Optimization Techniques and Applications with Examples

This book introduces the fundamentals of all the commonly used techniques  in optimization so that readers can see the broadness and diversity of the methods and algorithms and sample the flavor of each method (both traditional and new). Topics covered include mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. The author also discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization. Evolutionary computation, nature-inspired algorithms and other topics are also covered. Each topic is explained in detail with step-by-step examples to show how each method works. Exercises are also provided to test the acquired knowledge and potentially apply to real problem solving. This book uses an informal approach and helps readers rapidly acquire the basic knowledge in optimization, operational research, and applied data mining.