Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified...
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Quantum optimization algorithms (redirect from Quantum semidefinite programming)
(1997). "An exact duality theory for semidefinite programming and its complexity implications". Mathematical Programming. 77: 129–162. doi:10.1007/BF02614433...
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SOCPs by reformulating the objective function as a constraint. Semidefinite programming subsumes SOCPs as the SOCP constraints can be written as linear...
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Conic optimization (redirect from Conic programming)
known classes of convex optimization problems, namely linear and semidefinite programming. Given a real vector space X, a convex, real-valued function f...
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channels. Although the diamond norm can be efficiently computed via semidefinite programming, it is in general difficult to obtain analytical expressions and...
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(2019-02-04). "Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs". Mathematical Programming. 181: 1–17. arXiv:1802...
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Goemans, Michel X. (1997-10-01). "Semidefinite programming in combinatorial optimization". Mathematical Programming. 79 (1): 143–161. doi:10.1007/BF02614315...
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Unfolding (MVU), also known as Semidefinite Embedding (SDE), is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality...
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Convex optimization (redirect from Convex programming)
a convex quadratic function. Second order cone programming are more general. Semidefinite programming are more general. Conic optimization are even more...
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penalized matrix decomposition framework, a convex relaxation/semidefinite programming framework, a generalized power method framework an alternating...
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Definite matrix (redirect from Positive-semidefinite matrix)
{x} ^{\top }N\mathbf {x} \geq 0~.} This property guarantees that semidefinite programming problems converge to a globally optimal solution. The positive-definiteness...
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Interior-point method (section Semidefinite programs)
O((k+m)1/2[mk2+k3+n3]). Interior point methods can be used to solve semidefinite programs.: Sec.11 Affine scaling Augmented Lagrangian method Chambolle-Pock...
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stopping problems Oriented matroid Quadratic programming, a superset of linear programming Semidefinite programming Shadow price Simplex algorithm, used to...
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a linear matrix inequality. Alternatively, the set of n × n positive semidefinite matrices forms a convex cone in Rn × n, and a spectrahedron is a shape...
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approximation ratio is a method by Goemans and Williamson using semidefinite programming and randomized rounding that achieves an approximation ratio α...
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optimization is also known as the Lasserre hierarchy of relaxations in semidefinite programming. Sum-of-squares optimization techniques have been applied across...
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Gram matrix (section Positive-semidefiniteness)
L. E.; Jordan, M. I. (2004). "Learning the kernel matrix with semidefinite programming". Journal of Machine Learning Research. 5: 27–72 [p. 29]. Horn...
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for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization. The goal of supervised learning...
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technique for casting this problem as a semidefinite programming problem. Unfortunately, semidefinite programming solvers have a high computational cost...
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approximation ratio using semidefinite programming. Note that min-cut and max-cut are not dual problems in the linear programming sense, even though one...
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Mittelmann, Hans D.; Vallentin, Frank (2010). "High accuracy semidefinite programming bounds for kissing numbers". Experimental Mathematics. 19 (2):...
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popular relaxations include the following. Linear programming relaxations Semidefinite programming relaxations Primal-dual methods Dual fitting Embedding...
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approximations to this number can be computed in polynomial time by semidefinite programming and the ellipsoid method. The Lovász number of the complement of...
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Nemirovski. Semidefinite programming Spectrahedron Finsler's lemma Y. Nesterov and A. Nemirovsky, Interior Point Polynomial Methods in Convex Programming. SIAM...
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in polynomial-time, it follows that QRG ⊆ EXP. Min-max theorem Semidefinite programming QIP (complexity) Gutoski, G; Watrous J (2007). "Toward a general...
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problem the best approximation ratio is given by a certain simple semidefinite programming instance, which is in particular polynomial. In 2010, Prasad Raghavendra...
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Mathematical optimization (redirect from Mathematical programming)
Second-order cone programming (SOCP) is a convex program, and includes certain types of quadratic programs. Semidefinite programming (SDP) is a subfield...
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maximum clique in polynomial time, using an algorithm based on semidefinite programming. However, this method is complex and non-combinatorial, and specialized...
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journal requires |journal= (help) So, Anthony Man-Cho (2007). A Semidefinite Programming Approach to the Graph Realization Problem: Theory, Applications...
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optimization problems, and the first to make a systematic study of semidefinite programming (SDP). Also in this book, they introduced the self-concordant functions...
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