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.

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 Homepage: 15.084J / 6.252J Nonlinear Programming Spring 2004

Course features at MIT OpenCourseWare page:

• Syllabus
• Calendar
• Readings
• Lecture Notes
• Recitations
• Exams
• Download Course Materials