Advances in Convex Analysis and Global Optimization

Table of Contents

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   Book Overview
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   Chapter 1 Inner Approximation of State-Constrained Optimal Control Problems
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   Chapter 2 Nonsmooth Problems in Mathematical Diagnostics
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   Chapter 3 Deterministic Global Optimization for Protein Structure Prediction
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   Chapter 4 Some Remarks on Minimum Principles
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   Chapter 5 Transversal Hypergraphs and Families of Polyhedral Cones
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   Chapter 6 SDP Relaxations in Combinatorial Optimization from a Lagrangian Viewpoint
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   Chapter 7 Convex Analysis in the Calculus of Variations
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   Chapter 8 Global Minimization and Parameter Estimation in Computational Biology
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    Chapter 9 Lagrangian Quadratic Bounds in Polynomial Nonconvex and Boolean Models with Superfluous Constraints
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    Chapter 10 Generalized Duality in Variational Analysis
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    Chapter 11 Clustering via D. C. Optimization
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    Chapter 12 Algorithms and Merit Functions for the Principal Eigenvalue
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    Chapter 13 Modified Versions of the Cutting Angle Method
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    Chapter 14 Theoretical and Computational Results for a Linear Bilevel Problem
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    Chapter 15 The Lagrangian Search Method
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    Chapter 16 An ε -Maximum Principle for Generalized Control Systems
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    Chapter 17 D.C. Optimization Approaches via Markov Models for Restoration of Signal (1-D) and (2-D)
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    Chapter 18 New Positive Semidefinite Relaxations for Nonconvex Quadratic Programs
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    Chapter 19 Interval Analysis Applied to Global Minimization of Potential Energy Functions
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    Chapter 20 Approximate Analytic Center Quadratic Cut Method for Strongly Monotone Variational Inequalities
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    Chapter 21 Generating Convex Functions
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    Chapter 22 The Method of Moments for Nonconvex Variational Problems
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    Chapter 23 A Pivoting-Based Heuristic for the Maximum Clique Problem
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    Chapter 24 An Analytic Center Self-Concordant Cut Method for the Convex Feasibility Problem
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    Chapter 25 Strengthened Semidefinite Programming Relaxations for the Max-Cut Problem
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    Chapter 26 Supervised Training Using Global Search Methods
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    Chapter 27 Learning Rate Adaptation in Stochastic Gradient Descent
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    Chapter 28 Improving the Particle Swarm Optimizer by Function “Stretching”
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    Chapter 29 Some Convergence Properties of the Steepest Descent Algorithm Revealed by Renormalisation
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    Chapter 30 Interior—Point Algorithms for Dantzig and Wolfe Decomposition Principle
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    Chapter 31 Stochastic Perturbation Methods for Affine Restrictions
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    Chapter 32 Directed Derivatives of Convex Compact-Valued Mappings
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    Chapter 33 A Perturbed Auxiliary Problem Method for Paramonotone Multivalued Mappings
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    Chapter 34 A Note on Random Variational Inequalities and Simple Random Unilateral Boundary Value Problems: Well-Posedness and Stability Results
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    Chapter 35 A Comparison Principle and the Lipschitz Continuity for Minimizers
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    Chapter 36 Tunneling and Genetic Algorithms for Global Optimization
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    Chapter 37 Convexity and Monotonicity in Global Optimization