Syllabus
Optimization: need for unconstrained methods in solving constrained problems, necessary con- ditions of unconstrained optimization, structure methods, quadratic models, methods of line search, steepest descent method; quasi-Newton methods: DFP, BFGS, conjugate-direction meth- ods: methods for sums of squares and nonlinear equations; linear programming: simplex meth- ods, duality in linear programming, transportation problem; nonlinear programming: Lagrange multiplier, KKT conditions, convex programing
Text Books
Same as Reference
References
1. Chong, E.K.andZak,S.H.,An Introduction to Optimization 2nd Ed.,Wiley In- dia(2001).
2. Luenberger,D.G.andYe,Y.,Linear and Nonlinear Programming,3rd Ed.,Springer(2008).
3. Kambo,N.S.,Mathematical Programming Techniques, 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