An introduction to the modern theory of convex programming, its extensions and applications. Structure of convex sets, separation and support, set-valued analysis, subgradient calculus for convex functions, Fenchel conjugacy and duality. Lagrange multipliers, minimax theory. Algorithms for nondifferentiable optimization. Lipschitz functions, tangent cones and generalized derivatives, introductory non-smooth analysis and optimization. [Offered: F] Prereq: (CO 255/355 or 367/CM 442), (AMATH/PMATH 331 or PMATH 351); Cumulative overall average of at least 80%; Not open to General Mathematics students
