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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