by Stephen Boyd, Lieven V
http://www.stanford.edu/~boyd/reports/sdp_relaxations.ps
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Abstract:
We point out some connections between applications of semidefinite programming in control and in combinatorial optimization. In both fields semidefinite programs arise as convex relaxations of NP-hard quadratic optimization problems. We also show that these relaxations are readily extended to optimization problems over bilinear matrix inequalities. 1 SEMIDEFINITE PROGRAMMING In a semidefinite program (SDP) we minimize a linear function of a variable x 2 R
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