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: simplex methods, duality in linear programming, transportation problem; nonlinear programming: Lagrange multiplier, KKT conditions, convex programing

Same as Reference

1. Chong, E.K.and Zak,S.H.,An Introduction to Optimization,2nd Ed.,Wiley India(2001).

2. Luenberger,D.G.and Ye,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: Impart knowledge on theory of optimization

CO3: Familiarize with algorithms to solve optimization problems

CO4: Train to write programming codes for some real time optimization problems