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Publications supported by MOE Tier 3 Grant

RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy

Zhi Chen , Peng Xiong
We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
INFORMS Journal on Computing
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RSOME in Python: An Open-Source Package for Robust Stochastic Optimization Made Easy

Zhi Chen , Peng Xiong
We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in…
INFORMS Journal on Computing
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Picking winners: Diversification through portfolio optimization

Ju Liu, Liu Changchun, Chung Piaw Teo
We develop a general framework for selecting a small pool of candidate solutions to maximize the chances that one will be optimal for a combinatorial optimization problem, under a linear and additive random payoff function. We formulate this problem using a two-stage distributionally robust model, with a mixed 0–1…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Production and Operations Management
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Picking winners: Diversification through portfolio optimization

Ju Liu, Liu Changchun, Chung Piaw Teo
We develop a general framework for selecting a small pool of candidate solutions to maximize the chances that one will be optimal for a combinatorial optimization problem, under a linear and additive random payoff function. We formulate this problem using a two-stage distributionally robust model, with a mixed 0–1…
Production and Operations Management
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Robust CARA Optimization

Li Chen, Melvyn Sim
We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Operations Research
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Robust CARA Optimization

Li Chen, Melvyn Sim
We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent…
Operations Research
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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A feasible method for solving an SDP relaxation of the quadratic knapsack problem

Tianyun Tang, Kim-Chuan Toh
In this paper, we consider a semidefinite programming (SDP) relaxation of the quadratic knapsack problem. After applying low-rank factorization, we get a nonconvex problem, whose feasible region is an algebraic variety with certain good geometric properties, which we analyze. We derive a rank condition under which these two formulations…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Mathematics of Operations Research
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A feasible method for solving an SDP relaxation of the quadratic knapsack problem

Tianyun Tang, Kim-Chuan Toh
In this paper, we consider a semidefinite programming (SDP) relaxation of the quadratic knapsack problem. After applying low-rank factorization, we get a nonconvex problem, whose feasible region is an algebraic variety with certain good geometric properties, which we analyze. We derive a rank condition under which these two formulations…
Mathematics of Operations Research
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Solving graph equipartition SDPs on an algebraic variety

Tianyun Tang, Kim-Chuan Toh
Semidefinite programs are generally challenging to solve due to their high dimensionality. Burer and Monteiro developed a non-convex approach to solve linear SDP problems by applying its low rank property. Their approach is fast because they used factorization to reduce the problem size. In this paper, we focus on…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Mathematical Programming
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Solving graph equipartition SDPs on an algebraic variety

Tianyun Tang, Kim-Chuan Toh
Semidefinite programs are generally challenging to solve due to their high dimensionality. Burer and Monteiro developed a non-convex approach to solve linear SDP problems by applying its low rank property. Their approach is fast because they used factorization to reduce the problem size. In this paper, we focus on…
Mathematical Programming
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Dissolving Constraints for Riemannian Optimization

Nachuan Xiao , Xin Liu , Kim-Chuan Toh
In this paper, we consider optimization problems over closed embedded submanifolds of ℝ𝑛, which are defined by the constraints c(x) = 0. We propose a class of constraint-dissolving approaches for these Riemannian optimization problems. In these proposed approaches, solving a Riemannian optimization problem is transferred into the unconstrained minimization of a constraint-dissolving function…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Mathematics of Operations Research
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Dissolving Constraints for Riemannian Optimization

Nachuan Xiao , Xin Liu , Kim-Chuan Toh
In this paper, we consider optimization problems over closed embedded submanifolds of ℝ𝑛, which are defined by the constraints c(x) = 0. We propose a class of constraint-dissolving approaches for these Riemannian optimization problems. In these proposed approaches, solving a Riemannian optimization problem is transferred into the unconstrained minimization of a constraint-dissolving function…
Mathematics of Operations Research
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems

Kuang-Yu Ding, Xin-Yee Lam, Kim-Chuan Toh
We design inexact proximal augmented Lagrangian based decomposition methods for convex composite programming problems with dual block-angular structures. Our methods are particularly well suited for convex quadratic programming problems arising from stochastic programming models. The algorithmic framework is based on the application of the abstract inexact proximal ADMM framework developed in…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Computational Optimization and Applications
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On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems

Kuang-Yu Ding, Xin-Yee Lam, Kim-Chuan Toh
We design inexact proximal augmented Lagrangian based decomposition methods for convex composite programming problems with dual block-angular structures. Our methods are particularly well suited for convex quadratic programming problems arising from stochastic programming models. The algorithmic framework is based on the application of the abstract inexact proximal ADMM framework developed in…
Computational Optimization and Applications
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems

We introduce a class of specially structured linear programming (LP) problems, which has favorable modeling capability for important application problems in different areas such as optimal transport, discrete tomography, and economics. To solve these generally large-scale LP problems efficiently, we design an implementable inexact entropic proximal point algorithm (iEPPA)…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Computational Optimization and Applications
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An efficient implementable inexact entropic proximal point algorithm for a class of linear programming problems

We introduce a class of specially structured linear programming (LP) problems, which has favorable modeling capability for important application problems in different areas such as optimal transport, discrete tomography, and economics. To solve these generally large-scale LP problems efficiently, we design an implementable inexact entropic proximal point algorithm (iEPPA)…
Computational Optimization and Applications
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Screening with Limited Information: A Dual Perspective

Zhi Chen, Zhenyu Hu, Ruiqin Wang
Consider a seller seeking a selling mechanism to maximize the worst-case rev- enue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. Such a mechanism design problem with one product and one buyer is known as the screening problem. For a generic convex ambiguity set,…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Operations Research
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Screening with Limited Information: A Dual Perspective

Zhi Chen, Zhenyu Hu, Ruiqin Wang
Consider a seller seeking a selling mechanism to maximize the worst-case rev- enue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. Such a mechanism design problem with one product and one buyer is known as the screening problem. For a generic convex ambiguity set,…
Operations Research
Link to Publication
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times

Yanlu Zhao, Felix Papier, Chung-Piaw Teo
Problem definition: Online Food Delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Manufacturing and Service Operations Management
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Market Thickness in Online Food Delivery Platforms: The Impact of Food Processing Times

Yanlu Zhao, Felix Papier, Chung-Piaw Teo
Problem definition: Online Food Delivery (OFD) platforms have witnessed rapid global expansion, partly driven by shifts in consumer behavior during the COVID-19 pandemic. These platforms enable customers to order food conveniently from a diverse array of restaurants through their mobile phones. A core functionality of these platforms is the…
Manufacturing and Service Operations Management
Link to Publication
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Optimal deployment of an equitable CAV platoonable corridor on road networks with mixed traffic flow

Dan Zhu, Tingting Xie, Yang Liu, Bo Zou, Napat Rujeerapaiboon
Connected and autonomous vehicle (CAV) technology is expected to increase road capacity and reduce fuel consumption, but it may take time for all human-driven vehicles (HVs) to be replaced by CAVs. During the transition period, CAVs and HVs will continue to coexist. This paper aims to find an optimal…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Transportation Research Part C
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Optimal deployment of an equitable CAV platoonable corridor on road networks with mixed traffic flow

Dan Zhu, Tingting Xie, Yang Liu, Bo Zou, Napat Rujeerapaiboon
Connected and autonomous vehicle (CAV) technology is expected to increase road capacity and reduce fuel consumption, but it may take time for all human-driven vehicles (HVs) to be replaced by CAVs. During the transition period, CAVs and HVs will continue to coexist. This paper aims to find an optimal…
Transportation Research Part C
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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Hedging the Drift: Learning to Optimize Under Nonstationarity

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown \emph{a priori} and possibly adversarial) non-stationarity can be…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Management Science
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Hedging the Drift: Learning to Optimize Under Nonstationarity

Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown \emph{a priori} and possibly adversarial) non-stationarity can be…
Management Science
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This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
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