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...

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

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

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

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

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

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

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

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...

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

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

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

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

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

bounds on it, based on some given operator expectation values using semidefinite programming. The approach considers an optimizaton on the two-copy space. There...

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

Fulkerson Prize for joint work with David P. Williamson on the semidefinite programming approximation algorithm for the maximum cut problem. In 2012 Goemans...

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...

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

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

IEEE. pp. 70–72. arXiv:1111.4144. So, Anthony Man-Cho (2007). A Semidefinite Programming Approach to the Graph Realization Problem: Theory, Applications...

Second-order cone programming (SOCP) is a convex program, and includes certain types of quadratic programs. Semidefinite programming (SDP) is a subfield...

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

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

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

The solution method for semidefinite programs, used by this algorithm, is based on the ellipsoid method for linear programming. It leads to a polynomial...

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

maximum clique in polynomial time, using an algorithm based on semidefinite programming. However, this method is complex and non-combinatorial, and specialized...

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