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Learn more about finding a journal for publication, open access, predatory journals, your copyright as an author, social media in academics, enhancing your visbility, networking, tools for sharing and co-writing or research data management.
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Video
Convex Matrix Optimization (MOP) arises in a wide variety of applications. The last three decades have seen dramatic advances in the theory and practice of matrix optimization because of its extremely powerful modeling capability. In particular, semidefinite programming (SP) and its generalizations have been widely used to model problems in applications such as combinatorial and polynomial optimization, covariance matrix estimation, matrix completion and sensor network localization. The first part of the talk will describe the primal-dual interior-point methods (IPMs) implemented in SDPT3 for solving medium scale SP, followed by inexact IPMs (with linear systems solved by iterative solvers) for large scale SDP and discussions on their inherent limitations. The second part will present algorithmic advances for solving large scale SDP based on the proximal-point or augmented Lagrangian framework In particular, we describe the design and implementation of an augmented Lagrangian based method (called SDPNAL+) for solving SDP problems with large number of linear constraints. The last part of the talk will focus on recent advances on using a combination of local search methods and convex lifting to solve low-rank factorization models of SP problems.
Event date: 11/10/2022
Speaker: Prof. Kim-Chuan Toh (National University of Singapore)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Convex programming Semidefinite programming
- Resource Type:
- Video
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Video
We introduce a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only Hessian-vector products in two directions. Moreover; the computational overhead remains comparable to the first-order such as the gradient descent method. We show that the method has a local super-linear convergence and a global convergence rate of 0(∈-3/2) to satisfy the first-order and second-order conditions under a commonly used approximated Hessian assumption. We further show that this assumption can be removed if we perform one step of the Krylov subspace method at the end of the algorithm, which makes DRSOM the first first-order-type algorithm to achieve this complexity bound. The applicability and performance of DRSOM are exhibited by various computational experiments in logistic regression, L2-Lp minimization, sensor network localization, neural network training, and policy optimization in reinforcement learning. For neural networks, our preliminary implementation seems to gain computational advantages in terms of training accuracy and iteration complexity over state-of-the-art first-order methods including SGD and ADAM. For policy optimization, our experiments show that DRSOM compares favorably with popular policy gradient methods in terms of the effectiveness and robustness.
Event date: 19/09/2022
Speaker: Prof. Yinyu Ye (Stanford University)
Hosted by: Department of Applied Mathematics
- Subjects:
- Mathematics and Statistics
- Keywords:
- Convex programming Nonconvex programming Mathematical optimization
- Resource Type:
- Video
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Video
Academic advising has great potential in helping students learn to learn. In this activity, we will walk through the basic concepts of L2L, and provide tips on how to support students' L2L development through academic advising. This activity is designed particularly for academic advisors but everyone is welcome to join. It will be recorded and the recording will be uploaded to the Learning to Learn Website for sharing.
Event Date: 19/8/2022
Facilitator(s): Kevinia Cheung, Kenneth Tam
- Subjects:
- Lesson Design and Student Engagement
- Keywords:
- Learning
- Resource Type:
- Video
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Video
The QS Stars Reimagine Education Awards, backed by the Wharton School, describes itself as "The global awards for innovative higher education pedagogies enhancing learning and employability”. Over the past few years PolyU has enjoyed much success at these awards with multiple shortlisted and winning entries. In this workshop, recent PolyU winners will share their insights on converting great educational innovations into award-winning applications.
Event Date: 5/8/2022
Facilitator(s): Daniel Shek, Fridolin Ting, Johnny Yuen, Kai Pan Mark
- Subjects:
- Good Practices
- Keywords:
- Educational innovations
- Resource Type:
- Video
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Video
This EDC and ITS collaborative workshop series consists of two hybrid workshops about how to develop rubrics both theoretically and technically. Demonstration, discussion activities and hands-on exercises are designed for the participants to enrich their learning experience. Topics include: Introduction to rubrics Rubric policy Types of rubrics Developing rubrics and evaluating their quality How to set up rubrics in Learn@Polyu and Turnitin How to grade your students' work using rubrics in Learn@Polyu and Turnitin
Event Date: 27/7/2022
Facilitator(s): Eric Ng, Jamie Lee, Roy Kam
- Subjects:
- Assessment & Feedback
- Keywords:
- Educational tests measurements College students -- Evaluation
- Resource Type:
- Video
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Video
Join us in this online showcase that looks at assessment solutions that leverage technology to help you assess student learning outcomes. We will consider examples from PolyU and HKBU that move beyond large-hall exams and their online invigilated equivalents to offer a more holistic solution.
Event Date: 7/7/2022
Facilitator(s): Darren Harbutt, Wallace Lai, Bruce Li, Theresa Kwong
- Subjects:
- Assessment & Feedback
- Keywords:
- Educational tests measurements Web-based instruction
- Resource Type:
- Video
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Video
Earlier this year, EDC launched the Blended Learning Ambassadors initiative to promote best practices in Blended Learning across PolyU. Join us for our annual showcase event, in which three experienced practitioners share their success stories in designing and implementing Blended Learning, and put your questions to them in a panel discussion.
Event Date: 20/6/2022
Facilitator(s): Pauli Lai, Vincent Leung, Jack Chun, Darren Harbutt, Dave Gatrell
- Subjects:
- Good Practices
- Keywords:
- Teaching -- Computer network resources Computer-assisted instruction Blended learning
- Resource Type:
- Video
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Video
Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing. Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.
Event Date: 12/05/2022
Speaker: Prof. Lui Sha (University of Illinois Urbana-Champaign)
Hosted by: Graduate School
- Subjects:
- Aeronautical and Aviation Engineering and Computing
- Keywords:
- Machine learning Vehicles Remotely piloted Computer software -- Reliability Drone aircraft
- Resource Type:
- Video
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Others
A portfolio for ITC3089E showing how to present the theme and fabric
- Course related:
- ITC3089E Woven Fabric Design Project
- Subjects:
- Textiles
- Keywords:
- Weaving Textile fabrics
- Resource Type:
- Others