Translations*

15.084J / 6.252J Nonlinear Programming

As taught in: Spring 2004

A bowl-shaped function plotted as a three-dimentional graph.

A convex function to be optimized. (Graph courtesy of Prof. Robert Freund.)

Level:

Graduate

Instructors:

Prof. Robert Freund

Course Features

Course Description

This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods and penalty and barrier methods.

Technical Requirements

Special software is required to use some of the files in this course: .rm.


*Some translations represent previous versions of courses.