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X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART;TZID=Asia/Singapore:20210115T100000
DTEND;TZID=Asia/Singapore:20210116T110000
DTSTAMP:20260504T120351
CREATED:20210112T090755Z
LAST-MODIFIED:20210303T030021Z
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SUMMARY:IORA Seminar Series | 15 Jan | 10am
DESCRIPTION:Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]   \n  \n\n\n\nName of Speaker\n  Prof Toh Kim Chuan  \n\n\nSchedule \n  Friday 15 January 2021 \, 10am  \n\n\nLink \nhttps://nus-sg.zoom.us/j/83515146165?pwd=eUpLZm5NWSs0RUpxTU5jV3JTeFQ5UT09\n\n\nID\n835 1514 6165\n\n\nPassword\n700968\n\n\nTitle \n  An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems  \n\n\nAbstract \n Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]  \n\n\nAbout the Speaker\nKim–Chuan Toh is a Professor at the Department of Mathematics\, National University of Singapore (NUS). He obtained his BSc degree in Mathematics from NUS and PhD degree in Applied Mathematics from Cornell University.   His current research focuses on designing efficient algorithms and software for convex programming and its applications\, particularly large–scale optimization problems arising from data science/machine learning\, and large–scale matrix optimization problems such as linear semidefinite programming (SDP) and convex quadratic semidefinite programming (QSDP).   He is currently an Area Editor for Mathematical Programming Computation\, an Associate Editor for the SIAM Journal on Optimization\, Mathematical Programming Series B\, and ACM Transactions on Mathematical Software. He received the Farkas Prize awarded by the INFORMS Optimization Society in 2017 and the triennial Beale–Orchard Hays Prize awarded by the Mathematical Optimization Society in 2018. He was elected as a Fellow of the Society for Industrial and Applied Mathematics in 2018.  \n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-15-jan-10am/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20191020
DTEND;VALUE=DATE:20191024
DTSTAMP:20260504T120351
CREATED:20210303T070839Z
LAST-MODIFIED:20210303T071614Z
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SUMMARY:INFORMS 2019
DESCRIPTION:The 2019 INFORMS Annual Meeting is a unique opportunity to connect and network with the more than 6\,000 INFORMS members\, students\, prospective employers and employees\, and academic and industry experts who compose the INFORMS community. \nIt is with great pleasure to share the presentations and awards of IORA faculty\, students at the INFORMS 2019 conference that took place in Seattle. Professor Teo Chung Piaw\, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School\, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow\, one of a mere dozen selected worldwide annually. \n  \n\n\n\nAWARDS\n\n\nINFORMS FELLOWS: CLASS OF 2019\nPROFESSOR TEO CHUNG PIAW \nProfessor Teo Chung Piaw\, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School\, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow\, one of a mere dozen selected worldwide annually.\n\n\nGEORGE B.DANTZIG DISSERTATION AWARD COMPETITION\nDR. GUODONG LYU \nDr. Guodong Lyu is the finalist with his PhD thesis “Online Resource Allocation: Theory and Applications”\, under the supervision by Professor Chung-Piaw Teo (IORA\, NUS).\n\n\n2019 INFORMS-SECTION ON FINANCE BEST STUDENT PAPER COMPETITION\nMR. SHUAIJIE QIAN \nMr. Shuaijie Qian was awarded the first place in “2019 Informs-Section on Finance Best Student Paper Competition”. This year the competition has attracted many talented students worldwide\, including Columbia University and University of Pennsylvania.\n\n\nPAPER PRESENTATION\n\n\nPROFESSOR JOEL GOH\n  \nIORA Faculty Member\, Prof Joel Goh presented a paper titled “Design of Incentive Programs for Optimal Medication Adherence“\n\n\nPROFESSOR WENJIE TANG\nIORA Faculty Member\, Prof Wenjie Tang presented a paper titled “Invention Integrality And Gender Composition In Innovation Teams.“\n\n\nDR AARON\, JINJIA HUANG\nIORA Research Fellow\, Dr Aaron\, Jinjia Huang presented a paper titled “Sparse and Efficient Rebalancing Operations: Concentrating the Flows in Dynamic Network“.”\n\n\nHONG MING TAN\nIORA PHD Student\, Hong Ming Tan presented a paper titled “Information\, Investment and Risk management.”\n\n\nDR QINSHEN TANG\nIORA Research Fellow\, Dr Qinshen Tang presented a paper titled ” \n\nJoint pricing and production: an analytics perspective\nRepositioning for vehicle sharing: a risk mitigation perspective\n\n\n\n\nDR SATYANATH BHAT\nIORA Research Fellow\, Dr Satyanath Bhat presented a paper titled “Doubled Dipping of Two-sided Platform Economy“\n\n\nDR SHEN YICHI\nIORA Research Fellow\, Dr Shen Yichi presented a paper titled “Using Radial Basis Functions to Optimize Expensive Functions with Heterogenous Noise“
URL:https://iora.nus.edu.sg/events/informs-2019/
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20190809T093000
DTEND;TZID=Asia/Singapore:20190809T133000
DTSTAMP:20260504T120351
CREATED:20210303T025406Z
LAST-MODIFIED:20210303T025639Z
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SUMMARY:NUS National Observance Ceremony 2019
DESCRIPTION:IORA took part in NUS National Day Observance Ceremony held on Thursday\, 8 August 2019 at University Hall\, National University of Singapore. Dr Huang Jinjia\, IORA Research Fellow\, presented the  SDPNAL+ software. \nThe software SDPNAL+ is designed for solving semidefinite programming (SDP)\, an important subfield of mathematical optimization and its applications are growing rapidly. Many practical problems in operations research and machine learning can be modeled or approximated as SDP problems. Traditional optimization methods can only solve small and medium scale (say\, matrix dimension is less than 2000 and the number of constraints is less than 5000) SDP. Fortunately\, large-scale SDP can be solved efficiently by SDPNAL+ now. Numerical experiments in the paper and other benchmark tests show that SDPNAL+ is a state-of-the-art solver for large-scale SDP and it is the only viable software to solve many large-scale SDPs at present. The largest SDP problem that is solved has matrix dimension 9261 and the number of constraints more than 12 million\, which boosts the solvable scale to thousands of times. This software\, developed by IORA faculty Prof Toh Kim Chuan\,  has won the 2018 Beale-Orchard-Hays Prize\, the highest honor in the field of Computational Mathematical Optimization. In particular\, the prize jury chair Dr. Michael Grant presented a concrete example shared by the nominator. It takes 122 hours for the traditional solver to solve a problem in a cluster with 56 cores CPU and 128 GPUs while SDPNAL+ solves it within 1.5 hours in a normal desktop PC. This new solver has many applications in practice. For instance\, in a recent study\, another IORA team of researchers have used this software to develop the backbone network structure to support bike rebalancing operations by volunteers \nin a system like the Bike Angel program in New York\, and demonstrated big reduction in the number of redundant moves by volunteers in such a system (i.e. with much less incentives)\, but with essentially the same level of performance (number of rides supported).
URL:https://iora.nus.edu.sg/events/nus-national-observance-ceremony-2019/
LOCATION:University Hall\, National University of Singapore\, 21 Lower Kent Ridge Rd\, 119077\, Singapore
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