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Nonsmooth Optimization
Proceedings of a IIASA Workshop, March 28 - April 8, 1977
1st Edition - January 1, 1978
Editors: Claude Lemarechal, Robert Mifflin
Language: English
eBook ISBN:9781483188768
9 7 8 - 1 - 4 8 3 1 - 8 8 7 6 - 8
Nonsmooth Optimization contains the proceedings of a workshop on non-smooth optimization (NSO) held from March 28 to April 8,1977 in Austria under the auspices of the International…Read more
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Nonsmooth Optimization contains the proceedings of a workshop on non-smooth optimization (NSO) held from March 28 to April 8,1977 in Austria under the auspices of the International Institute for Applied Systems Analysis. The papers explore the techniques and theory of NSO and cover topics ranging from systems of inequalities to smooth approximation of non-smooth functions, as well as quadratic programming and line searches. Comprised of nine chapters, this volume begins with a survey of Soviet research on subgradient optimization carried out since 1962, followed by a discussion on rates of convergence in subgradient optimization. The reader is then introduced to the method of subgradient optimization in an abstract setting and the minimal hypotheses required to ensure convergence; NSO and nonlinear programming; and bundle methods in NSO. A feasible descent algorithm for linearly constrained least squares problems is described. The book also considers sufficient minimization of piecewise-linear univariate functions before concluding with a description of the method of parametric decomposition in mathematical programming. This monograph will be of interest to mathematicians and mathematics students.
Preface
Introduction
Subgradient Methods: A Survey of Soviet Research
Nondifferentiable Optimization and the Relaxation Method
An Extension of the Method of Subgradients
Nonsmooth Optimization and Nonlinear Programming
Bundle Methods in Nonsmooth Optimization
A Feasible Descent Algorithm for Linearly Constrained Least Squares Problems
Sufficient Minimization of Piecewise-Linear Univanate Functions
The Method of Parametric Decomposition in Mathematical Programming: The Nonconvex Case