Metaheuristic Algorithms in Industry 4.0 (Advances in Metaheuristics)
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must...
In Mathematical Analysis and Optimization for Economists, the author aims to introduce students of economics to the power and versatility of traditional as well as contemporary methodologies in mathematics and optimization theory; and, illustrates how these techniques can be applied in solving microeconomic problems. This book combines the areas of intermediate to advanced mathematics, optimization, and microeconomic decision making, and is suitable for advanced undergraduates and first-year gr...
This second volume presents research in the field of the mathematical model operation of economic systems, again using as a basis the theory and methods of vector optimization. This volume includes three chapters.The first chapter deals with issues related to the theory of the company, modeling and decision-making, while the second deals with issues related to modeling and decision-making in market systems. The third chapter deals with issues related to modeling, forecasting and decision-making.
Sparse Polynomial Optimization: Theory And Practice (Series On Optimization And Its Applications, #0)
by Victor Magron and Jie Wang
Many applications, including computer vision, computer arithmetic, deep learning, entanglement in quantum information, graph theory and energy networks, can be successfully tackled within the framework of polynomial optimization, an emerging field with growing research efforts in the last two decades. One key advantage of these techniques is their ability to model a wide range of problems using optimization formulations. Polynomial optimization heavily relies on the moment-sums of squares (momen...
Recent Advances in Global Optimization (Princeton Legacy Library) (Princeton Series in Computer Science)
by Christodoulos a Floudas and Panos M. Pardalos
This book will present the papers delivered at the first U.S. conference devoted exclusively to global optimization and will thus provide valuable insights into the significant research on the topic that has been emerging during recent years. Held at Princeton University in May 1991, the conference brought together an interdisciplinary group of the most active developers of algorithms for global optimization in order to focus the attention of the mathematical programming community on the unsolve...
Statistical Modeling with MATLAB Calibration Models Optimization and Optimization Analysis
by Olsen F
Algorithms of the Synthesis of Optimal Regulations
by F A Aliev, V B Larin, and N I Velieva
Numerical Analysis and Optimization (Numerical Mathematics and Scientific Computation)
by Gregoire Allaire
This text, based on the author's teaching at École Polytechnique, introduces the reader to the world of mathematical modelling and numerical simulation. Covering the finite difference method; variational formulation of elliptic problems; Sobolev spaces; elliptical problems; the finite element method; Eigenvalue problems; evolution problems; optimality conditions and algorithms and methods of operational research, and including a several exercises throughout, this is an ideal text for advanced...
Constraint Programming aims at solving hard combinatorial problems, with a computation time increasing in practice exponentially. The methods are today efficient enough to solve large industrial problems, in a generic framework. However, solvers are dedicated to a single variable type: integer or real. Solving mixed problems relies on ad hoc transformations. In another field, Abstract Interpretation offers tools to prove program properties, by studying an abstraction of their concrete semantics,...
We know very little about the time-evolution of many-particle dynamical systems, the subject of our book. Even the 3-body problem has no explicit solution (we cannot solve the corresponding system of differential equations, and computer simulation indicates hopelessly chaotic behaviour). For example, what can we say about the typical time evolution of a large system starting from a stage far from equilibrium? What happens in a realistic time scale? The reader's first reaction is probably: What a...
This textbook offers a concise yet rigorous introduction to calculus of variations and optimal control theory, and is a self-contained resource for graduate students in engineering, applied mathematics, and related subjects. Designed specifically for a one-semester course, the book begins with calculus of variations, preparing the ground for optimal control. It then gives a complete proof of the maximum principle and covers key topics such as the Hamilton-Jacobi-Bellman theory of dynamic program...
Stability and Control of Large-Scale Dynamical Systems (Princeton Series in Applied Mathematics)
by Wassim M Haddad and Sergey G Nersesov
Modern complex large-scale dynamical systems exist in virtually every aspect of science and engineering, and are associated with a wide variety of physical, technological, environmental, and social phenomena, including aerospace, power, communications, and network systems, to name just a few. This book develops a general stability analysis and control design framework for nonlinear large-scale interconnected dynamical systems, and presents the most complete treatment on vector Lyapunov function...
Theorie Der Linearen Parametrischen Optimierung (Mathematische Lehrbucher Und Monographien / Abteilung 1. Mathematische Lehrbucher, #24)
by F Nozička, J Guddat, H Hollatz, and Bernd Bank
International agreements, such as those governing arms control or the environment, virtually always require some degree of verification of information, in order that compliance can be established. To ensure that the verification process can be regarded as efficient, effective and impartial, it is important to have a mathematical model of it. One can be derived by applying methods from statistics and the theory of non-cooperative games, developed in part by John Nash, who received a Nobel prize i...
Optimization and Robotic Applications
Optimisation is the process of obtaining the most appropriate solution by providing certain constraints for the given purpose or purposes. Mathematically, optimisation can be briefly defined as minimising or maximising a function. In short, optimisation is to look for the best. The best found is called "optimum. Optimisation is used to accelerate decision-making processes and to solve real-life problems in an effective, accurate and real-time manner. In addition to the economic benefits, optimis...
How to design an efficient and cost-effective logistics network? How to plan procurement, production, and transportation to meet customer demand with minimum operating costs? How to sequence jobs through machines for on-time order completion? And how to dispatch vehicles and schedule their routes to serve customers efficiently?Answers to these questions are key to effective and efficient supply chain operations. This book provides a systematic and comprehensive coverage of data-driven optimizati...
Simulation & Optimization Methods in Risk & Reliability Theory
This book introduces recent advances in the area of risk estimation in complex systems. The authors study new methods of accelerated modelling, asymptotical analysis and optimal estimating. The processes are modelled using large failure trees, the methodology of fuzzy sets, bayesians, methods of stochastic optimisation, and optimal models of equipment service and control. The authors suggest applying numerical methods for analysis of super-large failure trees having large amount of multiple vert...
Advanced Trends in Modeling, Optimization and Computing
by S.C. Lenny Koh, R. Rajesh, and K. Ganesh
Variational Methods in Nonlinear Analysis (de Gruyter Textbook)
by Dimitrios C. Kravvaritis and Athanasios N. Yannacopoulos
This well-thought-out book covers the fundamentals of nonlinear analysis, with a particular focus on variational methods and their applications. Starting from preliminaries in functional analysis, it expands in several directions such as Banach spaces, fixed point theory, nonsmooth analysis, minimax theory, variational calculus and inequalities, critical point theory, monotone, maximal monotone and pseudomonotone operators, and evolution problems.