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Annual Allerton Conference on Communication, Control, and Computing

作者： Braden C. Soper Caleb J. Miller Dan M. Merl Lawrence Livermore National Laboratory Center for Applied Scientific Computing Livermore CA USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

Linearly solvable Markov decision processes (LSMDPs) are a special class of Markov decision processes (MDPs) in which the optimal value function under an exponential transformation satisfies a linear equation. This model was previously extended to a class of two-player, zero-sum Markov games in which the game's equilibrium value function can similarly be derived from a linear equation. In this work, a new class of linearly solvable n-player, general-sum Markov games is proposed. We show that games in this class have a unique Nash equilibrium, and the equilibrium value functions can be derived from a single system of linear equations. We demonstrate how to approximate discrete-state, discrete-action Markov games outside this class through an embedding process analogous to the way LSMDPs approximate standard MDPs. An off-policy reinforcement learning algorithm for general-sum Markov games, which we call Nash-Z learning, is presented. We empirically demonstrate that Nash-Z learning finds equilibrium policies with low regret and that it finds these solutions orders of magnitude faster than the classic Nash-Q learning algorithm.

关键词： Computational modeling Markov decision processes Games Reinforcement learning Gaussian processes approximation algorithms Nash equilibrium Mathematical models Function approximation Standards

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Faster Quantum algorithms with "Fractional"-Truncated Series

arXiv

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arXiv 2024年

作者： Wang, Yue Zhao, Qi Department of Computer Science City University of Hong Kong Pokfulam Road Hong Kong

Quantum algorithms frequently rely on truncated series approximations, which typically require high truncation orders for adequate accuracy, leading to impractical circuit complexity. In response, we introduce Randomized Truncated Series (RTS), a framework that significantly reduces circuit depth by quadratically improving truncation error and enabling continuous adjustment of the effective truncation order. RTS leverages random mixing of series expansions to achieve these enhancements. We generalize the mixing lemma to near-unitary instances to support our error analysis and demonstrate the versatility of RTS through applications in linear combinations of unitaries, quantum signal processing, and quantum differential equations. Our results shed light on the path toward practical quantum advantage. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Receiver-Side Lookup-Table-Assisted Equalization for PAM4 IM/DD Transmission Systems

Receiver-Side Lookup-Table-Assisted Equalization for PAM4 IM...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Junde Lu Yu Sun Jun Qin Junxiong Tan Qian Wang Jie Shi Lanling Chen Yueqin Li Jian Sun Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing China

We experimentally demonstrate the performance of the receiver (Rx)-side lookup-table (LUT)-assisted equalization, which has not been previously explored in the receiver for 4-ary pulse amplitude modulation (PAM4) inte... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Recurrent neural networks Asia Receivers approximation algorithms Optical fiber communication Table lookup Intensity modulation Computational complexity Long short term memory Photonics

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An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures

An Achievable and Analytic Solution to Information Bottlenec...

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IEEE International Symposium on Information Theory

作者： Yi Song Kai Wan Zhenyu Liao Hao Xu Giuseppe Caire Shlomo Shamai Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we consider a remote source coding problem with binary phase shift keying (BPSK) modulation sources, where observations are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs further compression to find the optimal trade-off between complexity and relevance. This problem can be formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations, for which no closed-form solution is known. To address this challenge, we propose a unified achievable scheme that employs three different compression strategies for intermediate node processing, i.e., two-level quantization, multi-level deterministic quantization, and soft quantization with tanh function. Comparative analyses with existing methods, such as the Blahut-Arimoto (BA) algorithm and the Information Dropout approach, are performed through numerical evaluations. The proposed analytic scheme is observed to consistently approach the (numerically) optimal performance over a range of signal-to-noise ratios (SNRs), confirming its effectiveness in the considered setting.

关键词： Quantization (signal) Closed-form solutions AWGN Phase modulation Source coding approximation algorithms Binary phase shift keying

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Federated Combinatorial Multi-Agent Multi-Armed Bandits

arXiv

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arXiv 2024年

作者： Fourati, Fares Alouini, Mohamed-Slim Aggarwal, Vaneet Thuwal Saudi Arabia School of Industrial Engineering Purdue University West LafayetteIN47907 United States

This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals. Our framework transforms any offline resilient single-agent (α − ϵ)-approximation algorithm—having a complexity of Õ ( ϵψβ ) , where the logarithm is omitted, for some function ψ and constant β—into an online multi-agent algorithm with m communicating agents and an α-regret of no more than Õ ( m−3+ 1 β ψ 3+ 1 β T 2+ 3+ β β ) . Our approach not only eliminates the ϵ approximation error but also ensures sublinear growth with respect to the time horizon T and demonstrates a linear speedup with an increasing number of communicating agents. Additionally, the algorithm is notably communication-efficient, requiring only a sublinear number of communication rounds, quantified as Õ ( ψT β+1 β ) . Furthermore, the framework has been successfully applied to online stochastic submodular maximization using various offline algorithms, yielding the first results for both single-agent and multi-agent settings and recovering specialized single-agent theoretical guarantees. We empirically validate our approach to a stochastic data summarization problem, illustrating the effectiveness of the proposed framework, even in single-agent scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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Adaptive Meta-Learning-based KKL Observer Design for Nonlinear Dynamical Systems

Adaptive Meta-Learning-based KKL Observer Design for Nonline...

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European Control Conference (ECC)

作者： Lukas Trommer Halil Yiğit Öksüz Control Systems Group Technical University of Berlin Germany Control Systems Group and Science of Intelligence Research Cluster of Excellence Technical University of Berlin Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The theory of Kazantzis-Kravaris/Luenberger (KKL) observer design introduces a methodology that uses a nonlinear transformation map and its left inverse to estimate the state of a nonlinear system through the introduction of a linear observer state space. Data-driven approaches using artificial neural networks have demonstrated the ability to accurately approximate these transformation maps. This paper presents a novel approach to observer design for nonlinear dynamical systems through meta-learning, a concept in machine learning that aims to optimize learning models for fast adaptation to a distribution of tasks through an improved focus on the intrinsic properties of the underlying learning problem. We introduce a framework that leverages information from measurements of the system output to design a learning-based KKL observer capable of online adaptation to a variety of system conditions and attributes. To validate the effectiveness of our approach, we present comprehensive experimental results for the estimation of nonlinear system states with varying initial conditions and internal parameters, demonstrating high accuracy, generalization capability, and robustness against noise.

关键词： Metalearning Training Time-frequency analysis Accuracy Estimation Observers approximation algorithms

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Near-Optimal Content Service Algorithm with Procurement and Placement in Edge Networks

Near-Optimal Content Service Algorithm with Procurement and ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Telecom carriers have announced new content services by making a partnership with content providers and offered popular video streaming to customers. Via the integration with edge networks, telecom carriers can procure various contents from content providers and place them on edge servers proximate to end users to serve real-time requests with high bandwidth and ultra-low latency. Nevertheless, it is challenging to consider the content procurement, placement, and services jointly due to the user preference, user distribution, storage capacity of edge server, economic costs, etc. Telecom carriers would like to balance the procuring, placing, and transfer costs. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. Finally, the simulation results manifest that our algorithm outperforms other baselines.

关键词： Procurement Costs Simulation Streaming media approximation algorithms Real-time systems Telecommunications Servers User preference Optimization

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Coevolution with Bee Colony: Paradigm Shift in Many Objective Optimization

Coevolution with Bee Colony: Paradigm Shift in Many Objectiv...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Selina Khoirom Shreya Pramanik Pratyusha Rakshit Archana Chowdhury Department of Electronics and Telecommunication Engineering Jadavpur University India Department of Computer Science and Engineering Christian College Of Engineering and Technology Bhilai

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The research offers a unique coevolution-based many-objective optimization (MaOO) approach to benefit from the underlying parallelism of the evolutionary process. The proposed MaOO handles individual objectives in parallel using artificial bee colony (ABC) algorithm. After achieving convergence, a group of good-quality solutions is carefully chosen from each ABC population, dealing with a specific objective. Next, the groups are combined to form a union set. Finally, a fuzzy membership-induced rank measure is developed to discover the top-ranked equally good members of the union set, showing the approximate Pareto optimal solutions to the given MaOO problem. The proposed MaOO algorithm, referred to as fuzzy-bee colony (FBC) is compared with three state-of-the-art techniques. Experiments undertaken reveal that FBC outperforms its contenders with respect to the performance metrics.

关键词： Measurement Technological innovation Decision making Focusing Pareto optimization Parallel processing approximation algorithms

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Graph Neural Network-Based Low-Complexity Channel Estimation for Massive MU-MIMO Systems

Graph Neural Network-Based Low-Complexity Channel Estimation...

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International Conference on Computers, Communications, and Systems (ICCCS)

作者： Qiming Peng Yuxiang Huang Wuyang Zhou CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Massive Multiple-Input Multiple-Output (MIMO) technology constitutes a fundamental component of 5G networks. As the system scales with an increasing number of antennas and users, the dimensionality of the channel matrix expands, elevating the computational complexity of channel estimation algorithms as a critical concern. For the single-cell multi-user large-scale MIMO channel estimation problem in time-division duplex (TDD) mode with uniform planar array (UPA) antennas, we proposes a structured computational theoretical approach using the Toeplitz structure of the covariance matrix to greatly reduce the computational complexity of the large-dimensional matrix in the Linear Minimum Mean Squared Error (LMMSE) channel estimation algorithm. As for the estimation of the sample covariance matrix, we proposes a redundant averaging method to estimate the covariance matrix of the channel. However, since the redundant averaging method requires a sufficiently large number of samples to achieve a certain accuracy, a graph neural network channel estimator is proposed to optimize it. Simulation results show that the proposed algorithm can reduce the computational complexity while ensuring good performance.

关键词： Transmission line matrix methods Simulation Channel estimation Estimation approximation algorithms Graph neural networks Covariance matrices

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Optimizing Air Cargo Delivery Routes with a Modified Artificial Bee Colony

Optimizing Air Cargo Delivery Routes with a Modified Artific...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Valeriana Lukitosari Sena Safarina Farhan Hafizh Sentot Didik Surjanto Laksmi Prita Wardhani Departement of Mathematics Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Airplanes serve as the primary means of transporting valuable and urgent items. Minimizing the costs associated with shipping, transshipment, and airport operational expenses, network design and air cargo delivery routes should be planned. Determining the minimum-cost air cargo delivery scheme necessitates basing the delivery routes on airport network connections. A MATLAB-based mixed integer linear programming model for six Indonesian airports’ air cargo flight route selection was resolved using Artificial Bee Colony (ABC), Modified ABC, and metaheuristic methods after implementation with the cplexmilp function. In numerical simulations, cplexmilp had the smallest objective value compared to ABC and Modified ABC, with decreases of 10.41% and 3.23%. 6.96% is the difference between the minimum objective values obtained using the Modified ABC and ABC algorithms. The Modified ABC algorithm outperforms the ABC algorithm in minimizing air cargo shipping costs.

关键词： Costs Sensitivity analysis Atmospheric modeling Computational modeling Heuristic algorithms Artificial bee colony algorithm approximation algorithms Airports Mathematical models Numerical models

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