About

Jong-Shi Pang is the Epstein Family Chair and Distinguished Professor of Industrial and Systems Engineering at the University of Southern California, having joined USC in August 2013. He holds a doctoral degree in Other Engineering from Stanford University, a master's degree in Statistics from Stanford University, and a bachelor's degree in Mathematics from National Taiwan University. Prior to his current appointment, he served as the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, and held faculty positions at Rensselaer Polytechnic Institute, Johns Hopkins University, the University of Texas at Dallas, and Carnegie-Mellon University. His research focuses on the mathematical modeling and analysis of complex engineering and economic systems, with particular emphasis on operations research, single-agent optimization, equilibrium programming, noncooperative game theory, and constrained dynamical systems. He has made significant contributions to multi-agent optimization and equilibrium theory, receiving prestigious awards such as the John von Neumann Theory Prize and the George B. Dantzig Prize, and has been recognized as an ISI Highly Cited Researcher. He is a member of the National Academy of Engineering and a Fellow of SIAM and INFORMS. Additionally, he serves as the Editor-in-Chief of the SIAM Journal on Optimization.

Research topics

• Computer Science
• Mathematics
• Mathematical optimization
• Artificial Intelligence
• Geometry
• Algorithm
• Mathematical economics
• Combinatorics
• Library science

Selected publications

• A Coupled V2G Equilibrium Model of Electric Vehicle and Power System Interactions

  arXiv (Cornell University) · 2026-05-16

  preprintOpen accessSenior author

  Vehicle-to-grid (V2G) technology empowers electric vehicles (EVs) to act as mobile energy resources, providing critical support to power systems, especially under stressed conditions. To understand the economic mechanism driving V2G participation and its benefits to power grid, this paper proposes a multi-player coupled equilibrium framework that models the bidirectional interactions between power grid operations and EV routing, incorporating charging and discharging choice in a preprocessed feasible path generation procedure. Energy prices are endogenously determined by market clearance conditions. We formulate the overall problem as a Variational Inequality that unite the decision-making of Distribution System Operator, Charging Network Operator, Load Serving Entities, and EV drivers. Numerical studies validate the framework under two stress scenarios: increased household load and power line outages. Results show that when EVs are incentivized by reduced generalized path costs, V2G is particularly effective in eliminating load shedding and reducing distribution locational marginal electricity prices. On the transportation side, V2G can lead to divergence in EV behavior between normal and scarcity conditions, and alter route choices yet improve overall trip economic.

  PublisherDOI

• Solving Constrained Affine Heaviside Composite Optimization Problems by a Progressive IP Approach

  arXiv (Cornell University) · 2026-05-07

  preprintOpen accessSenior author

  This paper discusses the computational resolution and presents numerical results for solving affine combinations of Heaviside composite optimization problems (abbreviated as A-HSCOPs) by a progressive integer programming (abbreviated as PIP) method. The characteristics of these problems are that the Heaviside functions, which appear in the objective and define the constraints, are discontinuous, and their mixed-signed combinations result in the overall objective lacking the matching semicontinuity needed for the optimization and in the feasible set being not necessarily closed. Added to these challenging properties is the nondifferentiability of the inner functions in the composition. In this paper, we propose resolutions to all these challenges by first an approximation to remedy the lack of semicontinuity in the objective and closedness in the constraints, followed by a progressive integer programming approach with successive decomposition to handle the intrinsically discrete nature of the Heaviside function. Convergence to the local optimizers of the given Heaviside optimization problem is established. The effectiveness of the overall solution strategy is supported by extensive computational experiments on the score-based and tree-based multiclass classification problems with precision constraints.

  PublisherDOI

• Equilibrium in Mixed-Autonomy Ride-Hailing Networks: Endogenous Fleets, Behavioral Heterogeneity, and Customer Patience

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Solving Constrained Affine Heaviside Composite Optimization Problems by a Progressive IP Approach

  ArXiv.org · 2026-05-07

  articleOpen accessSenior author

  This paper discusses the computational resolution and presents numerical results for solving affine combinations of Heaviside composite optimization problems (abbreviated as A-HSCOPs) by a progressive integer programming (abbreviated as PIP) method. The characteristics of these problems are that the Heaviside functions, which appear in the objective and define the constraints, are discontinuous, and their mixed-signed combinations result in the overall objective lacking the matching semicontinuity needed for the optimization and in the feasible set being not necessarily closed. Added to these challenging properties is the nondifferentiability of the inner functions in the composition. In this paper, we propose resolutions to all these challenges by first an approximation to remedy the lack of semicontinuity in the objective and closedness in the constraints, followed by a progressive integer programming approach with successive decomposition to handle the intrinsically discrete nature of the Heaviside function. Convergence to the local optimizers of the given Heaviside optimization problem is established. The effectiveness of the overall solution strategy is supported by extensive computational experiments on the score-based and tree-based multiclass classification problems with precision constraints.

  PublisherOA PDF

• Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization

  ArXiv.org · 2026-01-17

  articleOpen accessSenior author

  Offline policy learning aims to use historical data to learn an optimal personalized decision rule. In the standard estimate-then-optimize framework, reweighting-based methods (e.g., inverse propensity weighting or doubly robust estimators) are widely used to produce unbiased estimates of policy values. However, when the propensity scores of some treatments are small, these reweighting-based methods suffer from high variance in policy value estimation, which may mislead the downstream policy optimization and yield a learned policy with inferior value. In this paper, we systematically develop an offline policy learning algorithm based on a weight-clipping estimator that truncates small propensity scores via a clipping threshold chosen to minimize the mean squared error (MSE) in policy value estimation. Focusing on linear policies, we address the bilevel and discontinuous objective induced by weight-clipping-based policy optimization by reformulating the problem as a Heaviside composite optimization problem, which provides a rigorous computational framework. The reformulated policy optimization problem is then solved efficiently using the progressive integer programming method, making practical policy learning tractable. We establish an upper bound for the suboptimality of the proposed algorithm, which reveals how the reduction in MSE of policy value estimation, enabled by our proposed weight-clipping estimator, leads to improved policy learning performance.

  PublisherOA PDF

• A Coupled V2G Equilibrium Model of Electric Vehicle and Power System Interactions

  ArXiv.org · 2026-05-16

  articleOpen accessSenior author

  Vehicle-to-grid (V2G) technology empowers electric vehicles (EVs) to act as mobile energy resources, providing critical support to power systems, especially under stressed conditions. To understand the economic mechanism driving V2G participation and its benefits to power grid, this paper proposes a multi-player coupled equilibrium framework that models the bidirectional interactions between power grid operations and EV routing, incorporating charging and discharging choice in a preprocessed feasible path generation procedure. Energy prices are endogenously determined by market clearance conditions. We formulate the overall problem as a Variational Inequality that unite the decision-making of Distribution System Operator, Charging Network Operator, Load Serving Entities, and EV drivers. Numerical studies validate the framework under two stress scenarios: increased household load and power line outages. Results show that when EVs are incentivized by reduced generalized path costs, V2G is particularly effective in eliminating load shedding and reducing distribution locational marginal electricity prices. On the transportation side, V2G can lead to divergence in EV behavior between normal and scarcity conditions, and alter route choices yet improve overall trip economic.

  PublisherOA PDF

• Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization

  arXiv (Cornell University) · 2026-01-17

  preprintOpen accessSenior author

  Offline policy learning aims to use historical data to learn an optimal personalized decision rule. In the standard estimate-then-optimize framework, reweighting-based methods (e.g., inverse propensity weighting or doubly robust estimators) are widely used to produce unbiased estimates of policy values. However, when the propensity scores of some treatments are small, these reweighting-based methods suffer from high variance in policy value estimation, which may mislead the downstream policy optimization and yield a learned policy with inferior value. In this paper, we systematically develop an offline policy learning algorithm based on a weight-clipping estimator that truncates small propensity scores via a clipping threshold chosen to minimize the mean squared error (MSE) in policy value estimation. Focusing on linear policies, we address the bilevel and discontinuous objective induced by weight-clipping-based policy optimization by reformulating the problem as a Heaviside composite optimization problem, which provides a rigorous computational framework. The reformulated policy optimization problem is then solved efficiently using the progressive integer programming method, making practical policy learning tractable. We establish an upper bound for the suboptimality of the proposed algorithm, which reveals how the reduction in MSE of policy value estimation, enabled by our proposed weight-clipping estimator, leads to improved policy learning performance.

  PublisherDOI

• Logarithmic integral optimization via adaptive importance sampling based surrogation methods

  Mathematical Programming · 2025-07-11

  articleOpen accessSenior author

  Abstract This paper explores Logarithmic Integral Optimization () problems, providing a unified computational framework for various tasks in computational statistics. Key among these are Maximum Likelihood Estimation (MLE) and Maximum a Posteriori (MAP) inference for probabilistic models. Specifically, we investigate scenarios where the model consists of conditional density functions with intractable normalizers. This feature can pose substantial computational challenges for the associated , especially when coupled with the growing prevalence of nonconvex and nondifferentiable modelings in contemporary applications. To address these challenges, we propose an efficient algorithm for , termed Adaptive Importance Sampling-based Surrogation . This method is designed to simultaneously handle nonconvexity and nondifferentiability, while also improving the sampling approximation of the intractable integral term in through variance reduction. The justification of this algorithm is supported by our analysis, which establishes an almost sure subsequential convergence to a necessary candidate for a local minimizer, referred to as a surrogation stationary point . Furthermore, we demonstrate the effectiveness of our algorithm through extensive numerical experiments, confirming its efficiency and stability in facilitating more advanced probabilistic models with intractable normalizers.

  PublisherOA PDFDOI

• Improving the solution of indefinite quadratic programs and linear programs with complementarity constraints by a progressive MIP method

  Mathematical Programming Computation · 2025-09-16

  articleOpen accessSenior author

  Abstract Indefinite quadratic programs (QPs) are known to be very difficult to be solved to global optimality, so are linear programs with linear complementarity constraints (LPCCs). It is a classic result that for a QP with an optimal solution, the QP has an equivalent formulation as a certain LPCC in terms of their globally optimal solutions. Thus it is natural to attempt to solve an (indefinite) QP as a LPCC. This paper presents a progressive mixed integer linear programming method for solving a general LPCC. Instead of solving the LPCC with a full set of integer variables expressing the complementarity conditions, the presented method solves a finite number of mixed integer subprograms by starting with a small fraction of integer variables and progressively increasing this fraction. After describing the PIP (for progressive integer programming) method and providing some details for its implementation and tuning possibilities, we demonstrate, via an extensive set of computational experiments, the superior performance of the progressive approach over the direct solution of the full-integer formulation of the LPCCs in obtaining high-quality solutions. It is also shown that the solution obtained at the termination of the PIP method is a local minimizer of the LPCC, a property that cannot be claimed by any known non-enumerative method for solving this nonconvex program. In all the experiments, the PIP method is initiated at a feasible solution of the LPCC obtained from a nonlinear programming solver, and with high likelihood, can successfully improve it. Thus, the PIP method can improve a stationary solution of an indefinite QP, something that is not likely to be achievable by a nonlinear programming method. Finally, some analysis is presented that provides a better understanding of the roles of the LPCC suboptimal solutions in the local optimality of the indefinite QP. This local aspect of the connection between a QP and its LPCC formulation has seemingly not been addressed in the literature.

  PublisherOA PDFDOI

• Quasi-difference-convexity: Modernization of Quasi-differentiable Optimization

  ArXiv.org · 2025-07-16

  preprintOpen access1st authorCorresponding

  Quasi-differentiable functions were introduced by Pshenichnyi in a 1969 monograph written in Russian and translated in an English version in 1971. This class of nonsmooth functions was studied extensively in two decades since but has not received much attention in today's wide optimization literature. This regrettable omission is in spite of the fact that many functions in modern day applications of optimization can be shown to be quasi-differentiable. In essence, a quasi-differentiable function is one whose directional derivative at an arbitrary reference vector, as a function of the direction, is the difference of two positively homogenous, convex functions. Thus, to bring quasi-differentiable functions closer to the class of difference-of-convex functions that has received fast growing attention in recent years in connection with many applied subjects, we propose to rename quasi-differentiable functions as quasi-difference-convex (quasi-dc) functions. Besides modernizing and advancing this class of nonconvex and nondifferentiable functions, our research aims to put together a unified treatment of iterative convex-programming based descent algorithms for solving a broad class of composite quasi-dc programs and to establish their subsequential convergence, sequential convergence, and rates of convergence; the latter two topics are in line with the modern focus of such analysis for convex programs and some extensions and are departures from the sole emphasis of subsequential convergence in the traditional studies of quasi-differentiable optimization. Through this research, we have gained significant new insights and understanding, advanced the fundamentals, and broadened the applications of this neglected yet pervasive class of nonconvex and nondifferentiable functions and their optimization.

  PublisherOA PDFDOI

Recent grants

• Analysis and Control of Complementary Systems

  NSF · $225k · 2005–2007

• Extended Nash Equilibria and Their Applications

  NSF · $148k · 2007–2010

• Analysis and Control of Complementary Systems

  NSF · $114k · 2007–2009

• BECS Collaborative Research: Modeling the Dynamics of Traffic User Equilibria Using Differential Variational Inequalities

  NSF · $110k · 2010–2014

• Extended Nash Equilibria and Their Applications

  NSF · $300k · 2005–2007

Frequent coauthors

• Gesualdo Scutari

  32 shared

• Francisco Facchinei

  27 shared

• Ying Cui

  University of California, Berkeley

  26 shared

• Zhi‐Quan Luo

  22 shared

• John E. Mitchell

  21 shared

• Daniel P. Palomar

  University of Hong Kong

  13 shared

• Meisam Razaviyayn

  12 shared

• M. Kanat Camlibel

  11 shared

Education

• Ph.D., Operations Research

  University of California, Los Angeles

  1980

• M.S., Operations Research

  University of California, Los Angeles

  1976

• B.S., Mathematics

  National Taiwan University

  1974

Awards & honors

• Elected a member of the National Academy of Engineering in F…
• Distinguished Professor at USC (April 2023)
• Fellow of the Institute for Operations Research and Manageme…
• John von Neumann Theory Prize (2019)
• George B. Dantzig Prize (2003)