Course Meeting Times
Lectures: 2 sessions / week, 1.5 hours / session
Recitations: alternating weeks, 1 hour / session
A unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, and quasi-Newton methods. Constrained optimization methods include feasible directions, projection, interior point, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Applications drawn from control, communications, power systems, and resource allocation problems.
Bertsekas, Dimitri. Nonlinear Programming: 2nd Edition. Belmont, MA: Athena Scientific Press, 1999. ISBN: 1886529000.
In-class midterm (30%)
3-hour final (40%)
Problem Sets (30%)