Course Syllabus

ELEC5470/IEDA6100A - Convex Optimization

Fall 2020-21, HKUST


In the last three decades, a number of fundamental and practical results have been obtained in the area of convex optimization theory. It is a well-developed area, both in the theoretical and practical aspects, and the engineering community has greatly benefited from these recent advances by finding key applications.

This graduate course introduces convex optimization theory and illustrates its use with many applications where convex and nonconvex formulations arise. The emphasis will be on i) the art of unveiling the hidden convexity of problems by appropriate manipulations, ii) a proper characterization of the solution either analytically or algorithmically, and iii) multiple practical ways to approach nonconvex problems.

The course follows a case-study approach by considering recent successful applications of convex optimization published within the last decade in top scientific journals in the areas of signal processing, finance, machine learning, and big data. Problems covered include portfolio optimization in financial markets, filter design, beamforming design in wireless communications, classification in machine learning, circuit design, robust designs under uncertainty, sparse optimization, low-rank optimization, graph learning from data, discrete maximum likelihood decoding, network optimization, distributed algorithms, Internet protocol design, etc.


  • Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. [pdf]
  • Daniel P. Palomar and Yonina C. Eldar, Convex Optimization in Signal Processing and Communications, Cambridge University Press, 2009.


Students are expected to have a solid background in linear algebra. They are also expected to have research experience in their particular area and be capable of reading and dissecting scientific papers.


Homework: 25% (auditors too)
Midterm:   15% (auditors too)
Class participation: 10% (Zoom video on)
Final Project: 50%  (homeworks and midterm are required to be passed)

Course Schedule

Date Week Lect Topic
7-Sep   1   1 Introduction
  2 Theory: Convex sets and convex functions
14-Sep   2   3 Theory: Convex problems and taxonomy (LP, QP, SOCP, SDP, GP)
  4 Application: Filter design
21-Sep   3   5 Theory: Algorithms primer (Newton, IPM, BCD)
  6 Application: Disciplined convex programming - CVX
28-Sep   4   7 Theory: Lagrange duality and KKT conditions
  8 Application: Waterfilling solutions
5-Oct   5   9 Application: Markowitz portfolio optimization
10 Theory&Application: Geometric programming (GP)
12-Oct   6 11 Theory&Application: MM and SCA based algorithms
12 Application: Risk parity portfolio in finance
19-Oct   7 13 Application: Sparsity via l1-norm minimization
14 Application: Sparse index tracking in finance
2-Nov   8 - Midterm -
9-Nov   9 15 Application: Classification and SVM in machine learning
16 Application: Low-rank optimization problems (Netflix, video security)
16-Nov 10 17 Application: Robust optimization with applications
18 Application: Graph learning from data
23-Nov 11 19 Application: ML decoding via SDP relaxation
20 Application: Rank-constrained SDP and multiuser downlink beamforming
30-Nov 12 21 Theory: Primal/dual decomposition techniques with applications
22 Application: The Internet as a convex optimization problem

Lecture Information

Lecture Time: Mon 18:00 – 20:50

Lecture Venue: Online via Zoom and Rm2405 during mixed-mode delivery.

Teaching Team

Instructor: Prof. Daniel P. PALOMAR (


Office: 2398 (Lifts 17/18)

Office hours: By email appointment

TAs: Rui ZHOU ( and Zé Vinícius (