Nonlinear Programming
A graduate-level MIT course on nonlinear optimization theory and methods, offered through MIT OpenCourseWare and cross-listed between the Sloan School of Management and Electrical Engineering and Computer Science. Topics cover unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods include steepest descent, Newton's method, conditional gradient and subgradient optimization, and penalty and barrier methods. Materials include lecture notes, problem sets, and exams as published on OCW, free to access under a Creative Commons license with no certificate offered. The course assumes prior exposure to linear algebra and optimization basics and builds toward the algorithmic and theoretical tools used in modern optimization research and applications in engineering and finance.