Skydiving to bootstrap islands
A bstract We study families of semidefinite programs (SDPs) that depend nonlinearly on a small number of “external” parameters. Such families appear universally in numerical bootstrap computations. The traditional method for finding an optimal point in parameter space works by first solving an SDP with fixed external parameters, then moving to a new point in parameter space and repeating the process. Instead, we unify solving the SDP and moving in parameter space in a single algorithm that we call “skydiving”. We test skydiving on some representative problems in the conformal bootstrap, finding significant speedups compared to traditional methods.
Authors
- Aike Liu (ORCID: https://orcid.org/0009-0009-8509-4635)
- Ning Su (ORCID: https://orcid.org/0000-0001-5559-7922)
- David Simmons–Duffin (ORCID: https://orcid.org/0000-0002-2937-9515)
- Balt C. van Rees (ORCID: https://orcid.org/0000-0003-0904-5881)
Institutions
- University of Pisa (IT)
- California Institute of Technology (US)
- Centre National de la Recherche Scientifique (FR)
- École Polytechnique (FR)
Publication Details
- Journal
- Journal of High Energy Physics
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1007/jhep11(2025)003
- Citations
- 2
- Primary Topic
- Advanced Optimization Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 5.26
Funders
- U.S. Department of Energy
- California Institute of Technology
- Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology
- Innovation, Science and Economic Development Canada
- Government of Canada
- European Commission
- Institut Périmètre de physique théorique
- Ministry of Colleges and Universities