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On fairness notions in distributed systems - I. A characterization of implementability

INFORMATION AND COMPUTATION 2001年第1期166卷 1-34页

作者： Joung, YJ Natl Taiwan Univ Dept Informat Management Taipei 10764 Taiwan

This is the first pan of a two-part paper in which we discuss the implementability of fairness notions in distributed systems where asynchronous processes interact via synchronous constructs-usually called multiparty interactions. In this part we present a criterion for fairness notions and show that if a fairness notion violates the criterion, then no deterministic algorithm for scheduling multiparty interactions can satisfy the fairness notion. Conversely, the implementation is possible if the criterion is obeyed. Thus, the criterion is sufficient and necessary to guarantee the implementability of all possible fairness notions. To our knowledge, this is the first such criterion to appear in the literature. The main benefit of the proposed criterion is that it reduces reasoning about a complex and concrete implementation model to reasoning about a simpler and abstract model for process interaction. To illustrate this, we use the criterion to examine several important fairness notions, including strong interaction fairness, strong process fairness, weak processfairness, U-fairness. and hyperfairness. All, except weak process fairness, fail to pass the criterion. Moreover, we also apply the criterion to analyze the system structures rendering the impossibility phenomena. This analysis helps us separate, for each fairness notion, the set of systems for which the fairness notion can be implemented from those for which if cannot. (C) 2001 Academic Press.

关键词： deterministic algorithms Necessary Distributed Systems Sufficient INTERIM (PERMIT) STATUS TWO-COMPONENT

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On the complexity of neighbourhood learning in radio networks

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THEORETICAL COMPUTER SCIENCE 2015年第Part2期608卷 135-145页

作者： Miller, Avery Univ Toronto Dept Comp Sci Toronto ON Canada

Consider a synchronous radio network of n stationary nodes represented by an undirected graph with maximum degree Delta. Suppose that each node has a unique ID from {1,..., U}, where U >> n. In the neighbourhood learning task, each node must produce a list of the IDs of its neighbours in the network. We prove new lower bounds on the number of slots needed by certain classes of deterministic algorithms that solve this task. First, we show that O(U)-slot round-robin algorithms are optimal for the class of collision-free algorithms. Then, we consider algorithms where each node fixes its entire transmission schedule at the start. For such algorithms, we prove a Omega (Delta(2)/log Delta log U)-slot lower bound on schedule length that holds in very general models, e.g., when nodes possess collision detectors, messages can be of arbitrary size, and nodes know the schedules being followed by all other nodes. We also prove a similar result for the SINR model of radio networks. To prove these results, we consider a generalization of cover-free families of sets. We also show a separation between the class of fixed-schedule algorithms and the class of algorithms where nodes can choose to leave out some transmissions from their schedule. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Radio networks Lower bounds deterministic algorithms Neighbourhood learning task Set families

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Non-fragile H_∞ fuzzy filtering for discrete-time non-linear systems

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IET CONTROL THEORY AND APPLICATIONS 2013年第6期7卷 848-857页

作者： Ding, Da-Wei Li, Xiaoli Wang, Youyi Shi, Zhiguo Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

This study is concerned with the problem of non-fragile H-infinity filtering for discrete-time non-linear systems in Takagi-Sugeno's form. Additive interval uncertainty that reflects imprecision in filter implementation because of finite word length effects is considered. Based on vertex theory and probabilistic robust approach, deterministic and randomised filtering algorithms are proposed, respectively. Compared with the deterministic algorithm, the randomised algorithm has acceptable computational complexity, especially for high-dimensional systems. Two examples are given to illustrate the effectiveness of the proposed algorithms.

关键词： deterministic algorithms fuzzy filter randomized algorithms Filtering algorithms complexity classes discrete-time Filtration word length

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Approximate inference in hidden Markov models using iterative active state selection

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IEEE SIGNAL PROCESSING LETTERS 2006年第2期13卷 65-68页

作者： Vithanage, CM Andrieu, C Piechocki, RJ Univ Bristol Dept Math Bristol BS8 1TW Avon England Univ Bristol Commun Res Ctr Bristol BS8 1UB Avon England

The inferential task of computing the marginal posterior probability mass functions of state variables and pairs of consecutive state variables of a hidden Markov model is considered. This can be exactly and efficiently performed using a message passing scheme such as the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. We present a novel iterative reduced complexity variation of the BCJR algorithm that uses reduced support approximations for the forward and backward messages, as in the M-BCJR algorithm. Forward[backward message computation is based on the concept of expectation propagation, which results in analgorithm similar to the M-BCJR algorithm with the active state selection criterion being changed from the filtered distribution of state variables to beliefs of state variables. By allowing possibly different supports for the forward and backward messages, we derive identical forward and backward recursions that can be iterated. Simulation results of application for trellis-based equalization of a wireless communication system confirm the improved performance over the M-BCJR algorithm.

关键词： deterministic algorithms equalizers hidden Markov models (HMMs) message passing state space methods

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Dynastic accumulation of wealth

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MATHEMATICAL SOCIAL SCIENCES 2016年 81卷 66-78页

作者： Degan, Arianna Thibault, Emmanuel Univ Quebec Montreal Montreal PQ Canada CIRPEE Montreal PQ Canada Toulouse Sch Econ IDEI Toulouse France Univ Perpignan F-66025 Perpignan France

Why do some dynasties maintain the fortune of their founders while others completely squander it in few generations? To address this question, we propose a simple deterministic model combining bequests motives and labor supply. We show that the interaction of the two key parameters in the model, the "hunger for accumulation" and the "willingness to exert effort", can produce a surprisingly rich set of long-run process of wealth accumulation within a family lineage. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Mathematical sociology Wealth Families of royal descent Generations deterministic algorithms Inheritance & succession Labor supply

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Accelerated First-Order Optimization algorithms for Machine Learning

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PROCEEDINGS OF THE IEEE 2020年第11期108卷 2067-2082页

作者： Li, Huan Fang, Cong Lin, Zhouchen Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin 300071 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA Peking Univ Sch Elect Engn & Comp Sci EECS Key Lab Machine Percept MOE Beijing 100871 Peoples R China

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts have been put on designing theoretically and practically fast algorithms. This article provides a comprehensive survey on accelerated first-order algorithms with a focus on stochastic algorithms. Specifically, this article starts with reviewing the basic accelerated algorithms on deterministic convex optimization, then concentrates on their extensions to stochastic convex optimization, and at last introduces some recent developments on acceleration for nonconvex optimization.

关键词： Numerical analysis Machine learning algorithms Optimization methods Machine learning Convergence Complexity theory Approximation algorithms Convex functions Acceleration convex optimization deterministic algorithms machine learning nonconvex optimization stochastic algorithms

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A Quantum Algorithm for the Commutativity of Finite Dimensional Algebras

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IEEE ACCESS 2019年 7卷 45554-45562页

作者： Combarro, Elias F. Ranilla, Jose Rua, Ignacio F. Univ Oviedo Dept Informat Oviedo Spain

A quantum procedure for testing the commutativity of a finite dimensional algebra is introduced. This algorithm, based on Grover's quantum search, is shown to provide a quadratic speed-up (when the number of queries to the algebra multiplication constants is considered) over any classical algorithm (both deterministic and randomized) with equal success rate and shown to be optimal among the class of probabilistic quantum query algorithms. This algorithm can also be readily adapted to test commutativity and hermiticity of square matrices, again with quadratic speed-up. The results of the experiments carried out on a quantum computer simulator and on one of IBM's 5-qubit quantum computers are presented.

关键词： deterministic algorithms randomized algorithms quantum algorithms commutativity finite dimensional algebras

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Distribution network expansion planning under uncertainty: A hedging algorithm in an evolutionary approach

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IEEE TRANSACTIONS ON POWER DELIVERY 2000年第1期15卷 412-416页

作者： Carvalho, PMS Ferreira, LAFM Lobo, FG Barruncho, LMF IST Dept Eng Electrtecn & Computadores Lisbon Portugal

The paper addresses the problem of distribution network expansion planning under uncertainty. The deterministic problem is itself a large-scale, complex, difficult-to-define one;the combinatorial nature of the decision making can be efficiently handled by a specific evolutionary approach. We develop, in the evolutionary optimization context, a hedging algorithm to deal with scenario representation of uncertainty;decision making results in a convergence process for the first-stage naturally conflicting decisions, The process robustness is discussed and illustration is provided for a comprehensive example.

关键词： combinatorial mathematics convergence of numerical methods deterministic algorithms evolutionary computation numerical stability power distribution planning uncertain systems combinatorial decision making convergence process deterministic problem distrib convergence of numerical methods power distribution planning uncertain systems deterministic algorithms expansion plan Power grids combinatorial mathematics Numerical stability evolutionary algorithm

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Obtaining Significant and Interpretable Rules for Subgroup Discovery Tasks

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IEEE LATIN AMERICA TRANSACTIONS 2017年第10期15卷 2012-2016页

作者： Dominguez, J. L. Mata, J. Univ Huelva Dept Tecnol Informac Huelva Spain

In this work, a deterministic method to obtain rules for subgroup discovery tasks is described. It does not employ a previous discretization for the numeric attributes, but obtains conditions for the rules dynamically. To select the final rules, the equivalent sum of sensitivity and specificity has been used. An experimental study and appropriate statistical tests were performed, in a comparison with the classic deterministic algorithms APRIORI-SD and CN2-SD. The proposed method behaved very well, obtaining the best results in six of the eight pair-wise comparisons. From the results obtained, the described approach can be seen as a suitable deterministic way of extracting rules for subgroup discovery problems.

关键词： data mining subgroup discovery deterministic algorithms

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Determination of eigenmodes by using Cauchy's integral formula

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ELECTRONICS LETTERS 1998年第19期34卷 1865-1866页

作者： Conradi, O Fern Univ Hagen D-58084 Hagen Germany

A deterministic method is proposed for the computation of zeros within a complex plane by using Cauchy's theorem. The integrals have been calculated for different numbers of integration points by the Gaussian integration formula. As an example, several eigenmodes of a TE/TM mode splitter have been determined by the proposed method.

关键词： Gaussian integration poles and zeros eigenvalues and eigenfunctions integration eigenmodes deterministic algorithms complex plane zeros deterministic algorithm TE/TM mode splitter Cauchy integral formula Linear algebra (numerical analysis) Numerical integration and differentiation

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