Level:
Graduate
Instructors:
A convex function to be optimized. (Graph courtesy of Prof. Robert Freund.)
Course Features
Course Highlights
Nonlinear Programming features videos of three key lectures in their entirety. A set of comprehensive lecture notes are also available, which explains concepts with the help of equations and sample exercises.
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.