Syllabus
Introduction – Formulation of optimization problems – Linear programming – duality - Non- linear programming – unconstrained optimization: optimality conditions, range elimination methods, gradient method, quasi-newton method, conjugate gradient method – Constrained optimization: Lagrange multiplier theorem, Kuhn Tucker condition, penalty function methods, projected gradient methods, Quadratic programming, sequential quadratic programming – Non-traditional optimization techniques for single and multi-objective optimization – Applications in Engineering.
Text Books
Same as Reference
References
1. S.S. Rao, Engineering Optimization: Theory and Practice, John Wiley and sons, 4th edition 2009.
2. E. K. P. Chong and S. H. Zak, An introduction to optimization, Wiley publishers, 2017. 18
3. H.A. Taha, Operations Research: An Introduction", Pearson, 10th edition, 2016.
4. K. Deb, Optimization for Engineering Design: Algorithms and Examples, Prentice-Hall of India 2012.
5. K. Deb, Multi-objective optimization using Evolutionary Algorithms, Wiley, 2010.
Course Outcomes (COs):
CO1: Apply fundamental concepts of mathematics to formulate an optimization problem.
CO2: Analyze and solve general linear programming problems.
CO3: Analyze and solve constrained and unconstrained non-linear programming problems in single-variable as well as multi-variable.
CO4: Implement computer codes for mathematical as well as non-traditional techniques for various optimization problems and analyze the results.