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Semidefinite Programming: Convex Optimization, Matrix, Affine Space, Operations Research, Combinatorial Optimization, Linear Matrix Inequality, Conic Optimization -

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2026-03-15
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High Quality Content by WIKIPEDIA articles! High Quality Content by WIKIPEDIA articles! Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space. Semidefinite programming is a relatively new field of optimization which is of growing interest for s ... Visas aprašymas

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High Quality Content by WIKIPEDIA articles! High Quality Content by WIKIPEDIA articles! Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space. Semidefinite programming is a relatively new field of optimization which is of growing interest for several reasons. Many practical problems in operations research and combinatorial optimization can be modeled or approximated as semidefinite programming problems. In automatic control theory, SDP's are used in the context of linear matrix inequalities. SDPs are in fact a special case of cone programming and can be efficiently solved by interior point methods.

Daugiau informacijos

Leidėjas OmniScriptum
Išleidimo metai 2026
Viršelio tipas Minkšti viršeliai
EAN 9786131158179
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159,21 € 244,94 €