Lecture 1 - Introduction to Optimization

Lecture 2 - System Design and Analysis

Lecture 3 - Workable system

Lecture 4 - System simulation

Lecture 5 - Information flow diagrams

Lecture 6 - Successive substitution method

Lecture 7 - Successive substitution method (Continued.)

Lecture 8 - Successive substitution method and Newton-Raphson method

Lecture 9 - Newton-Raphson method (Continued.)

Lecture 10 - Convergence characteristics of Newton-Raphson method

Lecture 11 - Newton-Raphson method for multiple variables

Lecture 12 - Solution of system of linear equations

Lecture 13 - Introduction to Curve fitting

Lecture 14 - Example for Lagrange interpolation

Lecture 15 - Lagrange interpolation (Continued.)

Lecture 16 - Best fit

Lecture 17 - Least Square Regression

Lecture 18 - Least Square Regression (Continued.)

Lecture 19 - Least Square Regression (Continued.)

Lecture 20 - Non-linear Regression (Gauss - Newton Algorithm)

Lecture 21 - Optimization- Basic ideas

Lecture 22 - Properties of objective function and cardinal ideas in optimization

Lecture 23 - Unconstrained optimization

Lecture 24 - Constrained optimization problems

Lecture 25 - Mathematical proof of the Lagrange multiplier method

Lecture 26 - Test for Maxima / Minima

Lecture 27 - Handling in-equality constraints

Lecture 28 - Kuhn-Tucker conditions (Continued.)

Lecture 29 - Uni-modal function and search methods

Lecture 30 - Dichotomous search

Lecture 31 - Fibonacci search method

Lecture 32 - Reduction ratio of Fibonacci search method

Lecture 33 - Introduction to multi-variable optimization

Lecture 34 - The Conjugate gradient method

Lecture 35 - The Conjugate gradient method (Continued.)

Lecture 36 - Linear programming

Lecture 37 - Dynamic programming

Lecture 38 - Genetic Algorithms

Lecture 39 - Genetic Algorithms (Continued.)

Lecture 40 - Simulated Annealing and Summary