15 084J - Nonlinear Optimization

Description
Prereq: 18.06 and (18.100A, 18.100B, or 18.100Q). Unified analytical and computational approach to nonlinear optimization problems. Unconstrained optimization methods include gradient, conjugate direction, Newton, sub-gradient and first-order methods. Constrained optimization methods include feasible directions, projection, interior point methods, and Lagrange multiplier methods. Convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. Comprehensive treatment of optimality conditions and Lagrange multipliers. Geometric approach to duality theory. Applications drawn from control, communications, machine learning, and resource allocation problems. Final examination.
Credits
12
Recent Professors
Schedule Planner
Recent Semesters
Spring 2020
Offered
TuTh, F
Avg. Sections
3