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Distributed alternating gradient descent for convex semi-infinite programs over a network

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arXiv 2024年

作者： Aravind, Ashwin Chatterjee, Debasish Cherukuri, Ashish The Centre for Systems and Control Engineering Indian Institute of Technology Bombay India The Engineering and Technology Institute Groningen University of Groningen Groningen Netherlands

This paper presents a first-order distributed algorithm for solving a convex semi-infinite program (SIP) over a time-varying network. In this setting, the objective function associated with the optimization problem is a summation of a set of functions, each held by one node in a network. The semi-infinite constraint, on the other hand, is known to all agents. The nodes collectively aim to solve the problem using local data about the objective and limited communication capabilities depending on the network topology. Our algorithm is built on three key ingredients: consensus step, gradient descent in the local objective, and local gradient descent iterations in the constraint at a node when the estimate violates the semi-infinite constraint. The algorithm is constructed, and its parameters are prescribed in such a way that the iterates held by each agent provably converge to an optimizer. That is, as the algorithm progresses, the estimates achieve consensus, and the constraint violation and the error in the optimal value are bounded above by vanishing terms. A simulation example illustrates our *** Codes 90C34, 93A14 © 2024, CC BY.

关键词： consensus algorithm

An EM Gradient algorithm for Mixture Models with Components Derived from the Manly Transformation

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arXiv 2024年

作者： Clark, Katharine M. McNicholas, Paul D. Department of Mathematics & Statistics Trent University ON Canada Department of Mathematics & Statistics McMaster University ON Canada

Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parameter, λg. An alternative EM gradient algorithm is proposed, using one step of Newton’s method, when initial estimates for the model parameters are good. Copyright © 2024, The Authors. All rights reserved.

关键词： consensus algorithm

Towards a Blockchain-based IoT Platform for Buildings Energy Management integrating Electric Vehicles in Home Healthcare Context

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Towards a Blockchain-based IoT Platform for Buildings Energy...

World Conference on Complex Systems (WCCS)

作者： H. EL MIRI R. NAJI EL IDRISSI M. Najib A. NAIT SIDI MOH M. Bakhouya LERMA & TICLab International University of Rabat College of Engineering and Architecture Sala Al Jadida Morocco LASPI laboratory Saint-Etienne IUT de Roanne Jean Monnet University France Mohammed V University Mohammadia School of Engineers Rabat Morocco

ISBN: (数字)9798331519131

ISBN: (纸本)9798331519148

The electrification of the transportation sector, via the integration of battery electric-powered vehicles (BEV), is one of the solutions, which could help in reducing greenhouse gas (GHG) emission. Smart charging techniques with bidirectional flow, in which the electric power can flow back and forth (i.e., V2G and G2V) from/into the grid according the peak hours have been recently proposed for improving power quality and regulating frequency/voltage of the utility grid. In this paper, we aim to develop a blockchain-based IoT platform for peer-to-peer energy trading between BEV and smart buildings management systems in the context of home healthcare by using Hyperledger Besu; a private network based on Ethereum with the use of its consensus algorithm Clique. The developed platform's prototype made a success transactions of energy trading which makes renewable energy available to everyone. Moreover, the concrete implementation of consensus, transaction record layout, and self-implemented smart contracts make Hyperledger Besu a green framework for implementing the proposed blockchain-IoT system.

关键词： Renewable energy sources Vehicle-to-grid Smart buildings Distributed ledger Smart contracts Transportation Medical services consensus algorithm Peer-to-peer computing State of charge

An O(N) algorithm for Solving the Smallest Enclosing Sphere Problem in the Presence of Degeneracies

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arXiv 2024年

作者： Moriya, Netzer

Efficient algorithms for solving the Smallest Enclosing Sphere (SES) problem, such as Welzl's algorithm, often fail to handle degenerate subsets of points in 3D space. De- generacies and ill-posed configurations present significant challenges, leading to failures in convergence, inaccuracies or increased computational cost in such cases. Existing improvements to these algorithms, while addressing some of these issues, are either com- putationally expensive or only partially effective. In this paper, we propose a hybrid algorithm designed to mitigate degeneracy while maintaining an overall computational complexity of O(N). By combining robust preprocessing steps with efficient core com- putations, our approach avoids the pitfalls of degeneracy without sacrificing scalability. The proposed method is validated through theoretical analysis and experimental results, demonstrating its efficacy in addressing degenerate configurations and achieving high efficiency in *** Codes 49Mxx © 2024, CC BY.

关键词： consensus algorithm

New Results on Periodic Golay Pairs

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arXiv 2024年

作者： Lumsden, Tyler Kotsireas, Ilias Bright, Curtis School of Computer Science University of Windsor Canada CARGO Lab Wilfrid Laurier University Canada

In this paper, we provide algorithmic methods for conducting exhaustive searches for periodic Golay pairs. Our methods enumerate several lengths beyond the currently known state-of-the-art available searches: we conducted exhaustive searches for periodic Golay pairs of all lengths v ≤ 72 using our methods, while only lengths v ≤ 34 had previously been exhaustively enumerated. Our methods are applicable to periodic complementary sequences in general. We utilize sequence compression, a method of sequence generation derived in 2013 by Ðoković and Kotsireas. We also introduce and implement a new method of "multi-level" compression, where sequences are uncompressed in several steps. This method allowed us to exhaustively search all lengths v ≤ 72 using less than 10 CPU years. For cases of complementary sequences where uncompression is not possible, we introduce some new methods of sequence generation inspired by the isomorph-free exhaustive generation algorithm of orderly generation. Finally, we pose a conjecture regarding the structure of periodic Golay pairs and prove it holds in many lengths, including all lengths v MSC Codes 11B83, 05B20, 94A55, 68W30 Copyright © 2024, The Authors. All rights reserved.

关键词： consensus algorithm

Distributed Economic Dispatch of Multi-Microgrid Considering Environmental Cost

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Distributed Economic Dispatch of Multi-Microgrid Considering...

Complex Systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Yisha Wang Shenquan Wang Yulian Jiang Gaokai Hu College of Electrical and Electronic Engineering Changchun University of Technology Changchun China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

Based on the multi-agent consensus algorithm, a distributed economic dispatch strategy for multi-microgrid considering environmental costs is proposed. Different from the traditional centralized optimization method, this strategy takes the lowest operating cost of the generator set as the objective function and combines power balance of the multi-microgrid and output constraint of power generation unit. It can cope with technical requirements of multi-microgrid system including topological changes and “plug and play”, and is closer to the actual power generation cost as considering the environmental cost. Through distributed optimization, the communication burden can be reduced and economic dispatch of multi-microgrid achieved. The effectiveness and flexibility of this strategy are proved by simulation and analysis.

关键词： Technical requirements Costs Simulation Optimization models consensus algorithm Microgrids Linear programming Distributed power generation Plugs Multi-agent systems

Stronger adversaries grow cheaper forests: online node-weighted Steiner problems

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arXiv 2024年

作者： Borst, Sander Eliáš, Marek Venzin, Moritz MPI Informatics Germany Bocconi University Italy

We propose a O(log k log n)-competitive randomized algorithm for online node-weighted Steiner forest. This is essentially optimal and significantly improves over the previous bound of O(log2 k log n) by Hajiaghayi et al. [2017]. In fact, our result extends to the more general prize-collecting setting, improving over previous works by a poly-logarithmic factor. Our key technical contribution is a randomized online algorithm for set cover and non-metric facility location in a new adversarial model which we call semi-adaptive adversaries. As a by-product of our techniques, we obtain the first deterministic O(log |C| log |F |)-competitive algorithm for non-metric facility location. Copyright © 2024, The Authors. All rights reserved.

关键词： consensus algorithm

EvauleBlock: Evaluating Answers in E-Learning Applications with Enhanced Security and Intelligence through DRL and Blockchain Integration

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EvauleBlock: Evaluating Answers in E-Learning Applications w...

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Parthasarathy R Kiruthiga Devi M Karthick V Kumar P Rakesh Kumar Mahendran Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Dr. MGR Educational and Research Institute Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-Learning environment in terms of false data injection and modification attacks respectively. The state of the art work, either focus on designing intelligent and robust question answering model using Machine Learning (ML)/Deep Learning (DL) algorithm, or designing a secure framework for E-Learning environment using blockchain. To neutralize that issue, we design a secure and intelligent model for E-Learning environment using Deep Reinforcement Learning (DRL) and blockchain methodology respectively named EvauleBlock. The EvauleBlock composed of four major processes such as data assortment, data pre-processing, intelligent answer evaluation, and blockchain storage. In data assortment stage, the training of the proposed DRL algorithm named Multi Agent Deep Deterministic Policy Gradient (MADDPG) is taken place by crawling the related question answering data from the various websites. Followed by, we have performed pre-processing for the candidate written answers using adequate pre-processing steps. Once the answers are pre-processed, it is then provided to the MADDPG algorithm which employs four agents for validating the candidate answers by checking the semantic, syntactic, keyword, and context similarity scoring respectively. At last, the overall scores are securely stored in the blockchain using Proof of Work (PoW) consensus algorithm. The implementation of this work is carried out using python 3.11.0 tool and performance of the proposed model are validated and compared with adequate performance metrics. The results are promising when compared to the conventional models.

关键词： Training Electronic learning Semantics consensus algorithm Syntactics Proof of Work Deep reinforcement learning Question answering (information retrieval) Blockchains Security

An Inexact Boosted Difference of Convex algorithm for Nondifferentiable Functions

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arXiv 2024年

作者： Ferreira, Orizon P. Mordukhovich, Boris S. Santos, Wilkreffy M.S. Souza, João Carlos O. Institute of Mathematics and Statistics GO GoiâniaCEP 74001-970 Brazil Department of Mathematics Wayne State University DetroitMI48202 United States Department of Mathematics Federal University of Piauí PI Teresina Brazil Piauí Institute of Technology Department of Mathematics Federal University of Piauí PI Teresina Brazil

In this paper, we introduce an inexact approach to the Boosted Difference of Convex Functions algorithm (BDCA) for solving nonconvex and nondifferentiable problems involving the difference of two convex functions (DC functions). Specifically, when the first DC component is differentiable and the second may be nondifferentiable, BDCA utilizes the solution from the subproblem of the DC algorithm (DCA) to define a descent direction for the objective function. A monotone linesearch is then performed to find a new point that improves the objective function relative to the subproblem solution. This approach enhances the performance of DCA. However, if the first DC component is nondifferentiable, the BDCA direction may become an ascent direction, rendering the monotone linesearch ineffective. To address this, we propose an Inexact nonmonotone Boosted Difference of Convex algorithm (InmBDCA). This algorithm incorporates two main features of inexactness: First, the subproblem therein is solved approximately allowing us for a controlled relative error tolerance in defining the linesearch direction. Second, an inexact nonmonotone linesearch scheme is used to determine the step size for the next iteration. Under suitable assumptions, we demonstrate that InmBDCA is well-defined, with any accumulation point of the sequence generated by InmBDCA being a critical point of the problem. We also provide iteration-complexity bounds for the algorithm. Numerical experiments show that InmBDCA outperforms both the nonsmooth BDCA (nmBDCA) and the monotone version of DCA in practical *** Codes 49J53, 90C26, 65K05, 65K10 © 2024, CC BY.

关键词： consensus algorithm