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1. Network utility maximization with unknown utility functions: A distributed, data-driven bilevel optimization approach

   https://doi.org/10.1145/3565287.3610260  

   Ji, K; Ying, L (October 2023, International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing)

   Fair resource allocation is one of the most important topics in communication networks. Existing solutions almost exclusively assume each user utility function is known and concave. This paper seeks to answer the following question: how to allocate resources when utility functions are unknown, even to the users? This answer has become increasingly important in the next-generation AI-aware communication networks where the user utilities are complex and their closed-forms are hard to obtain. In this paper, we provide a new solution using a distributed and data-driven bilevel optimization approach, where the lower level is a distributed network utility maximization (NUM) algorithm with concave surrogate utility functions, and the upper level is a data-driven learning algorithm to find the best surrogate utility functions that maximize the sum of true network utility. The proposed algorithm learns from data samples (utility values or gradient values) to autotune the surrogate utility functions to maximize the true network utility, so works for unknown utility functions. For the general network, we establish the nonasymptotic convergence rate of the proposed algorithm with nonconcave utility functions. The simulations validate our theoretical results and demonstrate the great effectiveness of the proposed method in a real-world network. 

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2. DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game

   https://doi.org/10.1609/aaai.v39i15.33746  

   Chen, Xiaobing; Zhou, Xiangwei; Zhang, Songyang; Sun, Mingxuan (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel federated learning framework with a dual-level game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation.At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles.At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server.Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility. 

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3. Streaming submodular maximization with differential privacy

   Chaturvedi, Anamay; Nguyen, Huy; Nguyen, Thy D (July 2023, Proceedings of Machine Learning Research)

   In this work, we study the problem of privately maximizing a submodular function in the streaming setting. Extensive work has been done on privately maximizing submodular functions in the general case when the function depends upon the private data of individuals. However, when the size of the data stream drawn from the domain of the objective function is large or arrives very fast, one must privately optimize the objective within the constraints of the streaming setting. We establish fundamental differentially private baselines for this problem and then derive better trade-offs between privacy and utility for the special case of decomposable submodular functions. A submodular function is decomposable when it can be written as a sum of submodular functions; this structure arises naturally when each summand function models the utility of an individual and the goal is to study the total utility of the whole population as in the well-known Combinatorial Public Projects Problem. Finally, we complement our theoretical analysis with experimental corroboration. 

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4. Revenue-Utility Tradeoff in Assortment Optimization Under the Multinomial Logit Model with Totally Unimodular Constraints

   https://doi.org/10.1287/mnsc.2020.3657  

   Sumida, Mika; Gallego, Guillermo; Rusmevichientong, Paat; Topaloglu, Huseyin; Davis, James (May 2021, Management Science)

   We examine the revenue–utility assortment optimization problem with the goal of finding an assortment that maximizes a linear combination of the expected revenue of the firm and the expected utility of the customer. This criterion captures the trade-off between the firm-centric objective of maximizing the expected revenue and the customer-centric objective of maximizing the expected utility. The customers choose according to the multinomial logit model, and there is a constraint on the offered assortments characterized by a totally unimodular matrix. We show that we can solve the revenue–utility assortment optimization problem by finding the assortment that maximizes only the expected revenue after adjusting the revenue of each product by the same constant. Finding the appropriate revenue adjustment requires solving a nonconvex optimization problem. We give a parametric linear program to generate a collection of candidate assortments that is guaranteed to include an optimal solution to the revenue–utility assortment optimization problem. This collection of candidate assortments also allows us to construct an efficient frontier that shows the optimal expected revenue–utility pairs as we vary the weights in the objective function. Moreover, we develop an approximation scheme that limits the number of candidate assortments while ensuring a prespecified solution quality. Finally, we discuss practical assortment optimization problems that involve totally unimodular constraints. In our computational experiments, we demonstrate that we can obtain significant improvements in the expected utility without incurring a significant loss in the expected revenue. This paper was accepted by Omar Besbes, revenue management and market analytics. 

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5. Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment

   https://doi.org/10.1115/FEDSM2022-88368  

   Patel, Parth Y.; Jannoi, Thannathorn; Zou, Wenhui; Vantsevich, Vladimir; Koomullil, Roy (September 2022, Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment)

   Global climate change has affected the human race for decades. As a result, severe weather changes and more substantial hurricane impact have become a typical scenario. Utility trucks with the morphing boom equipment are the first responders to access these disaster areas in bad weather conditions and restore the damages caused by the disaster. The stability of the utility trucks while driving in a heavy wind scenario is an essential aspect for the safety of the rescue crew, and aerodynamic forces caused by the wind flow constitute a significant factor that influences the stability of the utility truck. In this paper, the aerodynamic performance of the utility truck is modeled using the incompressible unsteady Reynolds Averaged Navier Stokes (URANS) model. The Ahmed body, a well-recognized benchmark test case used by the computational fluid dynamics (CFD) community for the aerodynamic model validation of automobiles, is used to validate this aerodynamic model. The validated aerodynamic model investigates the impact of heavy wind on the utility truck with the morphing boom equipment. The visualization of the flow field around the utility truck with the force and moment coefficients at various side slip angles are presented in this paper. 

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