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Sammi Brie (talk | contribs) Importing Wikidata short description: "Subfield of convex optimization" |
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{{Short description|Subfield of convex optimization}}
'''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 (linear algebra)|cone]] of [[Positive-definite matrix#Negative-definite, semidefinite and indefinite matrices|positive semidefinite]] [[Matrix (mathematics)|matrices]] with an [[affine space]], i.e., a [[spectrahedron]].<ref>{{Citation |last=Gärtner |first=Bernd |title=Semidefinite Programming |date=2012 |url=https://doi.org/10.1007/978-3-642-22015-9_2 |work=Approximation Algorithms and Semidefinite Programming |pages=15–25 |editor-last=Gärtner |editor-first=Bernd |access-date=2023-12-31 |place=Berlin, Heidelberg |publisher=Springer |language=en |doi=10.1007/978-3-642-22015-9_2 |isbn=978-3-642-22015-9 |last2=Matoušek |first2=Jiří |editor2-last=Matousek |editor2-first=Jiri}}</ref>
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 inequality|linear matrix inequalities]]. SDPs are in fact a special case of [[conic optimization|cone programming]] and can be efficiently solved by [[interior point methods]].
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