Subject Code: MA7L002	Name: Advanced Techniques in Operation Research	L-T-P: 3-1-0	Credit: 4
One variable unconstrained optimization, multivariable unconstrained optimisation, Karush-Kuhn-Tucker (KKT) conditions for constrained optimization, quadratic programming, separable programming, convex and non convex programming, steepest and Quasi-Newton method. Dynamic Programming: Characteristics of dynamic problems, deterministic dynamic programming and probabilitistic dynamic programming, Network analysis, Shortest path problems, minimum spanning tree problem, maximum flow problem, minimum cost flow problem, network simplex, interior point methods, stochastic programming, Nonlinear goal programming applications, Geometric Programming. Multi-objective Optimization Problems: Linear and non linear programming problems, Weighting and Epsilon method, P-norm methods, Gradient Projection Method, STEM method, Convex Optimization. Text Book: S.S. Rao, Engineering Optimization Theory and Practices, John Wiley and Sons, 2009 Reference Books: M. Ehrgott, Multi-criteria Optimization, Springer 2006 K.M, Miettien, Non-linear multi-objective optimization, Kluwers International Series, 2004 K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, 2001.

Subject Code: MA7L002

Name: Advanced Techniques in Operation Research

L-T-P: 3-1-0

Credit: 4

One variable unconstrained optimization, multivariable unconstrained optimisation, Karush-Kuhn-Tucker (KKT) conditions for constrained optimization, quadratic programming, separable programming, convex and non convex programming, steepest and Quasi-Newton method.
Dynamic Programming: Characteristics of dynamic problems, deterministic dynamic programming and probabilitistic dynamic programming, Network analysis, Shortest path
problems, minimum spanning tree problem, maximum flow problem, minimum cost flow problem, network simplex, interior point methods, stochastic programming, Nonlinear goal
programming applications, Geometric Programming.
Multi-objective Optimization Problems: Linear and non linear programming problems, Weighting and Epsilon method, P-norm methods, Gradient Projection Method, STEM
method, Convex Optimization.

Text Book:

S.S. Rao, Engineering Optimization Theory and Practices, John Wiley and Sons, 2009

Reference Books:

M. Ehrgott, Multi-criteria Optimization, Springer 2006
K.M, Miettien, Non-linear multi-objective optimization, Kluwers International Series, 2004
K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, 2001.