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Optimization

Mathematical Modelling. Linear programing. Graphical method. Simplex method. Duality. Integer Programming. Greedy Algorithms. Dynamic Programming. Branch and Bound Algorithms. Network Optimization. Non-linear programing. Unconstrained optimization. Gradient Descent. Newton’s method. Constrained optimization. Lagrange Multipliers. KKT Optimality Conditions. Quadratic Programming. Separable Programming.

Credits : 3 (3-0-6)
Pre-Requisites : No

Course Learning Outcomes (CLOs) :
CLO 1formulate real-world problems into corresponding mathematical problems.
CLO 2apply appropriate optimization techniques to solve the formulated mathematical problems.
CLO 3apply programming skills to solve optimization problems.
CLO1CLO2CLO3
ELO 1
ELO 2
ELO 3
ELO 4
ELO 5

see ELOs, see OBE3

Revision : July 2021 (090245348)
Other Revisions : July 2020

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