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Optimization Methods for Systems and Control


Credit Hours:


Learning Objective:

  • Use various methods to compute minimum or maximum of nonlinear functions of many variables
  • Solve linear programming problems
  • Find minimum or maximum of nonlinear functions of many variables using population-based methods
  • Apply various optimization methods learned in the course to real-life design problems


This course provides an introduction to various methods of obtaining the extremum (minimum or maximum) of a non-dynamical system and the use of these methods in real-life applications. Computational methods for nonlinear optimization; unconstrained optimization. Constrained optimization; linear programming; simplex method for solving linear programs; Lagrange's conditions, the Karush-Kuhn-Tucker (KKT) conditions, Least squares, Convex optimization, Global optimization methods: Genetic algorithms and Particle swarm optimization (PSO) method.

Topics Covered:


MA 511 or equivalent (first graduate course in linear algebra) Linear algebra, calculus of several variables. In particular: matrix manipulation, linear spaces, quadratic forms, tangent planes. Elements of multivariable calculus, in particular, differentiation of real-valued functions of n variables, gradients, and the chain rule.

Applied / Theory:

30 / 70


Approximately 5 homework assignments




Two midterm exams; one final exam.


An Introduction to Optimization by E. K. P. Chong and S. H. Zak (4th edition), published by Wiley & Sons, Inc., New York 2013. Full text available online to Purdue students through Purdue Libraries.

Computer Requirements:

ProEd minimum computer requirements. Access to MATLAB or the Student Edition of MATLAB (registered students can access via Purdue GoRemote--

Other Requirements:


ProEd Minimum Requirements: