Optimization Techniques and Applications with Examples
1. Edition November 2018
384 Pages, Hardcover
Wiley & Sons Ltd
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.
1 Mathematical Foundations 3
2 Algorithms, Complexity and Convexity 39
PART II OPTIMISATION TECHNIQUES AND ALGORITHMS
3 Optimization 67
4 Constrained Optimization 89
5 Optimization Techniques: Approximation Methods 105
PART III APPLIED OPTIMIZATION
6 Linear Programming 129
7 Integer Programming 143
8 Regression and Regularization 167
9 Machine Learning Algorithms 201
10 Queueing Theory and Simulation 229
PART IV MULTIOBJECTIVE OPTIMIZATION
11 Multiobjective Optimization 253
12 Constraint Handling Techniques 271
PART V EVOLUTIONARY COMPUTATION AND NATUREINSPIRED ALGORITHMS
13 Evolutionary Algorithms 283
14 Nature-Inspired Algorithms 299
