Mathematical Programming with Data Perturbations (eBook, PDF)

Redaktion: Fiacco, Anthony V.

• Format: PDF

Produktbeschreibung

Describes various methodologies for sensitivity, stability and approximation analysis of mathematical programming and related problem structures involving parameters. This text covers such areas as the effect of perturbations on the performance of algorithms, and approximation techniques for optimal control problems.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 456
• Erscheinungstermin: 23. September 2020
• Englisch
• ISBN-13: 9781000117110
• Artikelnr.: 60149893

Autorenporträt

ANTHONY V. FIACCO is Professor Emeritus of Operations Research and Applied Science at George Washington University, Washington, D.C. From 1960 to 1971, Dr. Fiacco was an Operations Analyst for the Research Analysis Corporation in McLean, Virginia, where he was Project Chairman of a study that pioneered several breakthroughs in nonlinear programming (NLP) methodology. He is the author or coauthor of numerous papers on NLP theory and applications, the coauthor with Garth P. McCormick in 1968 of a Lanchester prize-winning book on barrier and penalty function methodology, and the editor of several books, including Mathematical Programming with Data Perturbations I and II (both titles, Marcel Dekker, Inc.). A prominent contributor to the development of computable methods for sensitivity and stability analysis, Dr. Fiacco received the Ph.D. degree 1967 in applied mathematics from Northwestern University, Evanston, Illinois. Since 1979, he has organized, at the George Washington University, the only annual conference completely devoted to sensitivity and stability issues.

Inhaltsangabe

Discretization and mesh-independent of Newton's method for generalized
differentiability of optimal solutions in non-linear parametric
optimization; characterisations of Lipschitzian stability in nonlinear
programming; on second order sufficient conditions for structured nonlinear
programs in infinite-dimensional function spaces; algorithmic stability
analysis for certain trust region methods; a note on using linear knowledge
to solve efficiency linear programs specified with approximate data; on the
role of the Mangasarian-Fromovitz constraint qualification for penalty-,
exact penalty-, and Lagrange multiplier methods; Hoffman's error bound for
systems of convex functions and applications to nonlinear optimization; on
well-posedness and stability analysis optimization; convergence of
approximations to nonlinear optimal control problems; a perturbation-based
duality classification for max-flow min-cut problems of Strang and Iri;
central and peripheral results in the study of marginal and performance
functions; topological stability of feasible sets in semi-infinite
optimization - a tutorial; solution existence for infinite quadratic
programming; sensitivity analysis of nonlinear programming problems via
minimax functions; parametric linear complementary problems; sufficient
conditions for weak sharp minima of order two and directional derivatives
of the value function.