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A hierarchy of eigencomputations for polynomial optimization on the sphere. [PDF]
Math ProgramLovitz B, Johnston N.
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A quantum-inspired classification for random mixed states. [PDF]
Sci RepSergioli G +6 more
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SIAM Review, 1996
In semidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g.
Stephen Boyd
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In semidefinite programming, one minimizes a linear function subject to the constraint that an affine combination of symmetric matrices is positive semidefinite. Such a constraint is nonlinear and nonsmooth, but convex, so semidefinite programs are convex optimization problems. Semidefinite programming unifies several standard problems (e.g.
Stephen Boyd
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On the Complexity of Semidefinite Programs
Journal of Global Optimization, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lorant Porkolab, Leonid Khachiyan
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Conditioning of semidefinite programs
Mathematical Programming, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Madhu V. Nayakkankuppam +1 more
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Semidefinite Programs and Association Schemes [PDF]
Computing, 1999 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
GOEMANS, Michel, RENDL, Franz
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European Journal of Operational Research, 2002
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Regularization Methods for Semidefinite Programming [PDF]
SIAM Journal on Optimization, 2009 We introduce a new class of algorithms for solving linear semidefinite programming (SDP) problems. Our approach is based on classical tools from convex optimization such as quadratic regularization and augmented Lagrangian techniques. We study the theoretical properties and we show that practical implementations behave very well on some instances of ...
Janez Povh, Jérôme Malick, Franz Rendl
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The Simplest Semidefinite Programs are Trivial
Mathematics of Operations Research, 1995We consider optimization problems of the following type: [Formula: see text] Here, tr(·) denotes the trace operator, C and X are symmetric n × n matrices, B is a symmetric m × m matrix and A(·) denotes a linear operator. Such problems are called semidefinite programs and have recently become the object of considerable interest due to important ...
Robert J. Vanderbei, Bing Yang
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