Description
Duality for Nonconvex Approximation and Optimization is a specialized academic text that delves into the mathematical foundations of duality theory as applied to nonconvex problems. Written by renowned mathematician I. Singer, this work provides rigorous treatment of approximation theory and optimization techniques that extend beyond traditional convex analysis.
The book systematically develops the theoretical underpinnings necessary for understanding dual problems in nonconvex settings, offering researchers and advanced students powerful mathematical tools. It covers key concepts including duality gaps, conjugate functions, and optimization algorithms applicable to practical problems. The author’s expertise is evident in the detailed proofs and comprehensive coverage of the subject matter, making this an indispensable reference for professionals in mathematics, operations research, and computational science.
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