A guide to modern optimization applications and techniques in newly
emerging areas spanning optimization, data science, machine
intelligence, engineering, and computer sciences
Optimization Techniques and Applications with Examples introduces the
fundamentals of all the commonly used techniques in optimization that
encompass the broadness and diversity of the methods (traditional and
new) and algorithms. The author--a noted expert in the field--covers a
wide range of topics including mathematical foundations, optimization
formulation, optimality conditions, algorithmic complexity, linear
programming, convex optimization, and integer programming. In addition,
the book discusses artificial neural network, clustering and
classifications, constraint-handling, queueing theory, support vector
machine and multi-objective optimization, evolutionary computation,
nature-inspired algorithms and many other topics.
Designed as a practical resource, all topics are explained in detail
with step-by-step examples to show how each method works. The book's
exercises test the acquired knowledge that can be potentially applied to
real problem solving. By taking an informal approach to the subject, the
author helps readers to rapidly acquire the basic knowledge in
optimization, operational research, and applied data mining. This
important resource:
- Offers an accessible and state-of-the-art introduction to the main
optimization techniques
- Contains both traditional optimization techniques and the most current
algorithms and swarm intelligence-based techniques
- Presents a balance of theory, algorithms, and implementation
- Includes more than 100 worked examples with step-by-step explanations
Written for upper undergraduates and graduates in a standard course on
optimization, operations research and data mining, Optimization
Techniques and Applications with Examples is a highly accessible guide
to understanding the fundamentals of all the commonly used techniques in
optimization.
