Semidefinite Programing (SDP): Difference between revisions

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Revision as of 12:48, 11 January 2016

Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function (an objective function is 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.