Convex Optimization - Home

Description: Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization problem can be transformed to a convex equivalent, then this interpretive benefit is acquired. That is a powerful attraction: the ability to visualize geometry of an optimization problem. Conversely, recent advances in geometry hold convex optimization within their proofs' core. This book is about convex optimization, convex geometry (with particular attention to distance geometry), geometrical problems, and problems that can be transformed into geometrical problems. Euclidean distance geometry is, fundamentally, a determination of point conformation from interpoint distance information; e.g., given only distance information, determine whether there corresponds a realizable configuration of points; a list of points in some dimension that attains the given interpoint distances., Expert information on convex optimization expert calculus of inequalities

Keywords: Convex Optimization, Convex Optimization Group, Calculus of Inequalities, Conic Independence, Convex Cones, Convex Geometry, Convex, Affine, Conic Hulls, Convex Functions, Distance Geometry, Distance Matrix Cone, Dual Cones, Euclidean Distance Matrices, Extreme Directions, Eigenvalues/Eigenvectors, Face Recognition, Kissing Number, Linear Algebra, Matrix Calculus, Manifold Learning, Molecular Conformation, Positive Semidefinite Cone Projection, Quasiconvex Functions, Rank Constraint, Semidefinite Programming, Schoenberg Criterion, Sensor Network Localization, Optimization News, Convex Optimization Book, Dattorro convex optimization distance geometry convex cone rank constraint

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Convexoptimization.com

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