Course Details

Subject {L-T-P / C} : EE4602 : Optimization Techniques {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Asim Kumar Naskar


Preliminaries: Space, Subspace, Positive definite matrices, Linearity, Convex set, Convex function, Affine set. Convex optimization: Unconstrained optimizations for single and multi-variable problems, Newton step, Backtracking line search. Constrained optimization techniques for linear and quadratic problems, Simplex method, Active set methods, Duality, Central path, Penalty function, Interior point methods. Heuristic Optimization: Evolutionary algorithms, Swarm optimization.

Course Objectives

  1. Students will be competent in critical questioning and analysis of the mathematical tools used to develop different optimization methods.
  2. Students will know how to mathematically formulate different problems.
  3. Students will have an appreciation of the necessity and difficulty of making choices between different optimization methods.

Course Outcomes

At the end of the course, students will be able to formulate different convex and non-convex optimization problems and solving them by different techniques.

Essential Reading

  1. S. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press , 2004
  2. D. P. Bertsekas, Convex Optimization Theory, University Press , 2010

Supplementary Reading

  1. D. E. Goldberg, Genetic Algorithms in search, Optimization and Machine Learning, Pearson India , 2002
  2. Xin-She Yan, Nature-Inspired Optimization Algorithms, Elsevier , 2014