The Huawei-PolyU joint workshop on Advanced Optimization, Solver Technology, and Industry Applications

Dr Bo Bai

Information Theory Scientist and Director of Theory Lab
Huawei, Hong Kong

Time: 08:40 - 09:00

Opening and Welcome Speech

Biography
Bo Bai received the Ph.D. degree in the Department of Electronic Engineering from Tsinghua University, Beijing China, 2010. He was a Research Associate from 2010 to 2012 with the Department of ECE, HKUST. From July 2012 to January 2017, he was an Assistant Professor with the Department of Electronic Engineering, Tsinghua University. Currently, he is an Information Theory Scientist and Director of Theory Lab, Huawei, Hong Kong. His research interests include classic and post-Shannon information theory, B5G/6G mobile networking, and graph informatics. He is an IEEE Senior Member, and has authored more than 130 papers in major IEEE and ACM journals and conferences. He was a recipient of the Best Paper Award in IEEE ICC 2016.

Prof. Defeng Sun

Chair Professor of Applied Optimization and Operations Research
The Hong Kong Polytechnic University

Time: 08:40 - 09:00

Opening and Welcome Speech

Biography

Professor Defeng Sun is currently Chair Professor of Applied Optimization and Operations Research at the Hong Kong Polytechnic University and the President of the Hong Kong Mathematical Society. He mainly publishes in non-convex continuous optimization and machine learning. Together with Professor Kim-Chuan Toh and Dr Liuqin Yang, he was awarded the triennial 2018 Beale--Orchard-Hays Prize for Excellence in Computational Mathematical Programming by the Mathematical Optimization Society. He served as editor-in-chief of Asia-Pacific Journal of Operational Research from 2011 to 2013 and he now serves as associate editor of Mathematical Programming, SIAM Journal on Optimization, Journal of Optimization Theory and Applications, Journal of the Operations Research Society of China, Journal of Computational Mathematics, and Science China: Mathematics. In 2020, he was elected as a Fellow of the societies SIAM and CSIAM and in 2021 he has received the Distinguished Collaborator Award from both the Hong Kong Research Center and Huawei Noah's Ark Lab, Huawei Technologies Co. Ltd. for the contributions on developing efficient and robust techniques for solving huge scale linear programming models arising from production planning and supply chain logistics. Most recently, he was awarded the RGC Senior Research Fellow.

Prof. Teodor Gabriel Crainic

Université du Québec à Montréal

Time: 09:00 - 09:40

Title:
Network Design Methodology for Capacity and Service Planning in Freight Transportation

Abstract:
The interactions of shippers, carriers, and intermediaries yield a dense freight transportation and logistics network of requests and offers of service to move or store loads, and of the resulting multimodal service, vehicle, and terminal flows enabling the journeys of goods from origins to destinations. We focus on the consolidation-based component of the field and a set of important planning issues most stakeholders face to efficiently and profitably secure or provide the required capacity and services for their regular operations over a short-medium time horizon. We particularly discuss the modelling approaches based on network and service-network design, which address a broad range of these issues. We present a bit of network design review, some new developments and work in progress, as well as a number of research challenges and perspectives.

Biography
Teodor Gabriel Crainic is Full Professor of Operations Research, Transportation, and Logistics, and holds the Chair on Intelligent Logistics and Transportation Systems Planning in the School of Management, Université du Québec à Montréal. He is also Adjunct Professor, Department of Computer Science and Operations Research, Université de Montréal, and senior scientist at CIRRELT, the Interuniversity Research Center for Enterprise Networks, Logistics and Transportation, where he is Director of the Intelligent Transportation Systems Laboratory. Professor Crainic is a member of the Royal Society of Canada – The Academies of Arts, Humanities and Sciences of Canada. He co-founded, in 1991, the TRISTAN - TRienial Symposium on Transportation Analysis and, in 2000, the Odysseus - International Workshop on Freight Transportation and Logistics series of international meetings. He contributes to several editorial boards. He was President of the Transportation Science and Logistics Society of INFORMS, Director of the Centre for Research on Transportation (currently CIRRELT), and received the 2006 Merit Award of the Canadian Operational Research Society. The research interests of Professor Crainic are in network, integer, and combinatorial optimization, meta-heuristics, and parallel computing applied to the planning and management of complex systems, particularly in transportation and logistics. Major contributions target the design, scheduling, and management of consolidation-based carrier services, including uncertainty, resource, and revenue management considerations, as well as routing and scheduling of vehicles, Intelligent Transportation Systems, City Logistics, new business and organizational transportation and logistics models and systems, planning of urban and inter-urban multimodal multi-stakeholder freight transportation systems at all geographic scales, etc.. Professor Crainic published over 290 scientific papers and chapters, and has a h-index of 78 (Google Scholar). He co-edited the Network Design with Applications in Transportation and Logistics book published by Springer in 2021, as well as numerous special issues of major scientific journals. He supervised over 160 graduate students and postdoctoral fellows.

Prof. Kim-Chuan Toh

National University of Singapore

Time: 09:40 - 10:20

Title:
QPPAL: A Two-phase Proximal Augmented Lagrangian Method for High Dimensional Convex Quadratic Programming Problems

Abstract:
In this talk, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of linear equality and inequality constraints. In order to solve the targeted QP problems to a desired accuracy efficiently, we develop a two-phase Proximal Augmented Lagrangian method (called QPPAL), with Phase I to generate a reasonably good initial point to warm start Phase II to obtain an accurate solution efficiently. More specifically, in Phase I, based on the recently developed symmetric Gauss-Seidel (sGS) decomposition technique, we design a novel sGS based semi-proximal augmented Lagrangian method for the purpose of finding a solution of low to medium accuracy. Then, in Phase II, a proximal augmented Lagrangian algorithm is proposed to obtain a more accurate solution efficiently. Extensive numerical results evaluating the performance of QPPAL against existing state-of-the-art solvers Gurobi, OSQP and QPALM are presented to demonstrate the high efficiency and robustness of our proposed algorithm for solving various classes of large-scale convex QP problems.

Biography
Dr Toh is a Professor in the Department of Mathematics at the National University of Singapore (NUS). He obtained his BSc degree from NUS and PhD degree from Cornell University. He works extensively on convex programming, particularly large-scale matrix optimization problems such as semidefinite programming, and structured convex problems arising from machine learning and data science. He is currently serving as an Area Editor for Mathematical Programming Computation, a co-Editor for Mathematical Programming, an Associate Editor for SIAM Journal on Optimization, and ACM Transactions on Mathematical Software. He received the 2017 Farkas Prize and the 2018 triennial Beale-Orchard Hays Prize. He is a Fellow of the Society for Industrial and Applied Mathematics, and a Fellow of the Singapore National Academy of Science.

Prof. Hai Yang

The Hong Kong University of Science and Technology

Time: 10:30 - 11:10

Title:
Some Emerging Research Issues in On-demand Transportation Markets

Abstract:
Urban mobility has undergone drastic changes in recent years with the introduction of application-based taxi and car service e-hailing systems. These systems provide timely and convenient on-demand ride services to anyone, anywhere and anytime. E-hailing is now prevalent in the traditional taxi industry by effectively mitigating information asymmetry and uncertainty between customers and taxi drivers; E-hailing in the form of ride-sourcing can efficiently match a requesting customer with an affiliated private car driver nearby for on-demand ride services. This talk highlights some emerging research issues and latest developments in ride-sourcing markets, including demand forecasting; surge-pricing; matching, pricing and ride-pooling, joint optimization of matching time interval and matching radius; customers’ maximum willingness to wait with sunk waiting time; optimal resource allocation with bundled options of choice; impact of ride-pooling on traffic congestion; competition and third-party platform-integration; Pareto-efficient market regulations; and analysis of human mobility and network property with big car trajectory data.

Biography
Prof. Hai Yang is currently a Chair Professor at The Hong Kong University of Science and Technology. He is internationally known as an active scholar in the field of transportation, with more than 300 papers published in SCI/SSCI indexed journals and a SCI H-index citation rate of 68. Most of his publications appeared in leading international journals, such as Transportation Research, Transportation Science and Operations Research. Prof. Yang received a number of national and international awards, including 2020 Frank M. Masters Transportation Engineering Award and 2021 Francis C. Turner Award of American Society of Civil Engineers; National Natural Science Award bestowed by the State Council of PR China (2011). He was appointed as Chang Jiang Chair Professor of the Ministry of Education of PR China and served as the Editor-in-Chief of Transportation Research (TR) Part B: Methodological from 2013 to 2018, a prestigious journal in the field of transportation. Currently, Professor Yang serves on the Distinguished Editorial Board of TR Part B, Scientific Council of TR Part C: Emerging Technologies, and serves as an Advisory Editor of Transportation Science.

Prof. Anthony Man-Cho So

The Chinese University of Hong Kong

Time: 11:10 - 11:50

Title:
Non-Convex Orthogonal Group Synchronization via the Generalized Power Method

Abstract:
The group synchronization problem calls for the estimation of a ground-truth vector from the noisy relative transforms of its elements, where the elements come from a group and the relative transforms are computed using the binary operation of the group. Such a problem provides an abstraction of a wide range of inverse problems that arise in practice. However, in many instances, one needs to tackle a non-convex optimization formulation. It turns out that for synchronization problems over certain subgroups of the orthogonal group, a simple projected gradient-type algorithm, often referred to as the generalized power method (GPM), is quite effective in finding the ground-truth when applied to their non-convex formulations. In this talk, we survey the related recent results in the literature and focus in particular on the techniques for analyzing the statistical and optimization performance of the GPM. This talk covers joint works with Huikang Liu, Jinxin Wang, Man-Chung Yue, and Linglingzhi Zhu.

Biography
Anthony Man-Cho So received his BSE degree in Computer Science from Princeton University with minors in Applied and Computational Mathematics, Engineering and Management Systems, and German Language and Culture. He then received his MSc degree in Computer Science and his PhD degree in Computer Science with a PhD minor in Mathematics from Stanford University. Dr So joined The Chinese University of Hong Kong (CUHK) in 2007. He is currently Dean of the Graduate School, Deputy Master of Morningside College, and Professor in the Department of Systems Engineering and Engineering Management. His research focuses on optimization theory and its applications in various areas of science and engineering, including computational geometry, machine learning, signal processing, and statistics. Dr So is appointed as an Outstanding Fellow of the Faculty of Engineering at CUHK in 2019. He currently serves on the editorial boards of Journal of Global Optimization, Mathematical Programming, Optimization Methods and Software, and SIAM Journal on Optimization. He has also served as the Lead Guest Editor of IEEE Signal Processing Magazine Special Issue on Non-Convex Optimization for Signal Processing and Machine Learning. Dr So has received a number of research and teaching awards, including the 2018 IEEE Signal Processing Society Best Paper Award, the 2015 IEEE Signal Processing Society Signal Processing Magazine Best Paper Award, the 2014 IEEE Communications Society Asia-Pacific Outstanding Paper Award, and the 2010 Institute for Operations Research and the Management Sciences (INFORMS) Optimization Society Optimization Prize for Young Researchers, as well as the 2022 University Grants Committee (UGC) Teaching Award, the 2022 University Education Award, and the 2013 CUHK Vice-Chancellor’s Exemplary Teaching Award. He also co-authored with his student a paper that receives the Best Student Paper Award at the 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2018).

Dr Fan Zhang

Huawei

Time: 11:50 - 12:30

Title: Several Challenging Practical and Theoretical Problems in Large Scale Optimizations

Abstract:
Large scale optimization has played an important role in practical information and communication technology (ICT) scenarios. Even though most of the practical optimizations are linear programming or mixed integer programming, the problem size could be over one million to even one billion, which makes such optimizations very challenging under the stringent requirement of solution time. In this talk, practical ICT scenarios covering optimizations in data communication and optical networks will be covered and several theoretical and practical problems will be introduced. The problems are from the following three perspectives: routing optimization under practical limitations, AI-aided optimization, and exploration of MILP lower bound. This talk serves as a platform to announce these open questions to public and we welcome anyone who is interested to work with us to conquer the challenges.

Biography
Fan Zhang (Michael) is currently with Hong Kong Theory Lab, Huawei Hong Kong Research Center as an optimization expert. He received the BEng (first class Hons) degree from Chu Kochen Honors College, Zhejiang University (ZJU), in 2010, and the Ph.D. degree from the Hong Kong University of Science and Technology (HKUST), in 2015. He joined Huawei Future Network Theory lab as a researcher in 2015 and is an optimization expert in Theory Lab since 2022. His research interest covers a wide range including large-scale optimization techniques, operational research with applications to networking problems, low complexity stochastic optimization, and networked control theory. Michael is now leading a diversified team with people from different backgrounds working on challenging and hardcore optimization problems originated from practical scenarios such as wide area network, optical network, wireless network, and data storage systems. He and his team has gained great experiences in designing customized algorithms for efficiently solving practical large-scale LPs/ILPs with number of variables or constraints to be over 1 million to billion. Some research outputs directly translate to solving practical problems, leading to increasing revenue or reducing costs, while some contribute to the development of Huawei’s generic solver (OptVerse) to enhance its performance.

Prof. Zaiwen Wen

Peking University

Time: 14:30 - 15:10

Title:
On the Convergence Analysis of Variational Monte Carlo Methods

Abstract:
The variational Monte Carlo (VMC) method is very promising for solving many-body quantum problems at a manageable cost. In this talk, we provide a rigorous analysis of the VMC method by showing the convergence of the stochastic gradient method with respect to the number of iterations and the cost of sampling.

Biography
Zaiwen Wen, Professor at Beijing International Center For Mathematical Research, Peking University. He mainly studies optimization algorithms and theory and their applications in machine learning. He was awarded the China Youth Science and Technology Award in 2016 and Beijing Outstanding Youth Zhongguancun Award in 2020. He was funded by the National Ten Thousand Talents Program for Science and Technology Innovation. He is an associate editor of "Communications in Mathematics and Statistics", "Journal of the Operations Research Society of China", "Journal of Computational Mathematics" and a technical editor of "Mathematical Programming Computation".

Prof. Jianfeng Cai

The Hong Kong University of Science and Technology

Time: 15:10 - 15:50

Title:
Unifying Non-Convex Low-Rank Matrix Recovery Algorithms by Riemannian Gradient Descent

Abstract:
The problem of low-rank matrix recovery from linear samples arises from numerous practical applications in machine learning, imaging, signal processing, computer vision, etc. Non-convex algorithms are usually very efficient and effective for low-rank matrix recovery with a theoretical guarantee, despite possible local minima. In this talk, non-convex low-rank matrix recovery algorithms are unified under the framework of Riemannian gradient descent. We show that many popular non-convex low-rank matrix recovery algorithms are special cases of Riemannian gradient descent with different Riemannian metrics and retraction operators. Moreover, we identify the best choice of metrics and construct the most efficient non-convex algorithms for low-rank matrix recovery, by considering the properties of sampling operators for different tasks such as matrix completion and phase retrieval.

Biography
Jianfeng Cai is a professor in the Department of Mathematics at HKUST. He received his B.Sc. and M.Sc. degrees from Fudan in 2000 and 2007 respectively, and his Ph.D. degree from CUHK in 2007. After his Ph.D. study, he worked at National University of Singapore, UCLA, and University of Iowa before settling down at HKUST. His research interest is in algorithmic and theoretical foundations of signals, images, and data. He was a Highly Cited Research in mathematics in 2017 and 2018.

Dr Dong Zhang

Huawei

Time: 16:00 - 16:40

Title: Challenging Optimization Problems in GTS: Non-convex and 0-1 mixed integer programming problems

Abstract:
As 5G is rapidly developing, telecom carriers and service provides wish to improve their efficiency/service performance, to be more competitive in the market. Optimization is a key technology to achieve this goal. In this talk, I will first introduce several typical optimization scenarios in GTS, such as 5G station base station location selection, IP+Fiber network planning and optimization, on-site staff scheduling and so on. Second, I will briefly talk about our preliminary attempt to solve these problems. Finally, I will discuss some challenges recognized in these scenarios.

Biography
He received his Ph.D. degree from University of Hong Kong. His primary research interests include transportation/telecom network optimization and planning, scheduling and heuristic algorithm. He is working in IP+Fiber network optimization, Topology design optimization, FME routing, inventory optimization, parameter optimization optimization and so on.

Dr Julian Hall

University of Edinburgh

Time: 16:40 - 17:20

Title:
HiGHS: Open-source Linear Optimization Software for the World

Abstract:
HiGHS is open-source optimization software for linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP). This talk will give an insight into the state-of-the-art techniques underlying the LP solvers, and an overview of the MIP and QP solvers. Independent benchmark results will be given to justify the claim that HiGHS is the world’s best open-source linear optimization software. For a very small team to turn high performance solvers into software that is accessible to as many people and institutions as possible is a big challenge. For HiGHS, this has been achieved by careful prioritisation of activities, and this strategy will be discussed with reference to the modelling and programming language interfaces to HiGHS that have been developed. The talk will close with an overview of plans for future work.

Biography
Born in 1964, Julian studied Mathematics at New College Oxford then Numerical Analysis and Optimization at the University of Dundee under the supervision of Roger Fletcher FRS, and has been employed as a lecturer in the School of Mathematics at the University of Edinburgh since 1990. His main research interest is in developing algorithmic and computational techniques for solving large scale linear programming (LP) problems on both serial and parallel computers using the revised simplex method. More recently, in collaboration with several young researchers, he is managing the development of the world's best open-source software linear optimization software, HiGHS.

Prof. Dr. Thorsten Koch

Technische Universität Berlin and Zuse Institute Berlin

Time: 17:20 - 18:00

Title:
Solving QUBOs on Digital and Quantum Computers

Abstract:
It is regularly claimed that quantum computers will bring breakthrough progress in solving challenging combinatorial optimization problems relevant in practice. In particular, Quadratic Unconstraint Binary Optimization (QUBO) problems are said to be the model of choice for use in (adiabatic) quantum systems. Combinatorial Optimization searches for an optimum object in a finite but usually vast collection of objects. This can be utilized for many practical purposes, like efficient allocation of limited resources, network planning, and hundreds of other applications in almost all fields, e.g., finance, production, scheduling, and inventory control. However, many combinatorial optimization problems are known to be NP-hard, often translated simplistically as "intractable." The practical side looks a bit different though. In many cases it is well possible to solve such problems even to proven global optimality. In this talk we will explain some of the meaning and implications, review the state of affairs, the potential of quantum computing, and give new computational results regarding solution of OUBOs.

Biography
Prof. Dr Thorsten Koch is Professor for Software and Algorithms for Discrete Optimization at TU-Berlin and head of the Applied Algorithmic Intelligence Methods and the Digital Data and Information for Society, Science, and Culture departments at the Zuse Institute Berlin (ZIB). He has worked in several areas, especially the planning of infrastructure networks, chip verification, mathematics education and integer optimization. From 2008-2014 he was the coordinator of the FORNE project, an industry collaboration project regarding gas transportation involving five universities and two research institutes. The project received the 2016 EURO Excellence in Practice Award of the European OR Society. From 2013-2019 he was head of the GasLab and the SynLab within the Research Campus MODAL (Mathematical Optimization and Data Analysis Laboratory). The project Optimized Execution of Dispatching conducted together with Germanys largest Gas Transmission System Operator became finalist of the 2020 INFORMS Innovative Applications in Analytics Award. Currently, the work is focused on developing high-performance parallel methods for solving large-scale structured optimization problems. Such problems arise, for example, in data-driven, real-world analysis and planning of sustainable network infrastructures. This includes high-performance solvers for Steiner Tree Problems in Graphs (STPG) and Quadratic Unconstraint Binary Optimization (QUBO).