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Course Weekly Lecture Plan

Week Topic
1 Introduction to optimization: Definitions, terminology, formulation and Classification of optimization problems and other basic concepts.
2 Review of mathematical modelling concepts: Degrees of freedom analysis, building mathematical models
3 Analytical techniques: classical optimization theory, necessary and sufficient conditions, Hessian matrix, determinant and eigenvalue analyses of Hessian matrix
4 Unconstrained one-dimensional optimization, numerical methods, region-elimination methods, sequential search, dichotomous search
5 Line-search without using derivatives, Golden-Section search, Fibonacci search, Line-search using derivatives, bisecting search, Newton and Secant methods
6 Unconstrained multivariable optimization: Line search in multidimensional search, Multidimensional search without using derivatives, Hooke-Jeeves method, Powell’s cojugate method
7 Multi-dimensional search using derivatives: steepest descent, Newton, Marquardt methods and Conjugate Direction methods
8 Constrained multivariable optimization, Linear Programming, graphical solution, simplex method
9 Constrained multivariable optimization, Linear Programming, graphical solution, simplex method
10 Mixed Integer Linear Programming
Dynamic Programming, Integer and Mixed Integer Programming
11 Linear Programming Big M method
12 Basics of nonlinear optimization
Parameter estimation: Linear/Nonlinear Regression
13 Basics of nonlinear optimization
Parameter estimation: Linear/Nonlinear Regression
14 Parameter estimation: Linear/Nonlinear Regression
15 Optimization in Chemical Industry
 
 
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