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This paper introduces link information value obtained by the traffic sensors and presents a traffic sensor location and flow estimation joint optimization model in an urban road network. In contrast to most previous studies, this paper adds new traffic sensors into the existing sensor network and proposes a data-driven path flow measurement method based on Wasserstein metric, which is utilized to measure the distance between the estimated traffic flow distribution and the actual distribution. Furthermore, this paper develops a customized greedy algorithm by combining a search strategy for the link information value to obtain the optimal sensor location scheme and perform traffic flow estimation under different budget conditions. Numerical experiments are conducted on Sioux-Falls test network and Eastern Massachusetts interstate highway subnetwork to verify the accuracy and effectiveness of the proposed model based on Wasserstein metric and the developed solution method. Computational results show that the sensor location scheme generated by the model based on Wasserstein metric can reduce the estimation error of the traffic flow compared with KL divergence model under the same deployment cost. Additionally, the customized greedy algorithm can achieve the better performance than the Brute force algorithm in terms of computing time and solution quality. • A traffic sensor location and path flow estimation joint optimization model is formulated. • A data-driven path flow measurement method based on Wasserstein metric is proposed. • A customized greedy algorithm by combining a search strategy is developed. • Numerical experiments are conducted based on the real-word transportation network. [ABSTRACT FROM AUTHOR]

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of ℝ. An order‐1 autoregressive model in this context is to be understood as a Markov chain, where one specifies a certain structure (regression) for the one‐step conditional Fréchet mean with respect to a natural probability metric. We construct and explore different models based on iterated random function systems of optimal transport maps. While the properties and interpretation of these models depend on how they relate to the iterated transport system, they can all be analyzed theoretically in a unified way. We present such a theoretical analysis, including convergence rates, and illustrate our methodology using real and simulated data. Our approach generalizes or extends certain existing models of transportation‐based regression and autoregression, and in doing so also provides some additional insights on existing models. [ABSTRACT FROM AUTHOR]

This paper examines the Wasserstein metric between the empirical probability measure of n discrete random variables and a continuous uniform measure in the d-dimensional ball, providing an asymptotic estimation of their expectations as n approaches infinity. Furthermore, we investigate this problem within a mixed process framework, where n discrete random variables are generated by the Poisson process. [ABSTRACT FROM AUTHOR]

A Dual Perspective to the Robust Screening Problem Robust screening problem is concerned with the problem of a seller seeking a selling mechanism that maximizes the worst-case revenue obtained from a buyer whose valuation distribution lies in a certain ambiguity set. In the paper "Screening with Limited Information: A Dual Perspective", Z. Chen, Z. Hu, and R. Wang show that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem, where the adversary first chooses a worst-case distribution and then the seller decides the best posted price mechanism. The duality result connects prior literature that separately studies the primal (robust screening) and problems related to the dual and offers a unified geometric intuition in solving the problem. Consider a seller seeking a selling mechanism to maximize the worst-case revenue 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, we show via the minimax theorem that strong duality holds between the problem of finding the optimal robust mechanism and a minimax pricing problem where the adversary first chooses a worst-case distribution, and then the seller decides the best posted price mechanism. This implies that the extra value of optimizing over more sophisticated mechanisms amounts exactly to the value of eliminating distributional ambiguity under a posted price mechanism. The duality result also connects prior literature that separately studies the primal (robust screening) and problems related to the dual (e.g., robust pricing, buyer-optimal pricing, and personalized pricing). We further analytically solve the minimax pricing problem (as well as the robust pricing problem) for several important ambiguity sets, such as the ones with mean and various dispersion measures, and with the Wasserstein metric, and we provide a unified geometric intuition behind our approach. The solutions are then used to construct the optimal robust mechanism and to compare with the solutions to the robust pricing problem. We also establish the uniqueness of the worst-case distribution for some cases. Funding: The research is funded in part by the Hong Kong Research Grants Council under its General Research Fund (CUHK-11502422) and the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 (Grant MOE-2019-T3-1-010). Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0016. [ABSTRACT FROM AUTHOR]

The Wiener index is the sum of distances of all pairs of nodes in a graph; and the Zagreb index is defined as the sum of squares of the degrees of nodes in a rooted tree. In this note, we calculate the first two moments of the Wiener and Zagreb indices of random exponential recursive trees (random ERTs) from two systems of recurrence relations. Then, by an application of the contraction method, we characterize the limit law for a scaled Zagreb index of ERTs. Via the martingale convergence theorem, we also show the almost sure convergence and quadratic mean convergence of an appropriately scaled Wiener index that is indicative of the distance of two randomly chosen nodes. [ABSTRACT FROM AUTHOR]

This paper presents a novel moving horizon estimation approach based on distributionally robust optimisation to tackle the state estimation problem of non-linear systems with missing noise distribution information. The proposed method adopts a fuzzy set to mitigate the impact of uncertainties on state estimation. Specifically, the method derives an empirical distribution within the prediction window using a priori data and constructs a fuzzy sphere set using the Wasserstein metric with the empirical distribution as the sphere centre. This enables the estimation of the state sequence under the worst probability distribution of the fuzzy set. To demonstrate the effectiveness of the proposed method, a simple simulation example is conducted to compare its performance with that of traditional moving horizon estimation. The results provide evidence of the feasibility and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper builds a bridge between two area in optimization and machine learning by establishing a general connection between Wasserstein distributional robustness and variation regularization. It helps to demystify the empirical success of Wasserstein distributionally robust optimization and devise new regularization schemes for machine learning. Wasserstein distributionally robust optimization (DRO) is an approach to optimization under uncertainty in which the decision maker hedges against a set of probability distributions, specified by a Wasserstein ball, for the uncertain parameters. This approach facilitates robust machine learning, resulting in models that sustain good performance when the data are to some extent different from the training data. This robustness is related to the well-studied effect of regularization. The connection between Wasserstein DRO and regularization has been established in several settings. However, existing results often require restrictive assumptions, such as smoothness or convexity, that are not satisfied by many important problems. In this paper, we develop a general theory for the variation regularization effect of the Wasserstein DRO—a new form of regularization that generalizes total-variation regularization, Lipschitz regularization, and gradient regularization. Our results cover possibly nonconvex and nonsmooth losses and losses on non-Euclidean spaces and highlight the bias-variation tradeoff intrinsic in the Wasserstein DRO, which balances between the empirical mean of the loss and the variation of the loss. Example applications include multi-item newsvendor, linear prediction, neural networks, manifold learning, and intensity estimation for Poisson processes. We also use our theory of variation regularization to derive new generalization guarantees for adversarial robust learning. Funding: X. Chen is supported by the National Science Foundation [Grant IIS-1845444]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2383. [ABSTRACT FROM AUTHOR]

In this paper, we consider the general mean-field backward doubly stochastic differential equations (mean-field BDSDEs) whose generator f can be discontinuous in y. We prove the existence theorem of solutions under stochastic linear growth conditions and also obtain the related comparison theorem. Naturally, we present those results under the linear growth condition, which is a special case of the stochastic condition. Finally, a financial claim sale problem is discussed, which demonstrates the application of the general mean-field BDSDEs in finance. [ABSTRACT FROM AUTHOR]

Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors. [ABSTRACT FROM AUTHOR]

Physical activity has long been shown to be associated with biological and physiological performance and risk of diseases. It is of great interest to assess whether the effect of an exposure or intervention on an outcome is mediated through physical activity measured by modern wearable devices such as actigraphy. However, existing methods for mediation analysis focus almost exclusively on mediation variable that is in the Euclidean space, which cannot be applied directly to physical activity measured by wearable devices. Such data is best summarized in the form of a random distribution. In this paper, we develop a structural equation model to the setting where a random distribution is treated as the mediator. We provide sufficient conditions for identifying the average causal effects of a distribution mediator and present methods for estimating the direct and mediating effects of a random distribution mediator on the outcome. We apply our method to analyze the iCOMPARE data set that compares flexible duty-hour policies and standard duty-hour policies on interns' sleep related outcomes and to investigate the mediation effect of physical activity on the causal path between flexible duty-hour policies and sleep related outcomes. [ABSTRACT FROM AUTHOR]

An optimal control problem in the space of probability measures and the viscosity solutions of the corresponding dynamic programming equations defined using the intrinsic linear derivative are studied. The value function is shown to be Lipschitz continuous with respect to a smooth Fourier--Wasserstein metric. A comparison result between the Lipschitz viscosity sub- and supersolutions of the dynamic programming equation is proved using this metric, characterizing the value function as the unique Lipschitz viscosity solution. [ABSTRACT FROM AUTHOR]

We study the computation of the zero set of the Bargmann transform of a signal contaminated with complex white noise, or, equivalently, the computation of the zeros of its short-time Fourier transform with Gaussian window. We introduce the adaptive minimal grid neighbors algorithm (AMN), a variant of a method that has recently appeared in the signal processing literature, and prove that with high probability it computes the desired zero set. More precisely, given samples of the Bargmann transform of a signal on a finite grid with spacing δ , AMN is shown to compute the desired zero set up to a factor of δ in the Wasserstein error metric, with failure probability O (δ 4 log 2 (1 / δ)) . We also provide numerical tests and comparison with other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop an exact reformulation and a deterministic approximation for distributionally robust joint chance-constrained programmings $ ({\rm DRCCPs}) $ (DRCCPs) with a general class of convex uncertain constraints under data-driven Wasserstein ambiguity sets. It is known that robust chance constraints can be conservatively approximated by worst-case conditional value-at-risk (CVaR) constraints. It is shown that the proposed worst-case CVaR approximation model can be reformulated as an optimization problem involving biconvex constraints for joint DRCCP. This approximation is essentially exact under certain conditions. We derive a convex relaxation of this approximation model by constructing new decision variables which allows us to eliminate biconvex terms. Specifically, when the constraint function is affine in both the decision variable and the uncertainty, the resulting approximation model is equivalent to a tractable mixed-integer convex reformulation for joint binary DRCCP. Numerical results illustrate the computational effectiveness and superiority of the proposed formulations. [ABSTRACT FROM AUTHOR]

By further exploring and deeply analyzing the concrete algebraic structures and essential computational properties about the Wasserstein-1 metric matrices of one- and two-dimensions, we show that they can be essentially expressed by the Neumann series of nilpotent matrices and, therefore, the products of these matrices with a prescribed vector can be accomplished accurately and stably in the optimal computational complexities through solving unit bidiagonal triangular systems of linear equations. We also give appropriate generalizations of these one- and two-dimensional Wasserstein-1 metric matrices, as well as their corresponding extensions to higher dimensions, and demonstrate the algebraic structures and computational properties of these generalized and extended Wasserstein-1 metric matrices. [ABSTRACT FROM AUTHOR]

In this paper, we consider a data-driven distributionally robust two-stage stochastic linear optimization problem over 1-Wasserstein ball centered at a discrete empirical distribution. Differently from the traditional two-stage stochastic programming which involves the expected recourse function as the preference criterion and hence is risk-neutral, we take the conditional value-at-risk (CVaR) as the risk measure in order to model its effects on decision making problems. We mainly explore tractable reformulations for the proposed robust two-stage stochastic programming with mean-CVaR criterion by analyzing the first case where uncertainties are only in the objective function and then the second case where uncertainties are only in the constraints. We demonstrate that the first model can be exactly reformulated as a deterministic convex programming. Furthermore, it is shown that under several different support sets, the resulting convex optimization problems can be converted into computationally tractable conic programmings. Besides, the second model is generally NP-hard since checking constraint feasibility can be reduced to a norm maximization problem over a polytope. However, even with the case of uncertainty in constraints, tractable conic reformulations can be established when the extreme points of the polytope are known. Finally, we present numerical results to discuss how to control the risk for the best decisions and illustrate the computational effectiveness and superiority of the proposed models. [ABSTRACT FROM AUTHOR]

We present a novel reshuffling exchange model and investigate its long time behavior. In this model, two individuals are picked randomly, and their wealth X i and X j are redistributed by flipping a sequence of fair coins leading to a binomial distribution denoted B ∘ (X i + X j). This dynamics can be considered as a natural variant of the so-called uniform reshuffling model in econophysics. May refer to Cao, Jabin and Motsch (2023), Dragulescu and Yakovenko (2000). As the number of individuals goes to infinity, we derive its mean-field limit, which links the stochastic dynamics to a deterministic infinite system of ordinary differential equations. Our aim of this work is then to prove (using a coupling argument) that the distribution of wealth converges to the Poisson distribution in the 2 -Wasserstein metric. Numerical simulations illustrate the main result and suggest that the polynomial convergence decay might be further improved. [ABSTRACT FROM AUTHOR]

This paper examines the Wasserstein metric between the empirical probability measure of n discrete random variables and a continuous uniform measure in the d-dimensional ball, providing an asymptotic estimation of their expectations as n approaches infinity. Furthermore, we investigate this problem within a mixed process framework, where n discrete random variables are generated by the Poisson process.

In this paper, we consider the general mean-field backward doubly stochastic differential equations (mean-field BDSDEs) whose generator f can be discontinuous in y. We prove the existence theorem of solutions under stochastic linear growth conditions and also obtain the related comparison theorem. Naturally, we present those results under the linear growth condition, which is a special case of the stochastic condition. Finally, a financial claim sale problem is discussed, which demonstrates the application of the general mean-field BDSDEs in finance.

Distribution data refer to a data set in which each sample is represented as a probability distribution, a subject area that has received increasing interest in the field of statistics. Although several studies have developed distribution-to-distribution regression models for univariate variables, the multivariate scenario remains under-explored due to technical complexities. In this study, we introduce models for regression from one Gaussian distribution to another, using the Wasserstein metric. These models are constructed using the geometry of the Wasserstein space, which enables the transformation of Gaussian distributions into components of a linear matrix space. Owing to their linear regression frameworks, our models are intuitively understandable, and their implementation is simplified because of the optimal transport problem's analytical solution between Gaussian distributions. We also explore a generalization of our models to encompass non-Gaussian scenarios. We establish the convergence rates of in-sample prediction errors for the empirical risk minimizations in our models. In comparative simulation experiments, our models demonstrate superior performance over a simpler alternative method that transforms Gaussian distributions into matrices. We present an application of our methodology using weather data for illustration purposes. [ABSTRACT FROM AUTHOR]

Concentrating solar power (CSP) plants have significant potential to complement the growing wind energy in power scheduling. This study examines an integrated energy system (IES) that incorporates a wind turbine (WT), CSP, and combined heat and power (CHP) to promote the utilization of renewable energy (RE), reduce fluctuations caused by uncertainty, and enhance the economic viability of the system. We propose a distributionally robust optimization (DRO) model for IES scheduling that considers the uncertainty of wind power by using an ambiguity set defined by the Wasserstein metric. Before the occurrence of uncertainties, the system determines the initial dispatching scheme based on the forecast data. In the second stage, the system aims to minimize the adjustment cost expectation under the worst distribution of the ambiguity set and adjusts the flexible resources in real time to offset the fluctuations caused by forecasting errors. The proposed DRO model is transformed into a conventional two-stage robust problem using strong dual theory and KKT conditions, and then solved with a modified column-and-constraint generation (C&CG) algorithm. The results of case studies show that the CSP plant enhances the system's flexibility and controllability through thermal energy storage (TES). [ABSTRACT FROM AUTHOR]

This paper addresses facility location, inventory pre-positioning and allocation of emergency supplies in disaster relief logistics by taking into account both primary and secondary disasters. To characterize the uncertainty associated with post-disaster demand and resource allocation cost, this paper constructs the statistical-distance-based ambiguity sets of possible probability distributions with the Wasserstein metric, which is utilized to measure their distances from the empirical distribution. Armed with the Wasserstein ambiguity set, this paper develops a hybrid risk-averse three-stage distributionally robust chance-constrained (TS-DRCC) model for the considered problem, which measures the risk from both quantitative and qualitative aspects. When the Wasserstein metric uses the l 1 -norm, this paper reformulates the proposed TS-DRCC model as a mixed-integer linear program (MILP) based on the strong duality theory, which can be efficiently solved via CPLEX, thereby enabling decision-makers to use it. Theoretically, this paper also proves that the proposed TS-DRCC model converges to stochastic programming (SP) model as the size of historical data approaches infinity. Finally, this paper conducts a computational study of hurricane threat in the US to indicate the superiority of our proposed TS-DRCC model in terms of demand satisfaction and out-of-sample performance compared to the model considering only primary disasters and the conventional SP model, respectively. Some key managerial insights are summarized as rules of thumb to effectively guide the integrated pre- and post-disaster relief actions in the disaster relief logistics planning practice. • A location-inventory-allocation problem with secondary disaster considerations in DRL is introduced. • A hybrid risk-averse TS-DRCC model for the LIAP under demand and cost ambiguities is formulated. • The Wasserstein ambiguity sets are constructed to address the distributional ambiguities. • Extensive numerical experiments are carried out on a realistic case study. [ABSTRACT FROM AUTHOR]

A rise in traffic accidents has led to both traffic congestion and subsequent secondary accidents. Effectively addressing this issue requires rapid accident investigation and management. In this paper, we aim to improve the efficiency of traffic accident assessment and investigation with the aid of drone technologies. Our approach involves strategically pre-positioning drones, enabling traffic supervisory agencies to dispatch drones immediately upon receiving an accident report. Methodology-wise, we present a data-driven robust stochastic optimization (RSO) model, which encapsulates the uncertainty of traffic accidents within a scenario-wise Wasserstein ambiguity set. To the best of our knowledge, this is the first study that incorporates covariates, i.e., weather conditions, into the Wasserstein ambiguity set with the CVaR metric. We demonstrate that the proposed RSO model can be reformulated into a mixed-integer programming model, allowing an efficient solution approach. Via a real-world dataset of London traffic accidents, we validate the practical applicability of the RSO model. Across various parameter settings, our RSO model exhibits superior out-of-sample performance compared with various benchmark models. The numerical results yield valuable insights for traffic supervisory agencies. • We propose a drone pre-positioning problem for traffic accident rapid assessment. • We propose an RSO model that incorporates the uncertain traffic accident. • We demonstrate that the RSO model admits a tractable MILP reformulation. • A real world instance is used to verify the practicality of our model. [ABSTRACT FROM AUTHOR]

This paper is concerned with the convergence of invariant measures in the Wasserstein sense for fractional stochastic reaction–diffusion equations defined on unbounded domains as the noise intensity approaches zero. Based on uniform estimates of solutions, we prove the family of invariant measures of the stochastic equations converges to the invariant measure of the corresponding deterministic equations in terms of the Wasserstein metric. We also provide the rate of such convergence. [ABSTRACT FROM AUTHOR]