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

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

a semidefinite programming problem. Unfortunately, semidefinite programming solvers have a high computational cost. Like Locally Linear Embedding, it...

Random projection Sammon mapping Semantic mapping (statistics) Semidefinite embedding Singular value decomposition Sufficient dimension reduction Topological...

that an order embedding between partially ordered sets is injective. Clearly, every isometry between metric spaces is a topological embedding. A global isometry...

Cayley–Menger determinant Semidefinite embedding Dokmanic et al. (2015) So (2007) Maehara, Hiroshi (2013). "Euclidean embeddings of finite metric spaces"...

Definitizable Functions, Akademie Verlag, 1994 Wells, J. H.; Williams, L. R. Embeddings and extensions in analysis. Ergebnisse der Mathematik und ihrer Grenzgebiete...

Self-Service Semantic Suite Semantic folding Semantic mapping (statistics) Semidefinite embedding Sense Networks Sensorium Project Sequence labeling Sequential minimal...

definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors...

(-1)^{k+1}\operatorname {CM} (P_{0},\ldots ,P_{k})\geq 0,} then such an embedding exists. Further, such embedding is unique up to isometry in R n {\displaystyle \mathbb...

relatedness Semantic similarity Semi-Markov process Semi-log graph Semidefinite embedding Semimartingale Semiparametric model Semiparametric regression Semivariance...

widely used low-dimensional embedding methods. Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional...

and hence is convex. The second-order cone can be embedded in the cone of the positive semidefinite matrices since | | x | | ≤ t ⇔ [ t I x x T t ] ≽ 0...

embedded manifold in some C n {\displaystyle \mathbb {C} ^{n}} . Thus not only are we embedding the manifold, but we also demand for global embedding...

(denatured protein), in biochemistry Maximum variance unfolding (semidefinite embedding), in computer science Unfold (Marié Digby album), 2008 Unfold (John...

guarantees, one way is to formulate the problems as a semidefinite program (SDP), by embedding the problem in a higher dimensional space using the transformation...

parameters can be defined and studied, such as the minimum rank, minimum semidefinite rank and minimum skew rank. van der Holst, Lovász & Schrijver (1999)...

been shown to be equivalent to Connes' embedding problem, so the same proof also implies that the Connes embedding problem is false. Quantum nonlocality...

it can be approximated to within a constant approximation ratio using semidefinite programming. Note that min-cut and max-cut are not dual problems in the...

holds: − ω {\displaystyle -\omega } is the imaginary part of a positive semidefinite (respectively, positive definite) Hermitian form. For some basis d z...

show that this sesquilinear form is in fact positive semidefinite. Since positive semidefinite Hermitian sesquilinear forms satisfy the Cauchy–Schwarz...

features. JuMP supports linear programming, mixed integer programming, semidefinite programming, conic optimization, nonlinear programming, and other classes...

graphs. He proved with his coauthors essentially that a huge class of semidefinite programming algorithms for the famous vertex cover problem will not achieve...

vertices of the embedding are required to be on the line, which is called the spine of the embedding, and the edges of the embedding are required to lie...

polar decomposition A = UP with a unitary matrix U and some positive semidefinite matrix P. A commutes with some normal matrix N with distinct[clarification...

boxes is equivalent to characterizing the cone of completely positive semidefinite matrices under a set of linear constraints. For small fixed dimensions...

following. Linear programming relaxations Semidefinite programming relaxations Primal-dual methods Dual fitting Embedding the problem in some metric and then...

{1}{2}}\left(1\pm |{\vec {a}}|\right)} . Density operators must be positive-semidefinite, so it follows that | a → | ≤ 1 {\displaystyle \left|{\vec {a}}\right|\leq...

solve graph homomorphism inequalities with computers by reducing them to semidefinite programming problems. Originally introduced by Alexander Razborov in...

with a strong embedding on a surface, the face coloring is the dual of the vertex coloring problem. For a graph G with a strong embedding on an orientable...