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Monotonic Optimization: Branch and Cut Methods

  • Hoang Tuy
  • Faiz Al-Khayyal
  • Phan Thien Thach

Abstract

Monotonic optimization is concerned with optimization problems dealing with multivariate monotonic functions and differences of monotonic functions. For the study of this class of problems a general framework (Tuy, 2000a) has been earlier developed where a key role was given to a separation property of solution sets of monotonic inequalities similar to the separation property of convex sets. In the present paper the separation cut is combined with other kinds of cuts, called reduction cuts, to further exploit the monotonic structure. Branch and cuts algorithms based on an exhaustive rectangular partition and a systematic use of cuts have proved to be much more efficient than the original polyblock and copolyblock outer approximation algorithms.

Keywords

Feasible Solution Global Optimization Separation Property Outer Approximation Approximate Optimal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Hoang Tuy
  • Faiz Al-Khayyal
  • Phan Thien Thach

There are no affiliations available

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