Optimization ((3 Units))

Catalog description

Prerequisites: [ MATH 223 , MATH 312 , and STAT 461 ] with grades of C- or better. Topics include convexity, convex optimization problems, Lagrangian duality, optimality conditions and optimization in machine learning. Algorithmic topics will include the gradient descent and its variants, Newton’s and quasi-Newton methods. Applications will emphasize topics in data science.