloading software from this site, you agree to the terms of the license agreement, created by the Georgia Tech Research Institute. Please read it before downloading the software.

View the License Agreement and Download the Software

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MINTO in Coral

In CORAL, Minto is installed in /usr/local. How to use and build MINTO is described in Coral-Wiki here.

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Relevant publications

A. Atamturk, G.L. Nemhauser, M.W.P. Savelsbergh (2000). Conflict Graphs in Integer Programming. European Journal of Operations Research 121, 40-55.

Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999). Cover Inequalities for 0-1 Linear Programs: Complexity. INFORMS J. on Computing 11, 117-123.

J. Linderoth, M.W.P. Savelsbergh (1999). A Computational Study of Search Strategies for Mixed Integer Programming. INFORMS J. on Computing 11, 173-187.

Z. Gu, G.L. Nemhauser, and M.W.P. Savelsbergh (1999). Lifted Flow Covers for Mixed 0-1 Integer Programs. Mathematical Programming 85, 439-468.

Gu, G.L., Nemhauser, and M.W.P. Savelsbergh (1998). Cover Inequalities for 0-1 Linear Programs: Computation. INFORMS Journal on Computing 10, 427-437.

G.L. Nemhauser, M.W.P. Savelsbergh, G.S. Sigismondi (1994). MINTO, a Mixed INTeger Optimizer. Oper. Res. Letters 15, 47-58.

M.W.P. Savelsbergh (1994). Preprocessing and Probing for Mixed Integer Programming Problems. ORSA J. on Computing 6, 445-454.

M.W.P. Savelsbergh, G.L. Nemhauser (1998). Functional description of MINTO, a Mixed INTeger Optimizer (Version 3.0). Report COC-91-03D, Georgia Institute of Technology.

M.W.P. Savelsbergh, G.L. Nemhauser (1995). A MINTO short course. Report COC-95-xx, Georgia Institute of Technology.