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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
  2. Altmetric Badge
    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 28: Computational Approaches to Max-Cut
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Chapter title
Computational Approaches to Max-Cut
Chapter number 28
Book title
Handbook on Semidefinite, Conic and Polynomial Optimization
Published by
Springer, Boston, MA, January 2012
DOI 10.1007/978-1-4614-0769-0_28
Book ISBNs
978-1-4614-0768-3, 978-1-4614-0769-0

Laura Palagi, Veronica Piccialli, Franz Rendl, Giovanni Rinaldi, Angelika Wiegele

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 18%
Researcher 2 18%
Student > Ph. D. Student 2 18%
Student > Master 1 9%
Other 1 9%
Other 2 18%
Unknown 1 9%
Readers by discipline Count As %
Computer Science 4 36%
Mathematics 3 27%
Agricultural and Biological Sciences 1 9%
Economics, Econometrics and Finance 1 9%
Engineering 1 9%
Other 0 0%
Unknown 1 9%