Courses
  /  
Descriptions
IEMS 450-2: Mathematical Optimization II

Quarter Offered

Winter : MW 2-3:30 ; Waechter

Prerequisites

Linear Algebra and Calculus, Basic programming skills in Matlab or Python

Description

  • Algorithms and theory for unconstrained and constrained optimization

Learning Objectives

  • Students will learn about the most common numerical optimization algorithms for solving smooth unconstrained and constrained optimization problems.  They will understand the theoretical foundation and convergence properties of these methods, and in programming assignments they will learn how to implement the methods.

 Topics

  • Optimality conditions for unconstrained and constrained optimization problems
  • Line search and trust region methods
  • Newton and quasi-Newton methods
  • Conjugate gradient method
  • Active set methods for quadratic programming
  • augmented Lagrangian methods, sequential quadratic programming methods, and interior point methods for nonlinear constrained optimization
  • Convergence properties of algorithms

 Materials

  • Textbook: Numerical Optimization, 2nd Edition, by J. Nocedal and S. Wright, Springer Verlag, 2006.

prerequisites

  •  Linear Algebra and Calculus
  • Basic programming skills in Matlab or Python