Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified...

(1997). "An exact duality theory for semidefinite programming and its complexity implications". Mathematical Programming. 77: 129–162. doi:10.1007/BF02614433...

point methods and in general, can be solved more efficiently than semidefinite programming (SDP) problems. Some engineering applications of SOCP include filter...

known classes of convex optimization problems, namely linear and semidefinite programming. Given a real vector space X, a convex, real-valued function f...

(2019-02-04). "Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs". Mathematical Programming. 181: 1–17. arXiv:1802...

Unfolding (MVU), also known as Semidefinite Embedding (SDE), is an algorithm in computer science that uses semidefinite programming to perform non-linear dimensionality...

a convex quadratic function. Second order cone programming are more general. Semidefinite programming are more general. Conic optimization are even more...

channels. Although the diamond norm can be efficiently computed via semidefinite programming, it is in general difficult to obtain analytical expressions and...

{x} ^{\top }N\mathbf {x} \geq 0~.} This property guarantees that semidefinite programming problems converge to a globally optimal solution. The positive-definiteness...

Goemans, Michel X. (1997-10-01). "Semidefinite programming in combinatorial optimization". Mathematical Programming. 79 (1): 143–161. doi:10.1007/BF02614315...

penalized matrix decomposition framework, a convex relaxation/semidefinite programming framework, a generalized power method framework an alternating...

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...

for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization. The goal of supervised learning...

stopping problems Oriented matroid Quadratic programming, a superset of linear programming Semidefinite programming Shadow price Simplex algorithm, used to...

L. E.; Jordan, M. I. (2004). "Learning the kernel matrix with semidefinite programming". Journal of Machine Learning Research. 5: 27–72 [p. 29]. Horn...

optimization is also known as the Lasserre hierarchy of relaxations in semidefinite programming. Sum-of-squares optimization techniques have been applied across...

approximation ratio is a method by Goemans and Williamson using semidefinite programming and randomized rounding that achieves an approximation ratio α...

approximation ratio using semidefinite programming. Note that min-cut and max-cut are not dual problems in the linear programming sense, even though one...

technique for casting this problem as a semidefinite programming problem. Unfortunately, semidefinite programming solvers have a high computational cost...

Ramana, Motakuri; Goldman, A. J. (1995), "Some geometric results in semidefinite programming", Journal of Global Optimization, 7 (1): 33–50, CiteSeerX 10.1...

optimization problems, and the first to make a systematic study of semidefinite programming (SDP). Also in this book, they introduced the self-concordant functions...

Nemirovski. Semidefinite programming Spectrahedron Finsler's lemma Y. Nesterov and A. Nemirovsky, Interior Point Polynomial Methods in Convex Programming. SIAM...

popular relaxations include the following. Linear programming relaxations Semidefinite programming relaxations Primal-dual methods Dual fitting Embedding...

approximations to this number can be computed in polynomial time by semidefinite programming and the ellipsoid method. The Lovász number of the complement of...

conic quadratic (a.k.a. Second-order cone programming) and semi-definite (aka. semidefinite programming), which the software is considerably efficient...

(optimization) Semidefinite programming Relaxation (approximation) Gärtner, Bernd; Matoušek, Jiří (2006). Understanding and Using Linear Programming. Berlin:...

Mittelmann, Hans D.; Vallentin, Frank (2010). "High accuracy semidefinite programming bounds for kissing numbers". Experimental Mathematics. 19 (2):...

journal requires |journal= (help) So, Anthony Man-Cho (2007). A Semidefinite Programming Approach to the Graph Realization Problem: Theory, Applications...

problem the best approximation ratio is given by a certain simple semidefinite programming instance, which is in particular polynomial. In 2010, Prasad Raghavendra...

in polynomial-time, it follows that QRG ⊆ EXP. Min-max theorem Semidefinite programming QIP (complexity) Gutoski, G; Watrous J (2007). "Toward a general...