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Handbook on Semidefinite, Conic and Polynomial Optimization

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Cover of 'Handbook on Semidefinite, Conic and Polynomial Optimization'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Semidefinite, Conic and Polynomial Optimization
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    Chapter 2 The Approach of Moments for Polynomial Equations
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    Chapter 3 Algebraic Degree in Semidefinite and Polynomial Optimization
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    Chapter 4 Semidefinite Representation of Convex Sets and Convex Hulls
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    Chapter 5 Convex Hulls of Algebraic Sets
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    Chapter 6 Convex Relaxations and Integrality Gaps
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    Chapter 7 Relaxations of Combinatorial Problems Via Association Schemes
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    Chapter 8 Copositive Programming
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    Chapter 9 Invariant Semidefinite Programs
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    Chapter 10 A “Joint+Marginal” Approach in Optimization
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    Chapter 11 An Introduction to Formally Real Jordan Algebras and Their Applications in Optimization
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    Chapter 12 Complementarity Problems Over Symmetric Cones: A Survey of Recent Developments in Several Aspects
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    Chapter 13 Handbook on Semidefinite, Conic and Polynomial Optimization
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    Chapter 14 Positivity and Optimization: Beyond Polynomials
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    Chapter 15 Self-Regular Interior-Point Methods for Semidefinite Optimization
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    Chapter 16 Elementary Optimality Conditions for Nonlinear SDPs
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    Chapter 17 Recent Progress in Interior-Point Methods: Cutting-Plane Algorithms and Warm Starts
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    Chapter 18 Exploiting Sparsity in SDP Relaxation of Polynomial Optimization Problems
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    Chapter 19 Block Coordinate Descent Methods for Semidefinite Programming
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    Chapter 20 Projection Methods in Conic Optimization
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    Chapter 21 SDP Relaxations for Non-Commutative Polynomial Optimization
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    Chapter 22 Handbook on Semidefinite, Conic and Polynomial Optimization
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    Chapter 23 The State-of-the-Art in Conic Optimization Software
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    Chapter 24 Latest Developments in the SDPA Family for Solving Large-Scale SDPs
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    Chapter 25 On the Implementation and Usage of SDPT3 – A Matlab Software Package for Semidefinite-Quadratic-Linear Programming, Version 4.0
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    Chapter 26 PENNON: Software for Linear and Nonlinear Matrix Inequalities
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    Chapter 27 SDP Relaxations for Some Combinatorial Optimization Problems
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    Chapter 28 Computational Approaches to Max-Cut
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    Chapter 29 Global Approaches for Facility Layout and VLSI Floorplanning
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    Chapter 30 Euclidean Distance Matrices and Applications
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    Chapter 31 Sparse PCA: Convex Relaxations, Algorithms and Applications
Attention for Chapter 5: Convex Hulls of Algebraic Sets
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