Course Details
Subject {L-T-P / C} : EE2013 : Optimization Techniques { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Asim Kumar Naskar
Syllabus
Preliminaries: Space, Subspace, Positive definite matrices, Linearity, Convex set, Convex function, Affine set. [6]
Convex optimization: Unconstrained optimizations for single and multi-variable problems, Newton step, Backtracking line search. [6]
Constrained optimization with Linear Cost: Simplex method, Active set methods, [8]
Constrained optimization with Quadretic Cost: Duality, Central path, Penalty function, Interior point methods.[8]
Heuristic Optimization: Evolutionary algorithms, Swarm optimization. [8]
Course Objectives
- Students will be competent in critically questioning and analyzing the mathematical tools used to develop different optimization methods.
- Students will know how to formulate different problems mathematically.
- Students will appreciate the necessity and difficulty of choosing between different optimization methods.
Course Outcomes
1.
Essential Reading
- S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press , 2004
- D. P. Bertsekas, Convex Optimization Theory, University Press , 2010
Supplementary Reading
- D. E. Goldberg, Genetic Algorithms in search, Optimization and Machine Learning, Pearson India , 2002
- Xin-She Yan, Nature-Inspired Optimization Algorithms, Elsevier , 2014