A convex function to be optimized. (Graph courtesy of Prof. Robert Freund.)
Instructor(s)
Prof. Robert Freund
MIT Course Number
15.084J / 6.252J
As Taught In
Spring 2004
Level
Graduate
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.
Other Versions
Other OCW Versions
OCW has published multiple versions of this subject. 