Design and Optimization of Energy systems


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