MIT 15.084J / 6.252J Nonlinear Programming - Spring 2004
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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.
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: