Files and links (1)
Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
MINIMIZER EXTRACTION IN POLYNOMIAL OPTIMIZATION IS ROBUST
SIAM journal on optimization, v 28(4), pp 3177-3207
01 Jan 2018
Abstract
In this article we present a robustness analysis of the extraction of optimizers in polynomial optimization. Optimizers can be extracted by solving moment problems using flatness and the Gelfand-Naimark-Segal (GNS) construction. Here a modification of the GNS construction is presented that applies even to nonflat data, and then its sensitivity under perturbations is studied. The focus is on eigenvalue optimization for noncommutative polynomials, but we also explain how the main results pertain to commutative and tracial optimization.
Metrics
Details
- Title
- MINIMIZER EXTRACTION IN POLYNOMIAL OPTIMIZATION IS ROBUST
- Creators
- Igor Klep - University of AucklandJanez Povh - University of LjubljanaJurij Volcic - Ben-Gurion University of the Negev
- Publication Details
- SIAM journal on optimization, v 28(4), pp 3177-3207
- Publisher
- Siam Publications
- Number of pages
- 31
- Grant note
- University of Auckland Doctoral Scholarship SCHW 1723/1-1 / Deutsche Forschungsgemeinschaft (DFG); German Research Foundation (DFG) Marsden Fund Council of the Royal Society of New Zealand; Royal Society of New Zealand; Marsden Fund (NZ) J1-8132; N1-0057; P1-0222 / Slovenian Research Agency; Slovenian Research Agency - Slovenia
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000453750000017
- Scopus ID
- 2-s2.0-85060290228
- Other Identifier
- 991021861880504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematics, Applied