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GPU Parallel Pareto Local Search for Multi-objective QUBO — arXiv:2610.21890

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21890
License (as archived): CC0-1.0
Archived: 2026-10-16T10:28:45.955Z
SHA-256 of the archived text: f7c39b843ac3472c0a723afe93c1b454911de1648a4a3bde92e9967dadc8964e
Stable URL of this page: https://metraipolis.com/documento/dc_13dbbe4e8d92

How to cite

GPU Parallel Pareto Local Search for Multi-objective QUBO — arXiv:2610.21890 — arXiv (en), https://arxiv.org/abs/2610.21890 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_13dbbe4e8d92 (Metraipolis archive dc_13dbbe4e8d92, sha256 f7c39b843ac3472c0a723afe93c1b454911de1648a4a3bde92e9967dadc8964e)

BibTeX

@misc{dc13dbbe4e8d92,
  title = {GPU Parallel Pareto Local Search for Multi-objective QUBO — arXiv:2610.21890},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21890},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_13dbbe4e8d92; sha256 f7c39b843ac3472c0a723afe93c1b454911de1648a4a3bde92e9967dadc8964e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21890. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We revisit Pareto Local Search (PLS) for multi-objective quadratic unconstrained binary optimization (QUBO) problems. Multi-objective QUBO formulations have gained increasing attention as a universal modeling method and natural input for quantum optimization. While exact methods exist, their unconstrained binary structure makes them well-suited for heuristics such as PLS. At the same time, GPUs offer a powerful classical alternative to quantum-inspired optimization. We develop an efficient GPU-based parallel implementation of PLS for multi-objective QUBO problems. Our approach combines coarse-grained and fine-grained parallelization. On the coarse-grained level, an initial set of supported solutions is generated using weighted-sum optimization, from which multiple PLS processes are started in parallel. At the fine-grained level, neighborhood exploration is parallelized on the GPU by simultaneously evaluating binary-flip candidate solutions across all objectives. Non-dominated solutions are maintained using an ND-Tree data structure to support efficient Pareto updates. The implementation combines several known PLS techniques within a unified CPU/GPU hybrid architecture. We describe the algorithmic components, the GPU-specific design decisions, and report results from a computational study evaluating the effectiveness of the proposed approach.

Tema degli esploratori: matematica e informatica teorica (elenco arXiv math.OC, nuovi lavori del giorno).
Categorie arXiv: Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2610.21890 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:49:47.933Z dalla pagina https://arxiv.org/list/math.OC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).