Computation and Optimization Methods in Statistics

3 credits · Graduate credit

This course is an introduction to mathematical optimization with emphasis on theory and algorithms relevant to statistical practice. The course covers algorithms for large-scale matrix computations, majorization-minimization methods, Newton-type methods, and stochastic approximation. It also covers optimality conditions, Legrange duality for convex optimization problems , and convergence analysis.