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MINTO - Mixed INTeger Optimizer

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.

The heart of MINTO is a linear programming based branch-and-bound algorithm. It 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.

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.

MINTO Availability

MINTO is available for free. MINTO is maintained by a group of enthusiastic users and contributors. Go to Minto at CORAL

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.

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