Optimization over polynomials: Selected topics

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Minimizing a polynomial function over a region defined by polynomial inequalities
models broad classes of hard problems from combinatorics, geometry and optimization.
New algorithmic approaches have emerged recently for computing the global
minimum, by combining tools from real algebra (sums of squares of polynomials) and functional
analysis (moments of measures) with semidefinite optimization. Sums of squares
are used to certify positive polynomials, combining an old idea of Hilbert with the recent algorithmic insight that they can be checked efficiently with semidefinite optimization. The dual approach revisits the classical moment problem and leads to algorithmic methods for checking optimality of semidefinite relaxations and extracting global minimizers. We review some selected features of this general methodology, illustrate how it applies to
some combinatorial graph problems, and discuss links with other relaxation methods.
Original languageEnglish
Title of host publicationProceedings of the International Congress of Mathematicians 2014 (ICM 2014)
EditorsSun Young Jang, Young Rock Kim, Dae-Woong Lee, Ikkwon Yie
Place of PublicationSeoul
PublisherKyung Moon SA Co. Ltd.
ISBN (Print)9788961058070
Publication statusPublished - 2014
EventInternational Congress of Mathematicians 2014 - Seoul, Korea, Republic of
Duration: 13 Aug 201421 Aug 2014


ConferenceInternational Congress of Mathematicians 2014
Abbreviated titleICM 2014
Country/TerritoryKorea, Republic of


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