izpis_h1_title_alt

Sparse noncommutative polynomial optimization
ID Klep, Igor (Author), ID Magron, Victor (Author), ID Povh, Janez (Author)

.pdfPDF - Presentation file, Download (560,05 KB)
MD5: 8477CFE28BCCE2817826C9C11DD3BCFC
URLURL - Source URL, Visit https://link.springer.com/article/10.1007%2Fs10107-020-01610-1#article-info This link opens in a new window

Abstract
This article focuses on optimization of polynomials in noncommuting variables, while taking into account sparsity in the input data. A converging hierarchy of semidefinite relaxations for eigenvalue and trace optimization is provided. This hierarchy is a noncommutative analogue of results due to Lasserre (SIAM J Optim 17(3):822-843, 2006) and Waki et al. (SIAM J Optim 17(1):218-242, 2006). The Gelfand-Naimark-Segal construction is applied to extract optimizers if flatness and irreducibility conditions are satisfied. Among the main techniques used are amalgamation results from operator algebra. The theoretical results are utilized to compute lower bounds on minimal eigenvalue of noncommutative polynomials from the literature.

Language:English
Keywords:noncommutative polynomial, sparsity pattern, semialgebraic set, semidefinite programming, eigenvalue optimization, trace optimization, GNS construction
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
FMF - Faculty of Mathematics and Physics
Publication status:Published
Publication version:Version of Record
Year:2021
Number of pages:Str. [1-41]
PID:20.500.12556/RUL-124550 This link opens in a new window
UDC:512.622(045)
ISSN on article:0025-5610
DOI:10.1007/s10107-020-01610-1 This link opens in a new window
COBISS.SI-ID:49537283 This link opens in a new window
Publication date in RUL:01.02.2021
Views:1069
Downloads:435
Metadata:XML DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Record is a part of a journal

Title:Mathematical programming
Shortened title:Math. program.
Publisher:North-Holland, Springer
ISSN:0025-5610
COBISS.SI-ID:5015818 This link opens in a new window

Secondary language

Language:Slovenian
Abstract:
Ta članek se osredotoča na optimizacijo polinomov v nekomutativnih spremenljivkah, ob upoštevanju redkosti v vhodnih podatkih. Najprej predstavimo konvergentno hierarhijo semidefinitnih poenostavitev za optimizacijo lastnih vrednosti in sledi. Ta hierarhija je nekomutativni analog rezultatov iz SIAM J Optim 17 (3): 822-843, 2006 in iz SIAM J Optim 17 (1): 218-242, 2006. V nadaljevanju uporabimo konstrukcijo Gelfand - Naimark - Segal za iskanje optimizatorjev, če so izpolnjeni pogoji sploščenosti in ireducibilnosti. Med glavnimi uporabljenimi tehnikami so postopki združevanja iz operaterske algebre. Rezultati so uporabni za izračun spodnjih meja minimalne lastne vrednosti nekomutativnih polinomov iz literature.

Keywords:nekomutativni polinom, redki polinomi, semialgebraična množica, semidefinitno programiranje, optimizacija lastnih vrednosti, optimizacija sledi, GNS postopek

Projects

Funder:ARRS - Slovenian Research Agency
Project number:J1-2453
Name:Matrično konveksne množice in realna algebraična geometrija

Funder:ARRS - Slovenian Research Agency
Project number:N1-0057
Name:Visoko zmogljiv reševalec za binarne kvadratične probleme

Funder:ARRS - Slovenian Research Agency
Project number:P1-0222
Name:Algebra v teoriji operatorjev in finančna matematika

Funder:Other - Other funder or multiple funders
Funding programme:Marsden Fund Council of the Royal Society of New Zealand.

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back