Scylla: A Matrix-Free Fix-Propagate-and-Project Heuristic for Mixed-Integer Optimization

Gioni Mexi (), Mathieu Besançon (), Suresh Bolusani (), Antonia Chmiela (), Alexander Hoen () and Ambros Gleixner ()
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Gioni Mexi: Interactive Optimization and Learning, Zuse Institute Berlin
Mathieu Besançon: Interactive Optimization and Learning, Zuse Institute Berlin
Suresh Bolusani: Interactive Optimization and Learning, Zuse Institute Berlin
Antonia Chmiela: Interactive Optimization and Learning, Zuse Institute Berlin
Alexander Hoen: Interactive Optimization and Learning, Zuse Institute Berlin
Ambros Gleixner: Interactive Optimization and Learning, Zuse Institute Berlin

Chapter Chapter 9 in Operations Research Proceedings 2023, 2025, pp 65-72 from Springer

Abstract: Abstract We introduce Scylla, a primal heuristic for mixed-integer optimization problems. It exploits approximate solves of the Linear Programming relaxations through the matrix-free Primal-Dual Hybrid Gradient algorithm with specialized termination criteria, and derives integer-feasible solutions via fix-and-propagate procedures and feasibility-pump-like updates to the objective function. Computational experiments show that the method is particularly suited to instances with hard linear relaxations.

Keywords: Mixed-integer optimization; Heuristics; Matrix-free (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-58405-3_9

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