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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.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Gioni Mexi,
  • Mathieu Besançon,
  • Suresh Bolusani,
  • Antonia Chmiela,
  • Alexander Hoen,
  • Ambros Gleixner

摘要

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.