6.255[J] Optimization Methods
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.
6.255[J] will be offered this semester (Fall 2018). It is instructed by S. Shtern.
This class counts for a total of 12 credits. This is a graduate-level class.
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