Overview

Nonsmooth optimization covers the minimization or maximization of functions which do not have the differentiability properties required by classical methods. The field of nonsmooth optimization is significant, not only because of the existence of nondifferentiable functions arising directly in applications, but also because several important methods for solving difficult smooth problems lead directly to the need to solve nonsmooth problems, which are either smaller in dimension or simpler in structure.This book contains twenty five papers written by forty six authors from twenty countries in five continents. It includes papers on theory, algorithms and applications for problems with first-order nondifferentiability (the usual sense of nonsmooth optimization) second-order nondifferentiability, nonsmooth equations, nonsmooth variational inequalities and other problems related to nonsmooth optimization.Contents: Hybrid Methods for Finding the Nearest Euclidean Distance Matrix (S Al-Homidan & R Fletcher) On Generalized Differentiability of Optimal Solutions and Its Application to an Algorithm for Solving Bilevel Optimization Problems (S Dempe) An Elementary Rate of Convergence Proof for the Deep Cut Ellipsoid Algorithm (J B G Frenk & J Gromicho) On Second-Order Directional Derivatives in Nonsmooth Optimization (L R Huang & K F Ng) Sensitivity of Solutions in Nonlinear Programming Problems with Nonunique Multipliers (A B Levy & R T Rockafellar) Necessary and Sufficient Conditions for Solution Stability of Parametric Nonsmooth Equations (J-S Pang) Characterizations of Optimality for Homogeneous Programming Problems with Applications (A M Rubinov & B M Glover) A Globally Convergent Newton Method for Solving Variational Inequality Problems with Inequality Constraints (K Taji & M Fukushima) A Successive Approximation Quasi-Newton Process for Nonlinear Complementarity Problem (S-Z Zhou et al.) and other papersReadership: Students, academics and industry professionals.

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