Nonlinear Programming

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

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


MIT Course Number

15.084J / 6.252J

As Taught In

Spring 2004



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Course Description

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.

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Related Content

Robert Freund. 15.084J Nonlinear Programming, Spring 2004. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed). License: Creative Commons BY-NC-SA

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