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- Q2269096 subject Q6426361.
- Q2269096 subject Q7013789.
- Q2269096 subject Q7016313.
- Q2269096 subject Q7413992.
- Q2269096 subject Q8707873.
- Q2269096 abstract "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.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 inequalities. SDPs are in fact a special case of cone programming and can be efficiently solved by interior point methods.All linear programs can be expressed as SDPs, and via hierarchies of SDPs the solutions of polynomial optimization problems can be approximated. Semidefinite programming has been used in the optimization of complex systems. In recent years, some quantum query complexity problems have been formulated in term of semidefinite programs.".
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- Q2269096 comment "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.Semidefinite programming is a relatively new field of optimization which is of growing interest for several reasons.".
- Q2269096 label "Semidefinite programming".