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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.
- 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
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
- Introduction to optimization
2. Linear programming
3. Single-variable nonlinear optimization
4. Multivariable unconstrained nonlinear optimization
5. Multivariable constrained nonlinear optimization
6. Geometric programming
7. Dynamic programming
8. Integer programming
9. Multiobjective optimization
10. Nature-inspired optimization
- No. of pages:
- © Academic Press 2021
- 25th August 2020
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr Sukanta Nayak is Assistant Professor in the Department of Mathematics, at the Amrita School of Engineering in Coimbatore, India. He previously held a postdoctoral research fellowship at the University of Johannesburg, South Africa, and received his Ph.D. in mathematics from the National Institute of Technology Rourkela, in India. His research interests include numerical analysis, linear algebra, fuzzy finite element method, fuzzy heat, neutron diffusion equations, fuzzy stochastic differential equations and wavelet analysis. He has published widely in the field, including as co-author of a book entitled Interval Finite Element Method with MATLAB, for Elsevier’s Academic Press (2018).
Assistant Professor, Department of Mathematics, Amrita School of Engineering, Coimbatore, India
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