Fundamentals of Optimization Techniques with Algorithms

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

1. 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