Focuses on recognizing, solving, and analyzing convex optimization problems. Convex sets, convex functions, convex and quasi-convex optimization problems. Duality theory and optimality conditions. Specific classes of problems including linear optimization (LP), semi-definite optimization (SDP), geometric programming. Algorithms for unconstrained and constrained optimization; interior-point methods. Applications in controls, communications, signal processing, statistics, and other areas.
Credit only granted for: ENEE759F or ENEE662.
Semesters OfferedFall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022