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...

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...

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

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...

^{\mathsf {T}}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...

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

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...

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 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...

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

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

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

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

proposed, including a regression framework, a convex relaxation/semidefinite programming framework, a generalized power method framework an alternating...

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

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

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

coloring of perfect graphs can be computed in polynomial time using semidefinite programming. Closed formulas for chromatic polynomials are known for many classes...

constraints Mixed-integer nonlinear programming Second-order cone programming Global optimization Semidefinite programming problems with bilinear matrix inequalities...