Tikhonov regularization is the most popular general-purpose method for regularization, a mathematical technique to suppress the effect of noise in data, and uses much of the machinery of Hilbert space theory. This book develops the theory of Tikhonov regularization for a certain class of linear inverse problems which are defined on Hilbert spaces. To explain why and how Tikhonov regularization works, the singular value expansion for compact operators is introduced. Tikhonov regularization with seminorms is also analyzed and for this purpose, densely defined unbounded operators are addressed and their basic properties presented. In addition, the author provides readers with a quick but thorough review of Hilbert space theory and a brief introduction to weak derivatives and Sobolev spaces. Intended as an expository work for those interested in inverse problems and Tikhonov regularization, including graduates and researchers, the author presents the theory in an engaging and straightforward style.

La publicacion no es unicamente un libro de ejercicios resueltos de programacion lineal para estudiantes, sino una fuente de informacion e incluso, en cierto modo, puede hablarse de una metodologia para la resolucion de dichos ejercicios, de interes tanto para estudiantes como para profesionales que en su trabajo lleven a cabo actividades de optimizacion tanto en el ambito de la empresa privada como en las administraciones publicas. Ha sido planificado para su utilizacion por personas con conocimientos de programacion lineal, primordialmente para facilitar el aprendizaje de los conceptos y procedimientos de formulacion y resolucion de modelos de programacion lineal de los estudiantes de dicha materia en las diversas Facultades y Escuelas Tecnicas en las que se imparte. Su finalidad es eminentemente didactica, y unicamente por razones pedagogicas se justifica la presente publicacion.

A comprehensive and rigorous introduction to optimization and approximation, including many exercises and examples.

One of the most frequently occurring types of optimization problems involves decision variables which have to take integer values. From a practical point of view, such problems occur in countless areas of management, engineering, administration, etc., and include such problems as location of plants or warehouses, scheduling of aircraft, cutting raw materials to prescribed dimensions, design of computer chips, increasing reliability or capacity of networks, etc. This is the class of problems known in the professional literature as "discrete optimization" problems. While these problems are of enormous applicability, they present many challenges from a computational point of view. This volume is an update on the impressive progress achieved by mathematicians, operations researchers, and computer scientists in solving discrete optimization problems of very large sizes. The surveys in this volume present a comprehensive overview of the state of the art in discrete optimization and are written by the most prominent researchers from all over the world.

This volume describes the tremendous progress in discrete optimization achieved in the last 20 years since the publication of Discrete Optimization '77, Annals of Discrete Mathematics, volumes 4 and 5, 1979 (Elsevier). It contains surveys of the state of the art written by the most prominent researchers in the field from all over the world, and covers topics like neighborhood search techniques, lift and project for mixed 0-1 programming, pseudo-Boolean optimization, scheduling and assignment problems, production planning, location, bin packing, cutting planes, vehicle routing, and applications to graph theory, mechanics, chip design, etc.

Key features:
state of the art surveys
comprehensiveness
prominent authors
theoretical, computational and applied aspects.

This book is a reprint of "Discrete Applied Mathematics" Volume 23, Numbers 1-3
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In 1781, Gaspard Monge defined the problem of ""optimal transportation"", or the transferring of mass with the least possible amount of work, with applications to engineering in mind. In 1942, Leonid Kantorovich applied the newborn machinery of linear programming to Monge's problem, with applications to economics in mind. In 1987, Yann Brenier used optimal transportation to prove a new projection theorem on the set of measure preserving maps, with applications to fluid mechanics in mind. Each of these contributions marked the beginning of a whole mathematical theory, with many unexpected ramifications. Nowadays, the Monge-Kantorovich problem is used and studied by researchers from extremely diverse horizons, including probability theory, functional analysis, isoperimetry, partial differential equations, and even meteorology. Originating from a graduate course, the present volume is at once an introduction to the field of optimal transportation and a survey of the research on the topic over the last 15 years. The book is intended for graduate students and researchers, and it covers both theory and applications. Readers are only assumed to be familiar with the basics of measure theory and functional analysis.

Clear and comprehensive, this volume introduces theoretical, computational, and applied concepts and is useful both as text and as a reference book. Considerations of theoretical and computational methods include the general linear programming problem, the simplex computational procedure, the revised simplex method, more. Examples and exercises with selected answers appear in every chapter. 1995 edition.

Multidimensional continued fractions form an area of research within number theory. Recently the topic has been linked to research in dynamical systems, and mathematical physics, which means that some of the results discovered in this area have applications in describing physical systems. This book gives a comprehensive and up to date overview of recent research in the area.

Integer solutions for systems of linear inequalities, equations, and congruences are considered along with the construction and theoretical analysis of integer programming algorithms. The complexity of algorithms is analyzed dependent upon two parameters: the dimension, and the maximal modulus of the coefficients describing the conditions of the problem. The analysis is based on a thorough treatment of the qualitative and quantitative aspects of integer programming, in particular on bounds obtained by the author for the number of extreme points. This permits progress in many cases in which the traditional approach - which regards complexity as a function only of the length of the input-leads to a negative result.

In recent years there has been an explosion of research into linear programming, as well as further steady advances in integer programming. This research has been reported in the research literature but there has been little done from the view of a "combined whole". This book aims to overcome this. With an international authorship of contributors from acknowledged experts in their field, this book provides a clear exposition on such topics as simplex algorithms, and interior point algorithms, both from a theoretical and a computational viewpoint. Surveying recent research that is currently only available in journals this topical book will be of interest not only in the field of mathematics, but also in computer science and operations research as well.

No one working in duality should be without a copy of Convex Analysis and Variational Problems. This book contains different developments of infinite dimensional convex programming in the context of convex analysis, including duality, minmax and Lagrangians, and convexification of nonconvex optimization problems in the calculus of variations (infinite dimension). It also includes the theory of convex duality applied to partial differential equations; no other reference presents this in a systematic way. The minmax theorems contained in this book have many useful applications, in particular the robust control of partial differential equations in finite time horizon. First published in English in 1976, this SIAM Classics in Applied Mathematics edition contains the original text along with a new preface and some additional references.

Provides a relatively brief introduction to conjugate duality in both finite- and infinite-dimensional problems. An emphasis is placed on the fundamental importance of the concepts of Lagrangian function, saddle-point, and saddle-value. General examples are drawn from nonlinear programming, approximation, stochastic programming, the calculus of variations, and optimal control.

In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are introduced to meet the needs of both the traditional user and the high performance specialist. Templates, a description of a general algorithm rather than the executable object or source code more commonly found in a conventional software library, offer whatever degree of customization the user may desire. Templates have three distinct advantages: they are general and reusable, they are not language specific, and they exploit the expertise of both the numerical analyst, who creates a template reflecting in depth knowledge of a specific numerical technique, and the computational scientist, who then provides "value added" capability to the general template description, customizing it for specific needs. For each template that is presented, the authors provide a mathematical description of the flow of the algorithm, discussion of convergence and stopping criteria to use in the iteration, suggestions for applying a method to special matrix types, advice for tuning the template, tips on parallel implementations, and hints as to when and why a method is useful.

Eigenfunctions of the Laplacian of a Riemannian manifold can be described in terms of vibrating membranes as well as quantum energy eigenstates. This book is an introduction to both the local and global analysis of eigenfunctions. The local analysis of eigenfunctions pertains to the behavior of the eigenfunctions on wavelength scale balls. After re-scaling to a unit ball, the eigenfunctions resemble almost-harmonic functions. Global analysis refers to the use of wave equation methods to relate properties of eigenfunctions to properties of the geodesic flow. The emphasis is on the global methods and the use of Fourier integral operator methods to analyze norms and nodal sets of eigenfunctions. A somewhat unusual topic is the analytic continuation of eigenfunctions to Grauert tubes in the real analytic case, and the study of nodal sets in the complex domain. The book, which grew out of lectures given by the author at a CBMS conference in 2011, provides complete proofs of some model results, but more often it gives informal and intuitive explanations of proofs of fairly recent results. It conveys inter-related themes and results and offers an up-to-date comprehensive treatment of this important active area of research.

This book is designed as an advanced undergraduate or a first-year graduate course for students from various disciplines and in particular from Economics and Social Sciences. The first part develops the fundamental aspects of mathematical modeling, dealing with both continuous time systems (differential equations) and discrete time systems (difference equations). Particular attention is devoted to equilibria, their classification in the linear case, and their stability. An effort has been made to convey intuition and emphasize connections and concrete aspects, without giving up the necessary theoretical tools. The second part introduces the basic concepts and techniques of Dynamic Optimization, covering the first elements of Calculus of Variations, the variational formulation of the most common problems in deterministic Optimal Control, both in continuous and discrete versions.

Welcome to the beginning of this book - an algebra book that shows and explains further topics than those that are covered in a primary course on linear algebra. Such course usually leaves the student with the question "Why in the world do I have to go through linear algebra?" which is absolutely understandable. Algebra, and other sciences in general, are not entities that one just needs to pass as a course. Sciences are masters that stay with you and are always there whenever you need them, that in some occasions we do not understand that those masters are something else. Before talking about the importance of algebra, let us first clear some details about this text. This book does not only contain a set of complicated equations with heavy definitions about abstract entities and problems that arise in several fields of mathematics, which in many other texts, makes you feel like you're reading Mandarin. This book also contains clear and deductive explanations from different perspectives about those set of complicated equations, with the hope of making them accessible and easy to assimilate for the reader. Algebra is the science of representing life through equations while keeping it simple. The reason for writing this book was, at first, the decision to spread knowledge of such a great science that is usually feared by many young students and ignored by others, when in reality Algebra is a body of knowledge so beautiful within itself, as well as necessary dealing with or approaching many mathematical situations. As this book is being developed, another reason to continue to write it arose - there is a lack of easy-to-understand texts, which are supposed to be the high end books that today's generation are obligated to read, in order to understand the subjects they should know to continue their formation in their respective careers. This book is dedicated to those who really want to learn linear algebra. But be warned, the journey is not promised to be easy as it will require dedication, honesty and time from the reader; but the journey will also be nurturing and revealing in the sense that at the end of each chapter, the reader will be able to say "I've got it!" in much the same way when Archimedes said "Eureka!" which means "I've found it!" In addition, this book also contains techniques and algorithms, along with their derivations and examples, which are used in the real world, thus helping the reader become a well prepared professional. Many texts, articles and lessons were used as resources for this book, however, the most used resource in the development of this text was common sense. You see, mathematics is a free science, a body of knowledge that anyone can derive on its own. This book is just a compendium of the works of many mathematicians through the centuries, making it possible for humankind to get to where it is today. And as life goes on, it is probable that further developments, researches and advances will continue. That was one of the main ideas in the background while writing this text - to make the reader able to fully understand as much knowledge in linear algebra as possible, especially those concerning methods and algorithms that arose in the twentieth century. Just as a brief comment, the first chapter of this book is dedicated to making certain topics clear to avoid confusion, and answering questions that usually arise during the first course in linear algebra. The rest of the chapters are more advanced and developed.

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions-a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as GLMs with Stata's glm command offers some advantages. For example, model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution. This text thoroughly covers GLMs, both theoretically and computationally, with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares, another method of parameter estimation, are a consequence of ML estimation using Fisher scoring.

Presenting recent developments of key topics in nonlinear programming, this text looks specifically at three main areas; convex analysis, optimality conditions and dual computational techniques.

In the past decade, primal-dual algorithms have emerged as the most important and useful algorithms from the interior-point class. This book presents the major primal-dual algorithms for linear programming in straightforward terms. A thorough description of the theoretical properties of these methods is given, as are a discussion of practical and computational aspects and a summary of current software. This is an excellent, timely, and well-written work. The major primal-dual algorithms covered in this book are path-following algorithms (short- and long-step, predictor-corrector), potential-reduction algorithms, and infeasible-interior-point algorithms. A unified treatment of superlinear convergence, finite termination, and detection of infeasible problems is presented. Issues relevant to practical implementation are also discussed, including sparse linear algebra and a complete specification of Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual algorithms to more general problems such as monotone complementarity, semidefinite programming, and general convex programming problems.

Performance analysis of modern communication systems has lead to a revision and sharpening of nonlinear stability analysis techniques developed over the last century. Applicability of such techniques involves a number of areas, including: process control systems, active queue management in data networks, and other branches of engineering. This monograph presents some recent performance analysis results within a unified stability analysis framework. Several interesting counterexamples to the existing nonlinear stability theory are given. Additionally, several cutting-edge case studies from air traffic control systems and data networks are presented to further illustrate the applications of the theory. The main theoretical results build upon the well-established multiplier theory, which has received much interest because of recent advances in software packages such as the linear matrix inequality (LMI) toolbox. The exposition demonstrates the existence of monotone nonlinearites in problems of interest and gives an exhaustive stability analysis treatment of systems containing such nonlinearities. Key features: * broad range of topics: stability analysis, monotone nonlinearities, multiplier theory, service quality in data networks, protocol scalability, air traffic congestion control * recent and original results presented * many examples and case studies illustrate the applicability of the theory * comprehensive glossaries, bibliography, index, and appendices "Qualitative Nonlinear Dynamics of Communication Networks" is a useful reference for graduate students, and practitioners in control, computer, electrical, aerospace, and mechanical engineering. It can be used as a supplementary textfor nonlinear stability courses at the graduate level. Prerequisites are a familiarity with elementary control theory, linear systems theory, matrix theory, and functional analysis.

Nonlinear Systems covers a wide range of topics in nonlinear science, from general nonlinear dynamics, soliton systems, and the solution of nonlinear differential and difference equations to the integrability of discrete nonlinear systems, and classical and quantum chaos. Its chapters reflect the current status of important nonlinear theories in various areas of applied mathematics and mathematical physics and collectively provide a comprehensive picture of new areas and their applications.

The problem of efficient or optimal allocation of resources is a fundamental concern of economic analysis. The theory of optimal economic growth can be viewed as an aspect of this central theme, which emphasizes in general the issues arising in the allocation of resources over an infinite time horizon, and in particular the consumption-investment decision process in models in which there is no natural "terminal date". This broad scope of "optimal growth theory" is one which has evolved over time, as economists have discovered new interpretations of its central results, as well as new applications of its basic methods. The "Handbook on Optimal Growth" provides surveys of significant results of the theory of optimal growth, as well as the techniques of dynamic optimization theory on which they are based. Armed with the results and methods of this theory, a researcher will be in an advantageous position to apply these versatile methods of analysis to new issues in the area of dynamic economics.

This text is one of the first to treat vector calculus using differential forms in place of vector fields and other outdated techniques. Geared towards students taking courses in multivariable calculus, this innovative book aims to make the subject more readily understandable. Differential forms unify and simplify the subject of multivariable calculus, and students who learn the subject as it is presented in this book should come away with a better conceptual understanding of it than those who learn using conventional methods.

The efficient numerical solution of PDE constrained optimization problems plays an important role in many engineering and science applications. The development of robust and efficient numerical algorithms requires the integration of tools from several mathematical subdisciplines, often only described individually in books or journal articles. The goal of this book is to provide readers with a brief introduction to this active research area as well as with an overview of the state-of-the-art in the important topics of adaptive discretizations of PDE optimization problems, handling of control and state constraints, domain decomposition and homogenization of PDEs on networks, and reduced order modeling.