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Systematic Design of Decentralized algorithms for Consensus optimization

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IEEE CONTROL SYSTEMS LETTERS 2019年第4期3卷 966-971页

作者： Han, Shuo Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

We propose a separation principle that enables a systematic way of designing decentralized algorithms used in consensus optimization. Specifically, we show that a decentralized optimization algorithm can be constructed by combining a non-decentralized base optimization algorithm and decentralized consensus tracking. The separation principle provides modularity in both the design and analysis of algorithms under an automated convergence analysis framework using integral quadratic constraints (IQCs). We show that consensus tracking can be incorporated into the IQC-based analysis. The workflow is illustrated through the design and analysis of a decentralized algorithm based on the alternating direction method of multipliers.

关键词： optimization algorithms robust control

PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

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PEbfs: Implement High-Performance Breadth-First Search on P...

24th International Conference on algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. Through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

MODIFIED ADAPTIVE TREE-STRUCTURED PARZEN ESTIMATOR FOR HYPERPARAMETER optimization

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arXiv 2025年

作者： Sieradzki, Szymon Mańdziuk, Jacek Faculty of Mathematics and Information Science Warsaw University of Technology Koszykowa 75 Warsaw Poland Faculty of Computer Science AGH University of Krakow A. Mickiewicza 30 Krakow Poland

In this paper we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm. We propose several modifications to ATPE and assess their efficacy on a diverse set of standard benchmark functions. Experimental results demonstrate that the proposed modifications significantly improve the effectiveness of ATPE hyperparameter optimization on selected benchmarks, a finding that holds practical relevance for their application in real-world machine learning / optimization tasks. © 2025, CC BY-NC-SA.

关键词： optimization algorithms

A consensus-based optimization method for nonsmooth nonconvex programs with approximated gradient descent scheme

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arXiv 2025年

作者： Wei, Jiazhen Wu, Fan Bian, Wei School of Mathematics Harbin Institute of Technology Harbin China

In this paper, we are interested in finding the global minimizer of a nonsmooth nonconvex unconstrained optimization problem. By combining the discrete consensus-based optimization (CBO) algorithm and the gradient descent method, we develop a novel CBO algorithm with an extra gradient descent scheme evaluated by the forward-difference technique on the function values, where only the objective function values are used in the proposed algorithm. First, we prove that the proposed algorithm can exhibit global consensus in an exponential rate in two senses and possess a unique global consensus point. Second, we evaluate the error estimate between the objective function value on the global consensus point and its global minimum. In particular, as the parameter β tends to ∞, the error converges to zero and the convergence rate is O (logββ) . Third, under some suitable assumptions on the objective function, we provide the number of iterations required for the mean square error in expectation to reach the desired accuracy. It is worth underlining that the theoretical analysis in this paper does not use the mean-field limit. Finally, we illustrate the improved efficiency and promising performance of our novel CBO method through some experiments on several nonconvex benchmark problems and the application to train deep neural networks. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Stochastic Gradient Descent for Constrained optimization based on Adaptive Relaxed Barrier Functions

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arXiv 2025年

作者： Dimitrieski, Naum Cao, Jing Ebenbauer, Christian Chair of Intelligent Control Systems RWTH Aachen University Aachen52062 Germany

This paper presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum objective function and relies on a relaxed logarithmic barrier function that is appropriately adapted in each optimization iteration. For a strongly convex objective function and affine inequality constraints, step-size rules and barrier adaptation rules are established that guarantee asymptotic convergence with probability one. The theoretical results in the paper are complemented by numerical studies which highlight potential advantages of the proposed algorithm for optimization problems with a large number of constraints. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Preference optimization via Contrastive Divergence: Your Reward Model is Secretly an NLL Estimator

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arXiv 2025年

作者： Chen, Zhuotong Liu, Fang Zhu, Xuan Wang, Haozhu Qi, Yanjun Ghavamzadeh, Mohammad Amazon Web Services Santa ClaraCA United States SunnyvaleCA United States

Existing studies on preference optimization (PO) have centered on constructing pairwise preference data following simple heuristics, such as maximizing the margin between preferred and dispreferred completions based on human (or AI) ranked scores. However, none of these heuristics has a full theoretical justification. In this work, we develop a novel PO framework that provides theoretical guidance to effectively sample dispreferred completions. To achieve this, we formulate PO as minimizing the negative log-likelihood (NLL) of a probability model and propose to estimate its normalization constant via a sampling strategy. As we will demonstrate, these estimative samples can act as dispreferred completions in PO. We then select contrastive divergence (CD) as the sampling strategy, and propose a novel MC-PO algorithm that applies the Monte Carlo (MC) kernel from CD to sample hard negatives w.r.t. the parameterized reward model. Finally, we propose the OnMC-PO algorithm, an extension of MC-PO to the online setting. On popular alignment benchmarks, MC-PO outperforms existing SOTA baselines, and OnMC-PO leads to further improvement. © 2025, CC BY.

关键词： optimization algorithms

Multi-constraint optimization for Energy Scheduling of Plug-In Electric Vehicles 8th

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Multi-constraint Optimization for Energy Scheduling of Plu...

8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Linxin Yang, Zhile Guo, Yuanjun Xiao, Qinge Zhu, Juncheng Faculty of Data Science City University of Macau Taipa999078 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

ISBN: (纸本)9789819602315

The integration of a substantial number of plug-in electric vehicles (PEVs) into power grid scheduling introduces complexities due to stochastic charging and discharging behaviors, which pose significant challenges to the stability and reliability of the power system. The PEV scheduling optimization extends the traditional combinatorial optimization problem, adding constraints and variables, and becomes a multi-constraint unit commitment (MCUC)problem. In this paper, a novel PEV scheduling optimization framework is proposed to address MCUC problem, taking economic cost and carbon emissions as the objectives, a multi-constraint mathematical model is established. Meanwhile, a new binary level-based learning optimization algorithm is proposed to solve flexible scheduling of PEVs in power systems. Finally, considering different numbers of units, the superiority of the proposed optimization algorithm is verified by comparison with different algorithms. The feasibility and effectiveness of the proposed solution are also analyzed before and after scheduling to verify its potential in solving the MCUC problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

A Centrality-Guided Modularity optimization Algorithm for Overlapping and Nested Community Detection in Opportunistic Networks 19th

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A Centrality-Guided Modularity Optimization Algorithm for Ov...

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wang, Shoucheng Guo, Xulong Xu, Gang Inner Mongolia University Hohhot010021 China Inner Mongolia Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Hohhot010021 China

ISBN: (纸本)9789819623723

This paper introduces a centrality-guided modularity optimization algorithm for overlapping and nested community detection (CG-MONCD), aimed at addressing community structure identification in opportunistic networks. Characterized by data transmission through sporadic encounters between nodes without the need for stable communication links, the CG-MONCD algorithm constructs a weighted graph model by quantifying the frequency and duration of encounters, as well as energy consumption. Incorporating a centrality-based initial community construction strategy and a method for identifying overlapping nodes, it effectively detects overlapping and nested communities within the network. Experimental results demonstrate that this algorithm outperforms existing methods in community detection, modularity optimization, and intra-community edge density, accurately revealing the network's nested communities and hierarchical features, thereby enhancing communication efficiency and network stability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

Local Optimisation Based Detection of Weak Targets in Isolated Forests 24

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Local Optimisation Based Detection of Weak Targets in Isolat...

8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Hu, Jurong Xing, Yanxiao Li, Yuxiao School of Information Science and Engineering Hohai University Jiangsu Nanjing China School of Computer and Software Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the iForest algorithm based on an improved splitting strategy. The algorithm introduces the Gini impurity, incorporates the Gini impurity parameter into the splitting strategy, and is more targeted in the selection of splitting attributes and splitting points, thus better adapting to the problem of detecting weak targets under different sea states, and has an excellent performance of high accuracy, low time duration, and low false alarm rate compared with the traditional methods in anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms