On branching rules for convex mixed-integer nonlinear optimization-Reference-Cited by-同舟云学术

On branching rules for convex mixed-integer nonlinear optimization

Published:2013-12 Issue: Volume:18 Page:
ISSN:1084-6654
Container-title:ACM Journal of Experimental Algorithmics
language:en
Short-container-title:ACM J. Exp. Algorithmics

Author:

Bonami Pierre¹,Lee Jon²,Leyffer Sven³,Wächter Andreas⁴

Affiliation:

1. CNRS, Université Aix Marseille, Marseille, France

2. University of Michigan, MI, USA

3. Argonne National Laboratory, IL, USA

4. Northwestern University, Evanston, IL, USA

Abstract

Branch-and-Bound (B&B) is perhaps the most fundamental algorithm for the global solution of convex Mixed-Integer Nonlinear Programming (MINLP) problems. It is well-known that carrying out branching in a nonsimplistic manner can greatly enhance the practicality of B&B in the context of Mixed-Integer Linear Programming (MILP). No detailed study of branching has heretofore been carried out for MINLP. In this article, we study and identify useful sophisticated branching methods for MINLP, including novel approaches based on approximations of the nonlinear relaxations by linear and quadratic programs.

Funder

U.S. Department of Energy

Directorate for Computer and Information Science and Engineering

Google

Division of Civil, Mechanical and Manufacturing Innovation

Agence Nationale de la Recherche

Advanced Scientific Computing Research

Division of Mathematical Sciences

Office of Science

Publisher

Association for Computing Machinery (ACM)

Subject

Theoretical Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/2532568

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