Semidefinite Programing (SDP): Difference between revisions
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Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function | 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]]. | ||
<B>Context:</B> | |||
** It is an optimization problem of the form: Minimize <math>C\dot X \\ </math> such that <math>A_i X=b_i,i=1,\dots,m \\ X \geq 0</math> | |||
Revision as of 12:59, 11 January 2016
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. Context:
- It is an optimization problem of the form: Minimize [math]\displaystyle{ C\dot X \\ }[/math] such that [math]\displaystyle{ A_i X=b_i,i=1,\dots,m \\ X \geq 0 }[/math]