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Recognition of the need to introduce the ideas of uncertainty in a wide variety of scientific fields today reflects in part some of the profound changes in science and engineering over the last decades. Nobody questions the ever-present need for a solid foundation in applied mechanics. Neither does anyone question nowadays the fundamental necessity to recognize that uncertainty exists, to learn to evaluate it rationally, and to incorporate it into design.This volume provides a timely and stimulating overview of the analysis of uncertainty in applied mechanics. It is not just one more rendition

Probability theory --- Convex sets. --- Probabilities. --- Mechanics, Applied. --- Statistique --- Probabilite

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Geodesic Convexity in Graphs is devoted to the study of the geodesic convexity on finite, simple, connected graphs. The first chapter includes the main definitions and results on graph theory, metric graph theory and graph path convexities. The following chapters focus exclusively on the geodesic convexity, including motivation and background, specific definitions, discussion and examples, results, proofs, exercises and open problems. The main and most st udied parameters involving geodesic convexity in graphs are both the geodetic and the hull number which are defined as the cardinality of minimum geodetic and hull set, respectively. This text reviews various results, obtained during the last one and a half decade, relating these two  invariants and some others such as convexity number, Steiner number, geodetic iteration number, Helly number, and Caratheodory number to a wide range a contexts, including products, boundary-type vertex sets, and perfect graph families. This monograph can serve as a supplement to a half-semester graduate course in geodesic convexity but is primarily a guide for postgraduates and researchers interested in topics related to metric graph theory and graph convexity theory.  .

Convex sets. --- Geodesics (Mathematics). --- Graphic methods. --- Geodesics (Mathematics) --- Graph theory --- Convex sets --- Mathematics --- Physical Sciences & Mathematics --- Algebra --- Graph theory. --- Graphs, Theory of --- Theory of graphs --- Extremal problems --- Mathematics. --- Partial differential equations. --- Differential geometry. --- Graph Theory. --- Differential Geometry. --- Partial Differential Equations. --- Combinatorial analysis --- Topology --- Geometry, Differential --- Global analysis (Mathematics) --- Global differential geometry. --- Differential equations, partial. --- Partial differential equations --- Differential geometry

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This text seeks to present the conjugate and sub/differential calculus using the method of perturbation functions in order to obtain the most general results in this field. Its secondary aim is to provide important applications of this calculus and of the properties of convex functions.

Convex functions. --- Convex sets. --- Functional analysis. --- Vector spaces. --- Linear spaces --- Linear vector spaces --- Algebras, Linear --- Functional analysis --- Vector analysis --- Functional calculus --- Calculus of variations --- Functional equations --- Integral equations --- Sets, Convex --- Convex domains --- Set theory --- Functions, Convex --- Functions of real variables

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In this monograph the author presents the theory of duality for nonconvex approximation in normed linear spaces and nonconvex global optimization in locally convex spaces. Key topics include: * duality for worst approximation (i.e., the maximization of the distance of an element to a convex set) * duality for reverse convex best approximation (i.e., the minimization of the distance of an element to the complement of a convex set) * duality for convex maximization (i.e., the maximization of a convex function on a convex set) * duality for reverse convex minimization (i.e., the minimization of a convex function on the complement of a convex set) * duality for d.c. optimization (i.e., optimization problems involving differences of convex functions). Detailed proofs of results are given, along with varied illustrations. While many of the results have been published in mathematical journals, this is the first time these results appear in book form. In addition, unpublished results and new proofs are provided. This monograph should be of great interest to experts in this and related fields. Ivan Singer is a Research Professor at the Simion Stoilow Institute of Mathematics in Bucharest, and a Member of the Romanian Academy. He is one of the pioneers of approximation theory in normed linear spaces, and of generalizations of approximation theory to optimization theory. He has been a Visiting Professor at several universities in the U.S.A., Great Britain, Germany, Holland, Italy, and other countries, and was the principal speaker at an N. S. F. Regional Conference at Kent State University. He is one of the editors of the journals Numerical Functional Analysis and Optimization (since its inception in 1979), Optimization, and Revue d'analyse num'erique et de th'eorie de l'approximation. His previous books include Best Approximation in Normed Linear Spaces by Elements of Linear Subspaces (Springer 1970), The Theory of Best Approximation and Functional Analysis (SIAM 1974), Bases in Banach Spaces I, II (Springer, 1970, 1981), and Abstract Convex Analysis (Wiley-Interscience, 1997).

Convex functions. --- Convex sets. --- Duality theory (Mathematics) --- Approximation theory. --- Convex domains. --- Convexity spaces. --- Theory of approximation --- Functional analysis --- Functions --- Polynomials --- Chebyshev systems --- Spaces, Convexity --- Convex sets --- Vector spaces --- Convex regions --- Convexity --- Calculus of variations --- Convex geometry --- Point set theory --- Algebra --- Mathematical analysis --- Topology --- Sets, Convex --- Convex domains --- Set theory --- Functions, Convex --- Functions of real variables --- Operator theory. --- Functional analysis. --- Mathematical optimization. --- Mathematics. --- Operator Theory. --- Functional Analysis. --- Optimization. --- Approximations and Expansions. --- Math --- Science --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Functional calculus --- Functional equations --- Integral equations

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This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter estimation, norm approximation and regularization, semi-definite programming problems, convex relaxation, and geometric problems. All derivation processes are presented in detail to aid in comprehension. The book offers concrete guidance, helping readers recognize and formulate convex optimization problems they might encounter in practice.

Mathematics. --- Operations Research, Management Science. --- Mathematical Software. --- Mathematical Modeling and Industrial Mathematics. --- Computer software. --- Mathématiques --- Logiciels --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Mathematical models. --- Operations research. --- Management science. --- Software, Computer --- Computer systems --- Mathematical optimization. --- Convex sets. --- Convex functions. --- Models, Mathematical --- Simulation methods --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory