Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book can help overcome the widely observed math-phobia and math-aversion among undergraduate students in these subjects. The book can also help them understand why they have to learn different mathematical techniques, how they can be applied, and how they will equip the students in their further studies.?The book provides a thorough but lucid exposition of most of the mathematical techniques applied in the fields of economics, business and finance. The book deals with topics right from high school mathematics to relatively advanced areas of integral calculus covering in t

Economics --- Programming (Mathematics) --- Mathematical models.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Computer science --- Constraint programming (Computer science) --- Mathematical optimization --- Programming (Mathematics) --- Mathematical models

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines

Mathematical optimization. --- Heuristic programming. --- Problem solving --- Computer algorithms. --- Data processing. --- Algorithms --- Artificial intelligence --- Programming (Mathematics) --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines including mathematics, operations research, engineering, and economics. The volume deals with constrained and unconstrained problems and puts a special emphasis on large scale problems. It also introduces a new unified concept for unconstrained, constrained, vector, and stochastic global optimization problems. All methods presented are illustrated by various examples. Practical numerical algorithms are given and analyzed in detail.   The topics presented include the randomized curve of steepest descent, the randomized curve of dominated points, the semi-implicit Euler method, the penalty approach, and active set strategies. The optimal decoding of block codes in digital communications is worked out as a case study and shows the potential and high practical relevance of this new approach.   Global Optimization: A Stochastic Approach is an elegant account of a refined theory, suitable for researchers and graduate students interested in global optimization and its applications.

Mathematical optimization. --- Nonlinear programming. --- Programming (Mathematics). --- Stochastic processes. --- Mathematical optimization --- Stochastic processes --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Random processes --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematics. --- Operations research. --- Management science. --- Optimization. --- Operations Research, Management Science. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Probabilities --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear relationships involving the decision variables that have a pronounced effect on the set of feasible and optimal solutions. Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables. MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.

Integer programming. --- Mathematical optimization. --- Nonlinear programming. --- Nonlinear programming --- Integer programming --- Mathematical optimization --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Mathematics. --- Approximation theory. --- Algorithms. --- Approximations and Expansions. --- Continuous Optimization. --- Programming (Mathematics) --- Math --- Science --- Algorism --- Algebra --- Arithmetic --- Foundations --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Theory of approximation --- Functional analysis --- Functions --- Polynomials --- Chebyshev systems