CUI, Ying - HKUST Class Schedule & Quota

Class Schedule & Quota
2026-27 Fall 2025-26 Summer 2025-26 Spring 2025-26 Winter
2026-27 Fall
Common Core Course
The class information on this page will normally be updated every 15 to 20 minutes during class enrollment period.
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AIAA AMAT BSBE CMAA CNCC CNGF DLED DSAA EOAS FTEC FUNH INFH INTR IOTA IPEN LANG MICS MOES MSSM PDEV PLED ROAS SEEN SMMG SOCH SYSH UCMP UCUG UFUG UGOD

COURSE INFO

DESCRIPTION	This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

DESCRIPTION

This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

IOTA 5501 - Convex and Nonconvex Optimization I (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6390)	Mo 01:30PM - 04:20PM	Rm 202, E1	CUI, Ying	40	17	23	0

COURSE INFO

PREVIOUS CODE	IIMP 6010
DESCRIPTION	This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems. May be graded PP.

UCMP 6010 - Cross-disciplinary Research Methods I (2 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6706)	TBA	TBA	CHI, Jinshu CUI, Ying JOHNSON, SHANE MIGUEL LI, Lei SHEN, Yang TAN, Chee Keong YUE, Liang ZHAO, Hang ZHAO, Wufan	350	400	0	0

COURSE INFO

PRE-REQUISITE	(UFUG 1103 OR UFUG1106) AND UFUG 2101 AND UFUG 2103
DESCRIPTION	This is an introductory-level course on optimization. Topics include convex sets and functions, optimization problems, convex problems, duality, numerical linear algebra, gradient methods, Newton's methods, conjugate gradient method, gradient projection method, block coordinate descent method, penalty methods, interior-point methods, software, and applications. Students learn to analyze and solve optimization problems theoretically and numerically using appropriate methods.

PRE-REQUISITE

(UFUG 1103 OR UFUG1106) AND UFUG 2101 AND UFUG 2103

DESCRIPTION

This is an introductory-level course on optimization. Topics include convex sets and functions, optimization problems, convex problems, duality, numerical linear algebra, gradient methods, Newton's methods, conjugate gradient method, gradient projection method, block coordinate descent method, penalty methods, interior-point methods, software, and applications. Students learn to analyze and solve optimization problems theoretically and numerically using appropriate methods.

UFUG 2105 - Optimization (3 units)

Section	Date & Time	Room	Instructor	Quota	Enrol	Avail	Wait	Remarks
L01 (6854)	Tu 12:00PM - 02:50PM	Rm 233, W1	CUI, Ying	40	6	34	0
T01 (6855)	Th 06:30PM - 07:20PM	Rm 102, E1	CUI, Ying	40	6	34	0