Nonlinear Optimization
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.
Spring 2025
This course offers a 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. The curriculum covers convex analysis, Lagrangian relaxation, and nondifferentiable optimization, as well as applications in integer programming. It provides a comprehensive treatment of optimality conditions and Lagrange multipliers. The course also utilizes a geometric approach to duality theory. Finally, applications are drawn from control, communications, machine learning, and resource allocation problems.
Prerequisites & course information
Same subject as 15.084[J] Prereq: 18.06 and ( 18.100A , 18.100B , or 18.100Q ) G (Spring) 4-0-8 units
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2025
- MIT OCW syllabus ↗
- MIT OCW calendar ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.