About: Semidefinite programming

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Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize)over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

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rdf:type	disease
rdfs:label	Semidefinite programming (en)
rdfs:comment	Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize)over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. (en)
sameAs	Semidefinite programming Semidefinite programming
dbp:wikiPageUsesTemplate	dbt:ISBN dbt:Reflist dbt:Mathematical_optimization_software dbt:Optimization_algorithms
Subject	Convex optimization P-complete problems Real algebraic geometry Linear programming
gold:hypernym	Subfield
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Semidefinite_programming?oldid=1074186079&ns=0
Wikipage page ID	4993539 (xsd:integer)
page length (characters) of wiki page	22077 (xsd:nonNegativeInteger)
Wikipage revision ID	1074186079 (xsd:integer)
has abstract	Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize)over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. 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, SDPs 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.All linear programs and (convex) quadratic programs can be expressed as SDPs, and via hierarchies of SDPs the solutions of polynomial optimization problems can be approximated. Semidefinite programming has been used in the optimization of complex systems. In recent years, some quantum query complexity problems have been formulated in terms of semidefinite programs. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Semidefinite_programming
is Wikipage redirect of	Algorithms for semidefinite programming Semi-definite programming Semidefinite program Semidefinite programs
is Link from a Wikipage to another Wikipage of	Frankl–Rödl graph Grothendieck inequality List of terms relating to algorithms and data structures Nl (format) 2-satisfiability AMPL Algorithms for semidefinite programming Analysis of Boolean functions Approximation algorithm Arkadi Nemirovski Avner Magen Betweenness Geometric median Geometric rigidity David P. Williamson Diamond norm Dimensionality reduction Octeract Engine Chebyshev's inequality Christine Bachoc Cut (graph theory) Definite matrix Hyperparameter optimization Kim-Chuan Toh Linear matrix inequality Linear programming List of numerical analysis topics Clique problem Computational hardness assumption Conic optimization Convex cone Convex optimization Michel Goemans Min-entropy Fulkerson Prize Gadget (computer science) Graph coloring Kazhdan's property (T) Extension complexity Joint spectral radius Euclidean distance matrix Lovász number Large margin nearest neighbor MOSEK Mathematical optimization Matrix completion Maximum cut Multiplicative weight update method Naum Z. Shor
is academic discipline of	Yurii Nesterov

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