About the book
Computational Optimization is an active and important area of study, practice and research today. It covers a wide range of applications in engineering, science and industry. It provides solutions to a variety of real life problems in a variety of disciplines of life including health, business, government, military, politics, security, education and many more. Various problems can be transformed to optimization problems and then can be solved simply, accurately and efficiently. This field is a source of revolutionizing, facilitating and enhancing the exchange of knowledge among researchers involved in both the theoretical and practical aspects. It emphasizes various topics including large scale optimization, unconstrained optimization, constrained optimization, nondifferentiable optimization, combinatorial optimization, stochastic optimization, multi-objective optimization, linear programming, quadratic programming, parametric programming, complexity theory, automatic differentiation, approximations, error analysis, sensitivity analysis, theoretical analysis, evolutionary computing, surrogate-based methods, simulated likelihood estimation, support vector machines, and others. This comprehensive reference will explore developments, methods, approaches and surveys on Computational Optimization in a wide variety of fields and endeavors. It plans to focus on optimization techniques, algorithms, analysis, applications, fields, nature of problems, and others.
This book intends to compile original and innovative findings on all aspects of issues relating to Computational Optimization. On one hand, it will cover various examples of optimization including cost, energy, profits, outputs, performance, and efficiency. On the other hand, it will handle different natures of optimization problems like nonlinearity, multimodality, discontinuity, and uncertainty.
The book is also open to address various real life applications which include but are not limited to science, engineering, industry, health, business, government, military, politics, security, education, parallel computing, distributed computing, vector processing, software, benchmarks, numerical experimentation and comparisons, modelling languages, systems for optimization, automatic differentiation, finance, optimal control, optimal design, operations research, transportation, economics, communications, manufacturing, and management science. Optimization algorithmic study will also be part of the book which may include algorithms for general-problems, algorithms for applied-problems, stochastic algorithms, deterministic algorithms, non-deterministic algorithms, evolutionary algorithms, metaheuristic algorithms. This publication will be compiled with views to provide researchers, practitioners, academicians, military professionals, government officials, and other industry professionals with an in-depth discussion of the latest advances. It will consist of various chapters in different areas of interest.