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arXiv 2025年

作者： Chen, Zhirui Karthik, P.N. Chee, Yeow Meng Tan, Vincent Y.F. Department of Electrical and Computer Engineering Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Artificial Intelligence Indian Institute of Technology Hyderabad India

We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an M-dimensional vector reward upon selection. We assume that the reward of each dimension (a.k.a. objective) is generated independently of the others. The best arm of any given objective is the arm with the largest component of mean corresponding to the objective. The end goal is to identify the best arm of every objective in the shortest (expected) time subject to an upper bound on the probability of error (i.e., fixed-confidence regime). We establish a problem-dependent lower bound on the limiting growth rate of the expected stopping time, in the limit of vanishing error probabilities. This lower bound, we show, is characterised by a max-min optimisation problem that is computationally expensive to solve at each time step. We propose an algorithm that uses the novel idea of surrogate proportions to sample the arms at each time step, eliminating the need to solve the max-min optimisation problem at each step. We demonstrate theoretically that our algorithm is asymptotically optimal. In addition, we provide extensive empirical studies to substantiate the efficiency of our algorithm. While existing works on pure exploration with multi-objective multi-armed bandits predominantly focus on Pareto frontier identification, our work fills the gap in the literature by conducting a formal investigation of the multi-objective best arm identification problem. Copyright © 2025, The Authors. All rights reserved.

关键词： Pareto principle

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Personalized Education with Generative AI and Digital Twins: VR, RAG, and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development

arXiv

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arXiv 2025年

作者： Lin, Yu-Zheng Petal, Karan Alhamadah, Ahmed H. Ghimire, Sujan Redondo, Matthew William Corona, David Rafael Vidal Pacheco, Jesus Salehi, Soheil Satam, Pratik Department of Electrical and Computer Engineering University of Arizona TucsonAZ United States Department of Systems and Industrial Engineering University of Arizona TucsonAZ United States Department of Industrial Engineering University of Sonora Hermosillo Mexico

While the advent of the Fourth industrial Revolution (4IR) technologies, like cloud computing, machine learning, and artificial intelligence have brought convenience and productivity improvements, they have also introduced new challenges in training and education that require the reskilling of existing employees and the building of a new workforce. Exacerbated by the already existing workforce shortages, this mammoth workforce reskilling and building effort aims to build a high-tech workforce capable of operating and maintaining these 4IR systems;requiring a higher student retention and persistence. This increase in student retention and persistence will be especially critical when training the workforce originating from marginalized communities like Underrepresented Minorities (URM), where challenges arise due to lack of access to high-quality education throughout the trainee’s formative years (pre/middle/high schools), creating a cyclic set of knowledge dependencies that are difficult to meet. To address these challenges, this research presents Generative AI-based Personalized Tutor for industrial 4.0 (gAI-PT4I4), a framework that focuses on personalization of 4IR experiential learning, using sentiment analysis to gauge student’s knowledge comprehension, while using a combination of generative AI and finite automaton to personalize the content to the students’ learning needs. The framework administers experiential learning, using low-fidelity Digital Twins that enable virtual reality-based (VR) training exercises focusing on 4IR training. The VR environment, integrates a generative AI teaching assistant called the Interactive Tutor, that guides the student through the training exercises, with audio and text communications. The gAI-PT4I4 uses these audio/text communications between the tutor and the trainee to perform sentiment analysis using a novel zero-shot learning pipeline, that uses Large Language Models (LLMs), with prompt engineering to evaluate student sen

关键词： Students

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A Deep Neural Network-based Frequency Predictor for Frequency-Constrained Optimal Power Flow

arXiv

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arXiv 2025年

作者： Jiang, Fan Li, Xingpeng Van Hentenryck, Pascal Department of Electrical and Computer Engineering University of Houston HoustonTX United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

Rate of change of frequency (RoCoF) and frequency nadir should be considered in real-time frequency-constrained optimal power flow (FCOPF) to ensure frequency stability of the modern power systems. Since calculating the frequency response is complex, deep neural network (DNN) could be adopted to capture the nonlinearities and estimate those two metrics accurately. Therefore, in this paper, a DNN-based frequency predictor is developed with the training data obtained from time-domain simulations using PSCAD/EMTDC. Subsequently, it is reformulated using a set of mixed-integer linear programming formulations and then embedded into the FCOPF framework as constraints to ensure grid frequency stability, creating the proposed DNN-FCOPF model. Two benchmark models, a traditional OPF without any frequency constraints and a linear system-wide RoCoF-constrained FCOPF, are also implemented to gauge the proposed DNN-FCOPF. Finally, the solutions obtained with these three models are compared and evaluated with time-domain simulations using PSCAD under various load profiles, demonstrating the effectiveness of the proposed DNN-FCOPF. © 2025, CC BY-NC-SA.

关键词： Frequency stability

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A Preprocessing Method for Security-Constrained Unit Commitment with AC Power Flows

A Preprocessing Method for Security-Constrained Unit Commitm...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Mingze Li Weihang Ren Lei Fan Yongpei Guan Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA Dept. of Industrial and Systems Engineering University of Florida FL USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a preprocessing method to solve the security-constrained unit commitment with AC power flows (SCUC-ACPF), which is a large scale mixed-integer non-convex, nonlinear optimization problem. We introduce clustering algorithms and local search to reduce problem complexity, and obtain local optimal solutions within limited solving time and computational capacity. The clustering algorithms group buses into manageable clusters centered around core buses, creating smaller sub-grids for efficient optimization. The local search algorithms iteratively explore and improve solution quality. Our method ensures balanced clusters that respect physical and operational constraints, significantly enhancing optimization efficiency and contributing to more effective grid management and operation.

关键词： Heuristic algorithms Clustering methods Clustering algorithms Search problems Boundary conditions Power grids Complexity theory Optimization AC power transmission

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

引用

IEEE Texas Power and Energy Conference (TPEC)

作者： Weihang Ren Mingze Li Lei Fan Yongpei Guan Dept. of Industrial and Systems Engineering University of Florida FL USA Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks.

关键词： Runtime Scalability Power systems Planning Security Optimization AC power transmission Load flow

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Task Shift: From Classification to Regression in Overparameterized Linear Models

arXiv

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arXiv 2025年

作者： LaBonte, Tyler Lai, Kuo-Wei Muthukumar, Vidya H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States

Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the task shifts from classification during training to regression during evaluation. In the zero-shot case, wherein no regression data is available, we prove that task shift is impossible in both sparse signal and random signal models for any Gaussian covariate distribution. In the few-shot case, wherein limited regression data is available, we propose a simple postprocessing algorithm which asymptotically recovers the ground-truth predictor. Our analysis leverages a fine-grained characterization of individual parameters arising from minimum-norm interpolation which may be of independent interest. Our results show that while minimum-norm interpolators for classification cannot transfer to regression a priori, they experience surprisingly structured attenuation which enables successful task shift with limited additional data. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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Safe Gradient Flow for Bilevel Optimization

arXiv

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arXiv 2025年

作者： Sharifi, Sina Abolfazli, Nazanin Hamedani, Erfan Yazdandoost Fazlyab, Mahyar Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Systems and Industrial Engineering The University of Arizona TucsonAZ85721 United States

Bilevel optimization is a key framework in hierarchical decision-making, where one problem is embedded within the constraints of another. In this work, we propose a control-theoretic approach to solving bilevel optimization problems. Our method consists of two components: a gradient flow mechanism to minimize the upper-level objective and a safety filter to enforce the constraints imposed by the lower-level problem. Together, these components form a safe gradient flow that solves the bilevel problem in a single loop. To improve scalability with respect to the lower-level problem’s dimensions, we introduce a relaxed formulation and design a compact variant of the safe gradient flow. This variant minimizes the upper-level objective while ensuring the lower-level decision variable remains within a user-defined suboptimality. Using Lyapunov analysis, we establish convergence guarantees for the dynamics, proving that they converge to a neighborhood of the optimal solution. Numerical experiments further validate the effectiveness of the proposed approaches. Our contributions provide both theoretical insights and practical tools for efficiently solving bilevel optimization problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Control theory

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On de Bruijn Array Codes, Part II: Linear Codes

arXiv

引用

arXiv 2025年

作者： Chee, Yeow Meng Etzion, Tuvi Lao, Huimin Department of Industrial Systems Engineering and Management National University of Singapore Singapore Computer Science Department Technion Israel Institute of Technology Haifa3200003 Israel Department of Electrical and Computer Engineering National University of Singapore Singapore

An M-sequence generated by a primitive polynomial has many interesting and desirable properties. A pseudorandom array is the two-dimensional generalization of an M-sequence. Similarly to primitive polynomials, there are irreducible and reducible polynomials whose all nonzero sequences have the same length. In this paper, a two-dimensional generalization for such sequences is given. This generalization is for a pseudo-random array code which is a set of r1×r2 arrays in which each n1×n2 nonzero matrix is contained exactly once as a window in one of the arrays. Moreover, these arrays have the shift-and-add property, i.e., the bitwise addition of two arrays (or a nontrivial shift of such arrays) is another array (or a shift of another array) from the code. All the known arrays can be formed by folding sequences generated from an irreducible polynomial or a reducible polynomial whose factors have the same degree and the same exponent. Two proof techniques are used to prove the parameters of the constructed arrays. The first one is based on another method for constructing some of these arrays. The second one is a generalization of a known proof technique. This generalization enables to present pseudo-random arrays with parameters not known before and also a variety of pseudo-random array codes which cannot be generated by the first method. The two techniques also suggest two different hierarchies between pseudo-random array codes. Finally, a method to verify whether a folding of sequences, generated by these polynomials, yields a pseudo-random array or a pseudo-random array code, will be presented. © 2025, CC BY.

关键词： Polynomials

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A Game-Theoretic Framework for Distributed Load Balancing: Static and Dynamic Game Models

arXiv

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arXiv 2025年

作者： Fardno, Fatemeh Rasoul Etesami, S. The Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to coordinate with each other, yet the goal is to balance the load among servers in a distributed manner. First, we provide a static game formulation in which each player holds a job with a certain processing requirement and wants to schedule it fractionally among a set of heterogeneous servers to minimize its average processing time. We show that this static game is a potential game and admits a pure Nash equilibrium (NE). In particular, the best-response dynamics converge to such an NE after n iterations, where n is the number of players. We then extend our results to a dynamic game setting, where jobs arrive and get processed in the system, and players observe the load (state) on the servers to decide how to schedule their jobs among the servers in order to minimize their averaged cumulative processing time. In this setting, we show that if the players update their strategies using dynamic best-response strategies, the system eventually becomes fully load-balanced and the players' strategies converge to the pure NE of the static game. In particular, we show that the convergence time scales only polynomially with respect to the game parameters. Finally, we provide numerical results to evaluate the performance of our proposed algorithms under both static and dynamic settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Queueing networks

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Multi-Agent Performative Prediction Beyond the Insensitivity Assumption: A Case Study for Mortgage Competition

arXiv

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arXiv 2025年

作者： Wang, Guanghui Acharya, Krishna Lakshmikanthan, Lokranjan Muthukumar, Vidya Ziani, Juba College of Computing Georgia Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States

Performative prediction models account for feedback loops in decision-making processes where predictions influence future data distributions. While existing work largely assumes insensitivity of data distributions to small strategy changes, this assumption usually fails in real-world competitive (i.e. multi-agent) settings. For example, in Bertrand-type competitions, a small reduction in one firm’s price can lead that firm to capture the entire demand, while all others sharply lose all of their customers. We study a representative setting of multi-agent performative prediction in which insensitivity assumptions do not hold, and investigate the convergence of natural dynamics. To do so, we focus on a specific game that we call the "Bank Game", where two lenders compete over interest rates and credit score thresholds. Consumers act similarly as to in a Bertrand Competition, with each consumer selecting the firm with the lowest interest rate that they are eligible for based on the firms’ credit thresholds. Our analysis characterizes the equilibria of this game and demonstrates that when both firms use a common and natural no-regret learning dynamic—exponential weights—with proper initialization, the dynamics always converge to stable outcomes despite the general-sum structure. Notably, our setting admits multiple stable equilibria, with convergence dependent on initial conditions. We also provide theoretical convergence results in the stochastic case when the utility matrix is not fully known, but each learner can observe sufficiently many samples of consumers at each time step to estimate it, showing robustness to slight mis-specifications. Finally, we provide experimental results that validate our theoretical findings. © 2025, CC BY.

关键词： Prediction models

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