A convex function to be optimized. (Graph courtesy of Prof. Robert Freund.)
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.
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.
Special software is required to use some of the files in this course: .rm.
*Some translations represent previous versions of courses.