6.215 Optimization Methods


Class Info

Introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. Emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton's method, heuristic methods, and dynamic programming and optimal control methods. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.

This class has 18.06 as a prerequisite.

6.215 will be offered this semester (Fall 2017). It is instructed by D. Bertsimas and P. Parrilo.

This class counts for a total of 12 credits.

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