END 458E - Nonlinear Optimization

Course Objectives

I. To convey the theory of unconstrained and constrained nonlinear (convex) optimization; local and global optimality, necessary and sufficient conditions,
II. To study main algorithmic approaches to unconstrained and constrained (convex) optimization,
III. To introduce global optimization.

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.