TY - BOOK ID - 156805 TI - Iterative methods for optimization PY - 1999 SN - 0898714338 9780898714333 PB - Philadelphia (Pa.): SIAM DB - UniCat KW - Programming KW - Numerical analysis KW - Computer science KW - Mathematical optimization KW - Iterative methods (Mathematics) KW - 519.86 KW - 519.68 KW - #TELE:SISTA KW - 519.6 KW - 681.3 *G18 KW - Theory of economic-mathematical models KW - Computer programming KW - Computational mathematics. Numerical analysis. Computer programming KW - Partial differential equations: difference methods; elliptic equations; finite element methods; hyperbolic equations; method of lines; parabolic equations (Numerical analysis) KW - Mathematical optimization. KW - Iterative methods (Mathematics). KW - 681.3 *G18 Partial differential equations: difference methods; elliptic equations; finite element methods; hyperbolic equations; method of lines; parabolic equations (Numerical analysis) KW - 519.6 Computational mathematics. Numerical analysis. Computer programming KW - 519.68 Computer programming KW - 519.86 Theory of economic-mathematical models KW - Optimization (Mathematics) KW - Optimization techniques KW - Optimization theory KW - Systems optimization KW - Mathematical analysis KW - Maxima and minima KW - Operations research KW - Simulation methods KW - System analysis KW - Iteration (Mathematics) UR - https://www.unicat.be/uniCat?func=search&query=sysid:156805 AB - This book presents a carefully selected group of methods for unconstrained and bound constrained optimization problems and analyzes them in depth both theoretically and algorithmically. It focuses on clarity in algorithmic description and analysis rather than generality, and while it provides pointers to the literature for the most general theoretical results and robust software, the author thinks it is more important that readers have a complete understanding of special cases that convey essential ideas. A companion to Kelley's book, Iterative Methods for Linear and Nonlinear Equations (SIAM, 1995), this book contains many exercises and examples and can be used as a text, a tutorial for self-study, or a reference. Iterative Methods for Optimization does more than cover traditional gradient-based optimization: it is the first book to treat sampling methods, including the Hooke& Jeeves, implicit filtering, MDS, and Nelder& Mead schemes in a unified way. ER -