Syllabus
Optimization: need for unconstrained methods in solving constrained problems, necessary conditions of unconstrained optimization, structure methods, quadratic models, methods of line search, steepest descent method; quasi-Newton methods: DFP, BFGS, conjugate-direction methods: methods for sums of squares and nonlinear equations; linear programming: sim- plex methods, duality in linear programming, transportation problem; nonlinear programming:
Text Books
Same as Reference
References
1. An Introduction to Optimization, Chong, E. K. and Zak, S. H., 2nd Ed., Wiley India, 2001.
2. Linear and Nonlinear Programming, Luenberger, D. G. and Ye, Y., 3rd Ed., Springer 2008.
3. Mathematical Programming Techniques, Kambo, N. S., East-West Press, 1997.
Course Outcomes (COs):
CO1: Modelling of optimization problem mathematically
CO2: Understand theory of optimization
CO3: Solve optimization problems
CO4: Write programming codes for real time optimization problems