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Extreme Bandits Using Robust Statistics

IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第3期69卷 1761-1776页

作者： Bhatt, Sujay Li, Ping Samorodnitsky, Gennady Baidu Res Cognit Comp Lab Bellevue WA 98004 USA Cornell Univ Sch Operat Res andInformat Engn ORIE Ithaca NY 14853 USA

Motivated by situations where the extreme values - as opposed to expected values in the classical stochastic multi-armed bandit (MAB) setting - are of interest, we propose a distribution-free algorithm for $\textit {extreme bandits}$ and characterize its statistical properties. The proposed novel algorithm is index based, where the index is fashioned in a non-parametric way using combinatorics and robust statistics. For distributions having "exponential-like tails" and "polynomial-like tails", we establish the following results: (i) the proposed algorithm is consistent, i.e., the index corresponding to the best arm will have the largest value asymptotically;(ii) the proposed algorithm achieves vanishing extremal regret under weaker conditions than the existing algorithms. Numerical experiments on the common class of distributions considered in the literature on extreme bandits highlight the superior finite-sample performance of the proposed algorithm compared to the state of the art.

关键词： Tail Indexes approximation algorithms Anomaly detection Object recognition Web and internet services Time measurement Extreme bandits heavy-tails robust statistics non-parametric median order statistics polynomial & exponential tails vanishing regret

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An Effective Solution Space Clipping-Based Algorithm for Large-Scale Permutation Flow Shop Scheduling Problem

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第1期53卷 635-646页

作者： Li, Yang Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The permutation flow shop scheduling problem (PFSP) is one of the most important scheduling types in the mass customization production with many real-world applications. It is also a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. However, most of the reported algorithms have not analyzed the solution space and may search many useless solution spaces, which impedes these algorithms from effectively optimizing the large-scale PFSPs in reasonable computation time. To address large-scale PFSPs with more than 100 jobs, this article proposes a solution space clipping-based improved simulated annealing (SA) algorithm. First, inspired by Johnson's rule, this article explores its essential principle and generalizes it to the general situation. Before the optimization algorithm is used to find the optimal solution, a preordering combination is performed on the processed jobs according to this extended rule to considerably clip the solution space. Second, a hybrid release strategy based on the Palmer algorithm is developed for the proposed algorithm. Then, some key operators of the SA algorithm are also improved. Finally, to verify the performance of the proposed algorithm, this work performs a set of comparative experiments with the-state-of-art methods on the part of the TA benchmark and VRF benchmark with more than 100 jobs. The experimental results show that the proposed method can achieve superior results compared to other algorithms. Furthermore, the performance of the algorithm is comprehensively analyzed, which confirms the effectiveness of the proposed method.

关键词： Job shop scheduling Heuristic algorithms Optimization Production Optimal scheduling approximation algorithms Simulated annealing Improved simulated annealing (SA) algorithm large-scale permutation flow shop scheduling solution space clipping method

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A Rational Function approximation Algorithm for the Frequency Sweep of Microwave Tube 24

A Rational Function Approximation Algorithm for the Frequenc...

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24th International Vacuum Electronics Conference, IVEC 2023

作者： Xue, Chen Xu, Li Wang, Hao Liu, Hangxin Yin, Junhui Li, Xing Li, Bin School of Electronic Science and Engineering University of Electronic Science and Technology of China No.2006 Xiyuan Ave West High-tech Zone Chengdu China Shenzhen Institute for Advanced Study UESTC Yinxing Zhijie Phase II Guanlan Street Longhua District Shenzhen518000 China

ISBN: (纸本)9781665473217

Frequency response is an important content in the design of the input/output structures of microwave tubes. Therefore, accurate and efficient computation of the frequency response of the model will undoubtedly improve our work efficiency. In this paper, we introduce an existing rational function approximation algorithm called AAA (adaptive Antoulas-Anderson). It's a speed, flexibility, and robustness algorithm. And this algorithm is extended to the frequency sweep of input/output structures of microwave tubes, by enforcing the realness condition and using the block-AAA algorithm to approximate a matrix-valued function. Through an example of the frequency sweep of a coaxial window, it is proved that the block-AAA algorithm is more accurate and faster than the Vector Fitting. © 2023 IEEE.

关键词： approximation algorithms

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Greedy Shapley Client Selection for Communication-Efficient Federated Learning

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 134-138页

作者： Singhal, Pranava Pandey, Shashi Raj Popovski, Petar Indian Institute of Technology Bombay Electrical Engineering Department Mumbai400076 India Aalborg University Connectivity Section Department of Electronic Systems Aalborg9220 Denmark

The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast convergence under practical settings characterized by significant heterogeneity in data distribution, computing, and communication resources across clients. For applications having timing constraints due to limited communication opportunities with the parameter server (PS), the client selection strategy is critical to complete model training within the fixed budget of communication rounds. To address this, we develop a biased client selection strategy, GreedyFed, that identifies and greedily selects the most contributing clients in each communication round. This method builds on a fast approximation algorithm for the Shapley Value at the PS, making the computation tractable for real-world applications with many clients. Compared to various client selection strategies on several real-world datasets, GreedyFed demonstrates fast and stable convergence with high accuracy under timing constraints and when imposing a higher degree of heterogeneity in data distribution, systems constraints, and privacy requirements. © 2019 IEEE.

关键词： approximation algorithms

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Stable Service Caching in MECs of Hierarchical Service Markets With Uncertain Request Rates

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第7期22卷 4279-4296页

作者： Xu, Zichuan Xia, Qiufen Wang, Lin Zhou, Pan Lui, John C. S. Liang, Weifa Xu, Wenzheng Wu, Guowei Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116621 Peoples R China Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116621 Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Hubei Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Sichuan Univ Sch Comp Sci 610017 Chengdu Sichuan Peoples R China

Multi-access edge computing (MEC) enables extreme low-latency AI services, such as Augmented Reality (AR) and Virtual Reality (VR), by deploying cloudlets in locations close to users. Meanwhile, a 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing for both computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network in a hierarchical service market. For the service caching problem, we first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with a provable approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed Stackelberg congestion game to measure the efficiency loss of the game due to selfishness of network service providers. Considering that the request rate of each service may not be given in advance, we study the service caching problem with the uncertainlity of request rates, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.

关键词： Mobile edge computing service caching stackelberg congestion game approximation algorithms price of anarchy

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HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2023年第1期34卷 328-345页

作者： Xu, Zichuan Zhao, Dapeng Liang, Weifa Rana, Omer F. Zhou, Pan Li, Mingchu Xu, Wenzheng Li, Hao Xia, Qiufen Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116621 Liaoning Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Cardiff Univ Phys Sci & Engn Coll Cardiff CF10 3AT Wales Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan 430074 Hubei Peoples R China Sichuan Univ Dept Comp Sci Chengdu 610065 Sichuan Peoples R China Ningxia Res Inst China Coal Res Inst Beijing 100013 Peoples R China Dalian Univ Technol Int Sch Informat Sci & Engn Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116621 Liaoning Peoples R China

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues - especially when multiple users issue concurrent FL requests. Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.

关键词： Mobile edge computing federated learning aggregator placement and UE assignment hierarchical federated learning framework approximation algorithms online learning algorithms

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Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2023年 9卷 346-356页

作者： Liu, Yang Coombes, Matthew Liu, Cunjia Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU England

Following the Bayesian inference framework, this article investigates the problem of distributed particle filtering over a sensor network to achieve consensus. The objective of the posterior-consensus strategy is to fuse the posterior probability distribution functions (PDFs) at different sensor nodes, so that an agreement of belief can be established in terms of the Kullback-Leibler average (KLA). To facilitate the consensus process and reduce the communication load, the local PDFs are approximated with weighted meshes and transmitted between neighboring nodes. The mesh representations are constructed by resorting to a grid partition of the state space, such that the PDF can be approximated by a linear combination of indicator functions. To derive a particle representation of the fused PDFs, a novel importance density function is designed to draw particles with respect to the information from all neighboring nodes. The weights of the particles are calculated via the recursive solution of the KLA. The effectiveness of the proposed filtering approach is demonstrated through two target tracking examples.

关键词： Probability density function Atmospheric measurements Particle measurements Density functional theory approximation algorithms Weight measurement Vehicle dynamics Particle filter sensor networks posterior consensus Kullback-Leibler average iterative calculation

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On the Design of Iterative Approximate Floating-Point Multipliers

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IEEE TRANSACTIONS ON COMPUTERS 2023年第6期72卷 1623-1635页

作者： Towhidy, Ahmad Omidi, Reza Mohammadi, Karim Iran Univ Sci & Technol Elect Engn Dept Tehran *** Iran Univ Zanjan Elect Engn Dept Zanjan *** Iran

multipliers provide power and area-saving for error-resilient applications. In this paper, we first propose two approximate floating-point multipliers based on two-dimensional pseudo-Booth encoding: floating-point pseudo-Booth (PB), and floating-point iterative pseudo-Booth (IPB). The accuracy of proposed multipliers can be tuned by three parameters: iteration, encoder's radix (R), and word length after truncation (W). Next, we developed the conventional iterative multipliers with a simplified steering circuit for their correction part to eliminate the power consumption of multipliers. The proposed iterative multipliers are compared with conventional iterative integer multipliers implemented by a simplified steering circuit for the floating-point area. The results reveal that the proposed PB-R4-W4 and IPB-R16-W19, compared to the exact floating-point multiplier, provide up to 98.9% and 67.5% reductions in power consumption, respectively, in TSMC 180nm CMOS technology. Also, their MRED values are, respectively, 2.9% and (7:4 x 10(-4))%. Finally, we evaluated the functionality of the proposed multipliers for real-life applications, including a hyper-plane classifier and two image processing applications of smoothing and sharpening.

关键词： Iterative methods approximation algorithms Power demand Hardware Complexity theory Adders Encoding Approximate computing Round based multiplier Image processing approximate floating-point multiplier iterative approximate multiplier

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Highly accurate σ- and τ-functionals for beyond-RPA methods with approximate exchange kernels

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JOURNAL OF CHEMICAL PHYSICS 2023年第19期159卷 1-35页

作者： Lemke, Yannick Ochsenfeld, Christian Ludwig Maximilians Univ Munchen LMU Chair Theoret Chem Dept Chem Butenandtstr 5-13 D-81377 Munich Germany Max Planck Inst Solid State Res Heisenbergstr 1 D-70569 Stuttgart Germany

sigma-Functionals are promising new developments for the Kohn-Sham correlation energy based upon the direct Random Phase approximation (dRPA) within the adiabatic connection formalism, providing impressive improvements over dRPA for a broad range of benchmarks. However, sigma-functionals exhibit a high amount of self-interaction inherited from the approximations made within dRPA. Inclusion of an exchange kernel in deriving the coupling-strength-dependent density-density response function leads to so-called tau-functionals, which - apart from a fourth-order Taylor series expansion - have only been realized in an approximate fashion so far to the best of our knowledge, most notably in the form of scaled sigma-functionals. In this work, we derive, optimize, and benchmark three types of sigma- and tau-functionals including approximate exchange effects in the form of an antisymmetrized Hartree kernel. These functionals, based on a second-order screened exchange type contribution in the adiabatic connection formalism, the electron-hole time-dependent Hartree-Fock kernel (eh-TDHF) otherwise known as RPA with exchange (RPAx), and an approximation thereof known as approximate exchange kernel (AXK), are optimized on the ASCDB database using two new parametrizations named A1 and A2. In addition, we report a first full evaluation of sigma- and tau-functionals on the GMTKN55 database, revealing our exchange-including functionals to considerably outperform existing sigma-functionals while being highly competitive with some of the best double-hybrid functionals of the original GMTKN55 publication. In particular, the sigma-functionals based on AXK and tau-functionals based on RPAx with PBE0 reference stand out as highly accurate approaches for a wide variety of chemically relevant problems.

关键词： approximation algorithms

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Realizable Reduction of Multi-Port RCL Networks by Block Elimination

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第1期70卷 399-412页

作者： Hao, Limin Shi, Guoyong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Dept Micro Nano Elect Shanghai 200240 Peoples R China

In this paper a block circuit elimination method is proposed for realizable reduction of resistor-capacitor-inductor (RCL) networks. It is an extension of a recently published realizable RC reduction method, called HD-TICER (high-dimensional time-constant equilibration reduction), to reduction of multi-port RCL networks. A modified nodal analysis (MNA) formulation is adopted in a setting for dealing with the inductive elements, which is suited for developing a realizable high-dimensional reduction scheme. A new matrix-based method is presented to establish the basic computation scheme, which seems more intuitive than the previously employed driving point impedance (DPI) method. Furthermore, a pole-zero based low-order moment matching method is introduced for the purpose of circuit recovery after reduction. The main advantages of the proposed reduction method include: 1) an intuition-based topological formulation and reduction computation method, 2) a natural port preserving reduction method, and 3) an easy-to-implement circuit recovery procedure. Experimental numerical implementation has validated the effectiveness of the proposed reduction method. Comparison to other existing methods further highlights multiple benefits of this new circuit reduction method.

关键词： Integrated circuit modeling Computational modeling Admittance Inductors approximation algorithms Voltage Very large scale integration Circuit simulation high dimensional model order reduction (MOR) realizable MOR resistor-capacitor-inductor (RCL) circuit time-constant equilibration reduction (TICER)

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