The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Generating Lagrangian cuts using normalized dual problems in multistage stochastic mixed-integer programming by C. Füllner, X. A. Sun and S. Rebennack. The snapshot is based on this SHA in the development repository.

Mobile technologies and AI enable health data collection from devices, allowing effective monitoring. Traditional methods often compromise privacy, but federated learning (FL) offers a potential solution. However, current FL approaches face two issues: they don’t identify key health features for clinical intervention, and their aggregation overlooks patient multidimensional heterogeneity. This study seeks to develop a new FL method to tackle these challenges and enhance privacy in mobile health monitoring. This study proposes a novel FL method combining (1) a spatial and temporal attention-based prediction model (STA-Pred) that uses attention to identify key spatial and temporal features, and (2) a multidimensional heterogeneity-based aggregation protocol (MDH-Aggr), which aggregates components based on their heterogeneity to handle multidimensional differences. Experiments on three data sets show that our method outperforms existing methods in several patient-monitoring contexts. This study enhances understanding of how to leverage mobile technologies and AI to enable privacy-preserving health monitoring that promotes the social good. Additionally, it advances FL research through two innovative designs (STA-Pred and MDH-Aggr). History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: Y. Chai, H. Liu, and Y. Liu are supported by the National Natural Science Foundation of China [Grants 72342011, 72322019, 72188101, and 72402001]. Dr. L. Wang’s work was in part supported by the National Natural Science Foundation of China [Grant 72271027], Hainan Provincial Natural Science Foundation of China [Grant 726MS0458], and Beijing Institute of Technology Research Fund Program for Young Scholars [Grant XSQD-202216004]. X. Liu is not supported by any funds or associated with any of the abovementioned funds. Supplemental Materials: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0521 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0521 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper MathOptAI.jl: Embed trained machine learning predictors into JuMP models by Oscar Dowson, Robert B. Parker, and Russell Bent.

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Integrating Mental Health and Juvenile Justice Outcomes: A Case Study of Model-Agnostic Interpretable Machine Learning by Monica Chiarini Tremblay, Arturo Castellanos, Rajiv Kohli, Erin Espinosa, and Thomas Roderick.

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring by Yidong Chai, Haoxin Liu, Xiao Liu, Liuan Wang, Yi Liu, Junqu Wang.

This distribution includes the source code to reproduce the results of the paper "Submodular Dispatching with Multiple Vehicles" by Ignacio Erazo and Alejandro Toriello, published in the INFORMS Journal of Computing. The results of the paper are provided as .pickle files, and scripts to reproduce the tables and figures are also given.

Kidney exchange problems with immunosuppressants extend the classical kidney exchange problem by allowing a limited number of arcs that do not belong to the compatibility graph to be included in the solution. Among these, the kidney exchange problem with reserve arcs (KEP-RA) assumes that any arc can be made compatible, whereas the kidney exchange problem with half-compatible arcs (KEP-HCA) assumes that this is the case only for a subset of arcs. Starting with KEP-RA, we first show that existing integer linear programming formulations for the kidney exchange problem can easily be extended to include reserve arcs. We then demonstrate that there always exists an optimal KEP-RA solution in which every cyclic set of exchanges contains at most one reserve arc, and we use this property to develop effective variable reduction procedures and new ad hoc modeling structures. We also extend these findings to the case where nondirected donors are included. Experiments show that trivial model extensions are not able to cope with medium-sized instances, whereas our enhanced models can solve instances with up to a thousand recipient-donor pairs. We also evaluate the number of transplants enabled by each reserve arc in various settings, demonstrating that although reserve arcs tend to have a diminishing return, there are instances for which this is not the case. Finally, we demonstrate how our techniques can be integrated into a variable and constraint generation algorithm to solve KEP-HCA and show that its performance complements that of a trivial model extension. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: D. Manlove was supported by the Engineering and Physical Sciences Research Council [Grant EP/X013618/1]. This work used the Dutch national e-infrastructure with the support of the SURF Cooperative [Grants EINF-8220 and EINF-12272]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.1071 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.1071 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We present a new difference of convex functions algorithm (DCA) for solving linear and nonlinear mixed complementarity problems (MCPs). The approach is based on the reformulation of bilinear complementarity constraints as difference of convex (DC) functions, more specifically, the difference of scalar, convex quadratic terms. This reformulation gives rise to a DC program, which is solved via sequential linear approximations of the concave term using standard DCA techniques. The reformulation is based on a generalization of earlier results on recasting bilinearities and leads to a novel algorithmic framework for MCPs. Through extensive numerical experimentation, the proposed approach, referred to as DCA-BL, proves to be an efficient heuristic for complementarity problems. For linear complementarity problems (LCPs), we test the approach on a number of randomly generated instances by varying the size, the density, and the eigenvalue distribution of the LCP matrix, providing insights into the numerical properties of DCA-BL. In addition, we apply the framework to a market equilibrium problem and find that DCA-BL scales well on realistic LCP instances. Also, through experimentation, we find that that DCA-BL performs particularly well compared with other DC-based complementarity approaches in the literature if the LCP is highly dense, asymmetric, or indefinite. Lastly, the method is successfully applied to a set of equilibrium problems with second-order cone constraints, which give rise to nonlinear complementarity problems, with applications to stochastic equilibrium problems in water infrastructure and finance. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2113891], the Division of Electrical, Communications and Cyber Systems [Grant 2114100], the Office of Naval Research Global [Grant 00014-22-1-2649], Petrobras [Grant 4324713], the Division of Mathematical Sciences [Grant 2318519], and the Danmarks Frie Forskningsfond [Grant 0217-00009B]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0822 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0822 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

This repository contains the implementation of a novel deep learning model, CrowdRank, for integrating heterogeneous expert opinions, as presented in our paper "Which to Trust: Extracting Collective Wisdom Based on Opinion Quality Rank Learning".