Optimization Methods For Engineers

By R.V.S. Raju more
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Product Specifications

Publisher PHI Learning All Mechanical Engineering books by PHI Learning
ISBN 9788120347441
Author: R.V.S. Raju
Number of Pages 616
Available in all digital devices
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  • About the book

About The Book Optimization Methods For Engineers
Book Summary:

Primarily designed as a text for the postgraduate students of mechanical engineering and related branches, it provides an excellent introduction to optimization methodsthe overview, the history, and the development. It is equally suitable for the undergraduate students for their electives. The text then moves on to familiarize the students with the formulation of optimization problems, graphical solutions, analytical methods of nonlinear optimization, classical optimization techniques, single variable (one-dimensional) unconstrained optimization, multidimensional problems, constrained optimization, equality and inequality constraints.

With complexities of human life, the importance of optimization techniques as a tool has increased manifold. The application of optimization techniques creates an efficient, effective and a better life.


Includes numerous illustrations and unsolved problems.
Contains university questions.
Discusses the topics with step-by-step procedures.

Table of Contents:

Preface Acknowledgements

1. OptimizationAn Overview

2. Formulation of Optimization Problems

3. Solutions by Graphical Methods for Optimization Problems

4. Nonlinear Programming Problems: Classical Optimization Techniques and Basic Concepts

5. Analytical One-dimensional (Single Variable) Unconstrained Optimization

6. Analytical Multidimensional (Multivariable) Unconstrained Optimization

7. Analytical Multidimensional Optimization with Equality Constraints

8. Analytical Multidimensional Optimization with Inequality Constraints

9. Numerical Methods for One-dimensional Nonlinear Programming

10. Numerical Methods for Unconstrained Optimization of Multivariate Nonlinear Programming Problem

11. Constrained Optimization Techniques for Nonlinear Programming Problems

12. Pivotal Reduction Method for Linear Programming Problems

13. Simplex Method for Linear Programming Problems

14. Regeneracy and Duality in Simplex

15. Dynamic Programming I

16. Dynamic Programming II

17. Simulation

18. Monte Carlo Simulation (Simulation II)

Quiz/Objective Type Questions



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