Optimization Methods
MIT's Sloan School and Department of Electrical Engineering and Computer Science cover the principal algorithms behind linear, network, discrete, nonlinear, and dynamic optimization along with optimal control. The course works through the simplex method, network flow methods, branch and bound and cutting plane techniques for discrete problems, optimality conditions for nonlinear optimization, interior point methods for convex problems, Newton's method, heuristic search techniques, and dynamic programming. Emphasis falls on the mathematical structures underlying each method rather than software packages, so students see why an algorithm works before they apply it. Materials come from MIT OpenCourseWare and include lecture notes and problem sets covering the full sequence of topics. The course assumes prior exposure to linear algebra and mathematical proof, and suits students heading toward operations research, control theory, or applied algorithm design.