Optimization Methods for Data Science II

4 credits

Unconstrained optimization: linear least squares; randomized linear least squares; method(s) of steepest descent; line-search methods; conjugate-gradient method; comparing the efficiency of methods; randomized/stochastic methods; nonlinear least squares; norm minimization methods. Convex constrained optimization: optimality conditions; convex programming; Lagrangian relaxation; the method of multipliers; the alternating direction method of multipliers; minimizing combinations of norms. Prerequisites: DSC 123A or MATH 173A. Students may not receive credit for both DSC 123B and MATH 173B. Students who have not completed listed prerequisites may enroll with consent of instructor.