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The Dual-Path Fixing Strategy and Its Application to the Set-Covering Problem

Authors Paulo Michel F. Yamagishi , Marcia Fampa , Jon Lee

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LIPIcs.SEA.2026.28.pdf
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Subject Classification

ACM Subject Classification
  • Applied computing → Operations research
Keywords
  • integer programming
  • mixed-integer programming
  • variable fixing
  • reduced-cost fixing
  • strong fixing
  • dual-path fixing
  • set cover

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Abstract

We introduce the dual-path fixing strategy to exploit dual algorithms for solving relaxations of mixed-integer nonlinear-optimization problems. Such dual algorithms are naturally applied in the context of branch-and-bound, and eventual impact on the success of branch-and-bound is our strong motivation. Our fixing strategy aims to be more powerful than the common strategy of fixing variables based on a single dual-feasible solution (e.g., standard reduced-cost fixing for mixed-integer linear optimization), but to be much faster than "strong fixing", essentially requiring no more time than that of the dual algorithm that we exploit. We have successfully tested our ideas on mixed-integer linear-optimization set-covering instances from the literature, in the context of the dual-simplex method applied to the continuous relaxations.

Cite As Get BibTex

Paulo Michel F. Yamagishi, Marcia Fampa, and Jon Lee. The Dual-Path Fixing Strategy and Its Application to the Set-Covering Problem. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 28:1-28:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.SEA.2026.28

Author Details

Paulo Michel F. Yamagishi
  • Universidade Federal do Rio de Janeiro, Brasil
Marcia Fampa
  • Universidade Federal do Rio de Janeiro, Brasil
Jon Lee
  • University of Michigan, Ann Arbor, MI, USA

Funding

  • Fampa, Marcia: partially funded by CNPq grant 307167/2022-4.
  • Lee, Jon: Partially funded by U.S. ONR grant N00014-24-1-2694.

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