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Vorticity, Statistical Mechanics, and Monte Carlo Simulation
This book is drawn from across many active fields of mathematics and physics, and has connections to atmospheric dynamics, spherical codes, graph theory, constrained optimization problems, Markov Chains, and Monte Carlo methods. It addresses how to access interesting, original, and publishable research in statistical modeling of large-scale flows and several related fields. The authors f this book explicitly reach around the major branches of mathematics and physics, showing how the use of a few straightforward approaches can create a cornucopia of intriguing questions and the tools to answer them. In reading this book, the reader will learn how to research a topic and how to understand statistical mechanics treatments of fluid dynamics.
Vector Optimization : Set-valued and Variational Analysis
Vector optimization model has found many important applications in decision making problems such as those in economics theory, management science, and engineering design (since the introduction of the Pareto optimal solu tion in 1896). Typical examples of vector optimization model include maxi mization/minimization of the objective pairs (time, cost), (benefit, cost), and (mean, variance) etc. Many practical equilibrium problems can be formulated as variational in equality problems, rather than optimization problems, unless further assump tions are imposed. The vector variational inequality was introduced by Gi- nessi (1980). Extensive research on its relations with vector optimization, the existence of a solution and duality theory has been pursued. The fundamental idea of the Ekeland's variational principle is to assign an optimization problem a slightly perturbed one having a unique solution which is at the same time an approximate solution of the original problem. This principle has been an important tool for nonlinear analysis and optimization theory. Along with the development of vector optimization and set-valued optimization, the vector variational principle introduced by Nemeth (1980) has been an interesting topic in the last decade
Variational Methods in Shape Optimization Problems
The study of shape optimization problems encompasses a wide spectrum of academic research with numerous applications to the real world. In this work these problems are treated from both the classical and modern perspectives and target a broad audience of graduate students in pure and applied mathematics, as well as engineers requiring a solid mathematical basis for the solution of practical problems.
Time-Varying Network Optimization
Network flow optimization analyzes optimization problems on networks; hence, network optimization is reflected in many application fields including transportation, telecommunication, computer networking, financial planning, logistics and supply chain management, energy systems, etc. However, to date, most network optimization problems that have been studied are static network optimization problems. But "real world networks" are time-varying in essence, and therefore any flow within a network must take a certain amount of time to traverse an arc. Moreover, the parameters of "real world networks" may change over time.
Theoretical Aspects of Local Search
Local search has been applied successfully to a diverse collection of optimization problems. It's appreciated for its basic conceptual foundation, its general applicability, and its power to serve as a source for new search paradigms. The typical characteristics of combinatorial optimization problems to which local search can be applied, its relation to complexity theory, and the combination with randomized search features have led to a wealth of interesting theoretical results. However, these results are scattered throughout the literature. This is the first book that presents a large collection of theoretical results in a consistent manner, thus providing the reader with a coherent overview of the achievements obtained so far, but also serving as a source of inspiration for the development of novel results in the challenging field of local search.
Stochastic Optimization Methods ; 1st ed.
Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.
Stochastic Optimization Methods ; 2nd ed.
Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations.
Stigmergic Optimization
Deals with the application of stigmergy for a variety of optimization problems. This volume comprises 12 chapters including an introductory chapter giving the fundamental definitions, inspirations and some research challenges.
Shock and Damage Models in Reliability Theory
Describes the reliability properties and maintenance policies associated with shock and damage models. The author is a leading researcher in this field with over thirty years’ experience. The book introduces stochastic processes before surveying current developments in shock and damage models. The reliability quantities of each model are explained and their optimization problems are discussed analytically. The maintenance policies of these models are explored in terms of maintenance theory and reliability theory and practical applications of all of these models are revealed with case studies.
Risk Management in Stochastic Integer Programming : With Application to Dispersed Power Generation
Two-stage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixed-integer two-stage stochastic optimization problem: The well-known mean-risk modeling, which aims at finding a best solution in terms of expected costs and risk measures, and stochastic programming with first order dominance constraints that heads towards a decision dominating a given cost benchmark and optimizing an additional objective. For this new class of stochastic optimization problems results on structure and stability are proven. Moreover, the author develops equivalent deterministic formulations of the problem, which are efficiently solved by the presented dual decomposition method based on Lagrangian relaxation and branch-and-bound techniques.
Representations for Genetic and Evolutionary Algorithms
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
Recent Advances in Optimization
This volume contains the Proceedings of the Twelfth French-German-Spanish Conference on Optimization held at the University of Avignon in 2004. We refer to this conference by using the acronym FGS-2004. During the period September 20-24, 2004, about 180 scientists from around the world met at Avignon (France) to discuss recent developments in optimization and related fields. The main topics discussed during this meeting were the following: 1. smooth and nonsmooth continuous optimization problems, 2. numerical methods for mathematical programming, 3. optimal control and calculus of variations, 4. differential inclusions and set-valued analysis, 5. stochastic optimization, 6. multicriteria optimization, 7. game theory and equilibrium concepts, 8. optimization models in finance and mathema.
Quantum Annealing and Related Optimization Methods
Quantum annealing employs quantum fluctuations in frustrated systems or networks to anneal the system down to its ground state, or more generally to its so-called minimum cost state. Often this procedure turns out to be more effective, in multivariable optimization problems, than its classical counterpart utilizing tunable thermal fluctuations. This volume is divided into three parts. Part I is an extensive tutorial introduction familiarizing the reader with the background material necessary to follow the core of the book. Part II gives a comprehensive account of the fundamentals and applications of the quantum annealing method, and Part III compares quantum annealing with other related optimization methods. This is the first book entirely devoted to quantum annealing and will be both an invaluable primer and guidebook for all advanced students and researchers in this important field.
Probabilistic and Randomized Methods for Design under Uncertainty
Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimization problems for which data is not known exactly. Gathering contributions from the world’s leading researchers in optimization and robust control; this book highlights the interactions between these two fields, and focuses on new randomised and probabilistic techniques for solving design problems in the presence of uncertainty.
Portfolio Management with Heuristic Optimization
Portfolio Management with Heuristic Optimization consist of two parts. The first part (Foundations) deals with the foundations of portfolio optimization, its assumptions, approaches and the limitations when "traditional" optimization techniques are to be applied. In addition, the basic concepts of several heuristic optimization techniques are presented along with examples of how to implement them for financial optimization problems. The second part (Applications and Contributions) consists of five chapters, covering different problems in financial optimization: the effects of (linear, proportional and combined) transaction costs together with integer constraints and limitations on the initital endowment to be invested; the diversification in small portfolios; the effect of cardinality constraints on the Markowitz efficient line; the effects (and hidden risks) of Value-at-Risk when used the relevant risk constraint; the problem factor selection for the Arbitrage Pricing Theory.
Optimization with Multivalued Mappings : Theory, Applications and Algorithms
In the field of nondifferentiable nonconvex optimization, one of the most intensely investigated areas is that of optimization problems involving multivalued mappings in constraints or as the objective function. This book focuses on the tremendous development in the field that has taken place since the publication of the most recent volumes on the subject. The new topics studied include the formulation of optimality conditions using different kinds of generalized derivatives for set-valued mappings (such as, for example, the coderivative of Mordukhovich), the opening of new applications (e.g., the calibration of water supply systems), or the elaboration of new solution algorithms (e.g., smoothing methods). The book is divided into three parts. The focus in the first part is on bilevel programming. The chapters in the second part contain investigations of mathematical programs with equilibrium constraints. The third part is on multivalued set-valued optimization. The chapters were written by outstanding experts in the areas of bilevel programming, mathematical programs with equilibrium (or complementarity) constraints (MPEC), and set-valued optimization problems.
Optimisation et contrôle stochastique appliqués à la finance = Optimization and stochastic control applied to finance
The objective and the originality of this book is to present the different aspects and methods used in the resolution of stochastic optimization problems with a view to more specific applications in finance: portfolio management, option hedging, optimal investment. . We have included some recent developments on the subject without seeking a priori the greatest generality. We wanted a gradual exposure of mathematical methods by first presenting the intuitive ideas and then precisely stating the results with full and detailed proofs.
Optimal Design of Complex Mechanical Systems : With Applications to Vehicle Engineering
Devoted both to researchers wishing to acquire a basic knowledge on the optimization of complex mechanical systems and to engineers specialist in the field of vehicle design. The main optimization problems issues are briefly presented without resorting to involved mathematics. Therefore the reader is aware of what is actually the optimization of complex systems and the actual problem solving capabilities available.
Numerical Methods Using Java : For Data Science, Analysis, and Engineering
Covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. You will: Program in Java using a high-performance numerical library / Learn the mathematics for a wide range of numerical computing algorithms / Convert ideas and equations into code / Put together algorithms/ and classes to build your own engineering solution / Build solvers for industrial optimization problems / Do data analysis using basic and advanced statistics
Numerical Methods and Applications ; 6th International Conference, NMA 2006, Borovets, Bulgaria, August 20-24, 2006, Revised Papers
This book constitutes the thoroughly refereed post-proceedings of the 6th International Conference on Numerical Methods and Applications, NMA 2006. The papers are organized in topical sections on numerical methods for hyperbolic problems, robust preconditioning solution methods, Monte Carlo and quasi-Monte Carlo for diverse applications, metaheuristics for optimization problems, uncertain/control systems and reliable numerics, interpolation and quadrature processes, large-scale computations in environmental modelling, and contributed talks.
