Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aims to cover a wide range of topics in this area.
This book shows how runtime behavior can be analyzed in a rigorous way and for combinatorial optimization in particular. It presents well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems.
Optimization is a field important in its own right but is also integral to numerous applied sciences, including operations research, management science, economics, and finance. This book offers a well-balanced presentation of the subject. It includes exercises, solutions and hints, and Matlab/Maple supplements.
Mathematical optimization encompasses both a rich and rapidly evolving body of fundamental theory, and a variety of exciting applications in science and engineering. This title contains a careful selection of articles on advances in optimization theory, numerical methods, and their applications in engineering.
Introduces developments in the area of risk estimation in complex systems. This book studies methods of accelerated modelling, asymptotical analysis and optimal estimating. It also suggests applying numerical methods for analysis of super-large failure trees having large amount of multiple vertices.
Focusing on the modeling and solution of deterministic multistage decision problems, this book looks at dynamic programming as a problem-solving optimization method. It also discusses the dynamic programming analysis of a problem, illustrates the rationale behind this analysis, and clarifies the theoretical grounds that justify the rationale.
Offers a review of convex analysis and the fundamental theoretical findings on generalized convexity and on optimization, including their applications. This work includes a chapter devoted to generalized monotonicity and its relationship to generalized convexity and with the characterizations of important classes of fractional programming.
With contributions from leaders in the field, this volume assesses the main issues in the experimental analysis of algorithms, examines their developmental cycle, and demonstrates how to configure and tune algorithms with advanced experimental techniques.
Shows how life often works at the extremes - with values becoming as small (or as large) as possible - and how mathematicians over the centuries have struggled to calculate these problems of minima and maxima. This is the book on optimization for math enthusiasts of all backgrounds.
Containing illustrations, computer code, insights, and advice, this volume explores differential evolution (DE) in principle and practice. It is aimed at professionals and students wanting an evolutionary perspective on global numerical optimization. A companion CD includes DE-based optimization software in several programming languages.
This book brings together papers of well-known specialists in game theory and adjacent problems. It presents the basic results in dynamic games, stochastic games, applications of game theoretical methods in ecology and economics and methodological aspects of game theory.
This is a comprehensive guide to Pyomo, an open source software package for formulating and solving large-scale optimization and operations research problems. Includes chapters on advanced modeling capabilities for nonlinear and stochastic optimization.
'Exact' and 'heuristic' techniques are enhancing our ability to overcome intractable obstacles in the world of optimization. Using the linear ordering problem as an illustration, this text provides a toolkit for tackling a variety of combinatorial dilemmas.
Global optimization aims at solving the most general problem of deterministic mathematical programming. In addition, once the solution is found, proof of its optimality is also expected from this methodology. This title examines global optimization's mathematical capability, power, and wide ranging solution to many fields in the applied sciences.
Gives a treatment of the theory of optimization (unconstrained optimization, nonlinear programming, semi-infinite programming, etcetera) in finite-dimensional spaces. This book covers the fundamental results of convexity theory and the theory of duality in nonlinear programming and the theories of linear inequalities and linear programming.
Focuses on the mathematics underlying the ideas of optimizing linear functions under linear constraints and the algorithms used to solve them. This book is suitable for a junior/senior level course on linear optimization. It is also suitable for secondary school mathematics teachers.
Several problems in modern genome mapping analysis belong to the field of discrete optimisation on a set of all possible orders. This book proposes formulations, mathematical models and algorithms for genetic/genomic mapping problem that can be formulated in TSP-like terms.
Many important problems in global optimization, algebra, probability and statistics, applied mathematics, control theory, and financial mathematics can be modeled as a particular instance of the Generalized Moment Problem (GMP). This book introduces a general methodology to solve the GMP when its data are polynomials and basic semi-algebraic sets.
Here is a much-needed attempt to reconcile optimization with apportionment in just-in-time (JIT) sequences. It argues that apportionment theory and optimization based on deviation functions provide benchmarks for a process, and examines recent developments.
Combinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization set aim to cover a wide range of topics in this area.