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Abstract
The Supporting Hyperplane Optimization Toolkit (SHOT) is an optimization solver based on polyhedral outer approximation, primarily designed for convex mixed-integer nonlinear programming (MINLP) problems. Nonconvexities are handled through reformulations and heuristic techniques, and thus, the solver is positioned somewhere between a global and heuristic solver for nonconvex problems. This paper introduces two heuristic enhancements to improve SHOT’s nonconvex capabilities, primarily by reducing the optimality gap. The first technique involves solving a convex bounding problem to improve the dual bound, and the second updates an existing primal objective cut heuristic within SHOT to improve the primal bound.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Stockholm Global Optimization Workshop, STOGO 2025 |
| Pages | 133-136 |
| Number of pages | 4 |
| Publication status | Published - 2 Sept 2025 |
| MoE publication type | A4 Article in a conference publication |
| Event | Stockholm Global Optimization Workshop 2025 - Duration: 2 Sept 2025 → 5 Sept 2025 https://sites.google.com/view/stogo25/ |
Workshop
| Workshop | Stockholm Global Optimization Workshop 2025 |
|---|---|
| Abbreviated title | STOGO |
| Period | 02/09/25 → 05/09/25 |
| Internet address |
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Dive into the research topics of 'Improved Bounding Techniques for Nonconvex MINLP in SHOT'. Together they form a unique fingerprint.Projects
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Algorithms and Software for Large-Scale Optimization
Lundell, A. (Principal Investigator), Björkqvist, J. (Co-Investigator), Olama, A. (Participant) & Blomqvist, J. (Participant)
01/09/23 → 30/06/26
Project: Foundation