Fundamentals of Optimization Techniques with Algorithms
Auteur : Nayak Sukanta
Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice.
Researchers and postgraduate students in mechanical engineering, electrical engineering, electronics, computer science, aerospace engineering, and related fields; Researchers and postgraduate students in mathematics; applied mathematics; and industrial mathematics.
- Presents optimization techniques clearly, including worked-out examples, from traditional to advanced
- Maps out the relations between optimization and other mathematical topics and disciplines
- Provides systematic coverage of algorithms to facilitate computer coding
- Gives MATLAB© codes in relation to optimization techniques and their use in computer-aided design
- Presents nature-inspired optimization techniques including genetic algorithms and artificial neural networks
Date de parution : 08-2020
Ouvrage de 320 p.
15x22.8 cm
Thèmes de Fundamentals of Optimization Techniques with Algorithms :
Mots-clés :
Active constraint; Algorithm; Artificial variable technique; Boundary value problem; Bounded objective function; Branch and bound method; Cardinal information; Constraints; Convex function; Crossover; Degree of difficulty; Descent; Design variables; Direct search; Dual simplex method; Duality; Dynamic programming; Feasible direction; Feasible region; Feed-forward neural network; Feedback neural network; Final value problem; Gomory’; s cutting plane method; Gomory’; s mixed-integer programming; Gradient; Gradient search; Graphical method; Hessian matrix; Hypercube; Initial value problem; Integer nonlinear programming; Integer programming problems; Minimization; Mutation; Nondominated solution; Normality; Objective function; Optimality; Orthogonality; Pareto optimal solutions; Penalty function; Pheromone; Posynomial; Potential energy surface; Region elimination; Sampling; Selection; Simplex; Simplex method; Swarm intelligence; Tabulation; Taylor series; Taylor’; s series; Unconstrained optimization; Unimodal; Utility function; Variable bounds