"Nonsmooth Optimization Methods and Applications" provides an overview of this branch of mathematics, concentrating on the interaction between the theory and its applications. The topics covered include: generalization of convexity, invexity, coercivity, monotonicity and pseudoconvexity; generalization of differentiability of functions and multifunctions, and their applications to extremum problems; variational and hemivariational inequalities and their applications to mechanics and operations research; calculus for non-differentiable functions and new developments in the calculus of variations; stability of mathematical programs; Lagrangian multipliers and augmented Langrangian theory; penalty methods; vector extremum problems and methods for solving non-differentiable extremum problems, in particular bundle methods.
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math- ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air- line crew scheduling, corporate planning, computer-aided design and man- ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca- tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover- ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo- rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi- tion, linear programming relaxations are often the basis for many approxi- mation algorithms for solving NP-hard problems (e.g. dual heuristics).
Stochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science. This book is unique in the following aspects.
In recent decades, there has been increased interest in understanding ecosystems in order to be able to manage and conserve them. Yet examples of how research directly supports conservation are rare. Protected area managers and policy makers need scientific information from protected areas for policy development and to effectively devise, revise, and implement management strategies. Researchers seek a clear understanding of what types of research can directly support conservation efforts to guide them in the design of such projects. A variety of perspectives of what constitutes 'conservation' or 'applied' wildlife research may exist, and indeed conservation priorities do differ between sites so that ultimately, what we describe here is from one perspective and designing projects that directly support site conservation depends on a prior understanding of issues at the site. This book is intended to encourage thinking about what constitutes conservation research to be able to better develop projects that directly support conservation. The aim of this book is to support research that directly benefits conservation by reviewing applied research and providing examples in which it has been used for conservation purposes.
This book deals with combinatorial aspects of epistasis, a notion that existed for years in genetics and appeared in the ?eld of evolutionary algorithms in the early 1990s. Even thoughthe?rst chapterputsepistasisintheperspective ofevolutionary algorithms and arti?cial intelligence, and applications occasionally pop up in other chapters, thisbookisessentiallyaboutmathematics, aboutcombinatorialtechniques to compute in an e?cient and mathematically elegant way what will be de?ned as normalized epistasis. Some of the material in this book ?nds its origin in the PhD theses of Hugo Van Hove  and Dominique Suys . The sixth chapter also contains material that appeared in the dissertation of Luk Schoofs . Together with that of M. Teresa Iglesias , these dissertations form the backbone of a decade of mathematical ventures in the world of epistasis. The authors wish to acknowledge support from the Flemish Fund of Scienti?c - search (FWO-Vlaanderen) and of the Xunta de Galicia. They also wish to explicitly mentiontheintellectualandmoralsupporttheyreceivedthroughoutthepreparation of this work from their family and their colleagues Emilio Villanueva, Jose Mar'a Barja and Arnold Beckelheimer, as well as our local T T Xpert Jan Adriaenssens.
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