ENEE662

Convex Optimization

Reviews for this courseTestudo

Recommended: MATH410. Credit only granted for: ENEE759F or ENEE662. 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.

Fall 2025

Nuno Martins

4 reviews
Average rating: 1.75

Past Semesters

Nuno Martins

4 reviews
Average rating: 1.75

Nuno Martins

4 reviews
Average rating: 1.75

Sennur Ulukus

0 reviews
Average rating: N/A

Nuno Martins

4 reviews
Average rating: 1.75

John Baras

0 reviews
Average rating: N/A

Michael Rotkowitz

0 reviews
Average rating: N/A

Richard La

0 reviews
Average rating: N/A

During the Spring 2020 and Spring 2021 semesters, students could choose to take some of their courses pass-fail mid-semester which skews grade data aggregated across multiple semesters.

Average GPA of 3.15 between 368 students*

ENEE662 Grade Distribution+-051015202530354045% of studentsABCDFWother
A-: 18.75%
A: 18.21%
A+: 7.34%
B-: 6.25%
B: 15.22%
B+: 10.87%
C: 2.17%
C+: 2.45%
D-: 0.54%
D: 0.27%
D+: 0.54%
F: 0.54%
W: 6.25%
other: 10.6%
* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.