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Unsupervised Deep Lagrange Dual With Equation Embedding for AC Optimal Power Flow

IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第1期40卷 1078-1090页

作者： Kim, Minsoo Kim, Hongseok Sogang Univ Dept Elect Engn Seoul 04107 South Korea

Conventional solvers are often computationally expensive for constrained optimization, particularly in large-scale and time-critical problems including AC optimal power flow (OPF) problems. While this leads to a growing interest in using neural networks (NNs) as fast optimal solution approximators, incorporating the constraints with NNs is challenging. In this regard, we propose deep Lagrange dual with equation embedding (DeepLDE), a framework that learns to find an optimal solution without using labels. To ensure feasible solutions, we embed equality constraints into the NNs and train the NNs using the primal-dual method to impose inequality constraints. The equality constraints correspond to power flow equations, and the inequality constraints include the operational limits of generators and transmission lines. We prove the convergence of DeepLDE and show that the previous primal-dual learning method cannot solely ensure equality constraints without the help of equation embedding. Simulation results on non-convex and AC-OPF problems show that the proposed DeepLDE achieves the smallest optimality gap among all the NN-based approaches while always ensuring feasible solutions. Furthermore, the computation time of the proposed method is up to 35 times faster than the baselines in solving constrained non-convex optimization, and/or AC-OPF.

关键词： Artificial neural networks Optimization Mathematical models Training Load flow linear programming Unsupervised learning Deep learning Neural network AC optimal power flow

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Index Tracking via Sparse Bayesian Regression and Collaborative Neurodynamic Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第3期55卷 1238-1249页

作者： Zhang, Fangyu Wang, Jun City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Dept Data Sci Hong Kong Peoples R China

Index tracking is a primary passive investment strategy. Many existing methods, such as cardinality-constrained and regularized regressions, need to prespecify parameters to generate sparse portfolios to track indices, which complicates the tracking procedure and may compromise tracking performance. This article addresses index tracking and enhanced index tracking via Bayesian learning and collaborative neurodynamic optimization. Specifically, we formulate a sparse Bayesian regression problem for index tracking. Furthermore, we reformulate the problem for enhanced index tracking by adding constraints based on a second-order stochastic domination rule. To overcome the nonconvexity of the objective function in the formulated problems, we propose a sparse Bayesian regression algorithm based on multiple recurrent neural networks in the collaborative neurodynamic optimization framework. We demonstrate the superiority of the proposed methods to mainstream baselines in terms of predictability, consistency, sparsity, and profitability via experimentation on the data from seven major stock markets.

关键词： Indexes Portfolios Optimization Bayes methods Target tracking Neurodynamics Vectors Collaboration Stochastic processes linear programming Collaborative neurodynamic optimization enhanced index tracking index tracking recurrent neural network sparse Bayesian regression

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Addressing the multiplicity of optimal solutions to the Clonal Deconvolution and Evolution Problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期320卷 777-788页

作者： Tellaetxe-Abete, Maitena Lawrie, Charles Calvo, Borja Biodonostia Hlth Res Inst Mol Oncol Grp Paseo Dr Begiristain Paseo Dr Donostia San Sebastian 20014 Spain Univ Basque Country UPV EHU Fac Comp Sci Intelligent Syst Grp Paseo Manuel de Lardizabal 1 E-20018 Donostia San Sebastian Gipuzkoa Spain Ikerbasque Basque Fdn Sci E-48011 Bilbao Spain Univ Oxford Radcliffe Dept Med Oxford England Shanghai Univ Sino Swiss Inst Adv Technol SSIAT Shanghai Peoples R China

The Clonal Deconvolution and Evolution Problem consists on unraveling the clonal structure and phylogeny of a tumor using estimated mutation frequency values obtained from multiple biopsies containing mixtures of tumor clones. In this article, we tackle the problem from an optimization perspective and we explore the number of optimal solutions for a given instance. Even in ideal scenarios without noise, we demonstrate that the Clonal Deconvolution and Evolution Problem is highly under-determined, leading to multiple solutions. Through a comprehensive analysis, we examine the factors contributing to the multiplicity of solutions. We find that as the number of samples increases, the number of optimal solutions decreases. Additionally, we explore how this phenomenon operates across various tumor topology scenarios. To address the issue of the existence of multiple solutions, we present sufficient conditions under which the problem can have a unique solution, and we propose a linear programming-based algorithm that leverages mutation orderings to generate instances with a single solution for a given topology. This algorithm encounters numerical challenges when applied to large instance sizes so, to overcome this, we propose a heuristic adaptation that enables the algorithm's use for instances of any size.

关键词： Bioinformatics Clonal deconvolution and evolution problem Tumor phylogenetics Combinatorial algorithm linear programming

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Active RIS-Assisted Secure Communications Against Simultaneous Jamming and Eavesdropping

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第3期14卷 686-690页

作者： Ma, Rui Peng, Yuyang Ye, Runlong Yue, Ming Al-Hazemi, Fawaz Lee, Juho Macau Univ Sci & Technol Sch Comp Sci & Engn Macau Peoples R China Zhuhai MUST Sci & Technol Res Inst Zhuhai 519087 Peoples R China Univ Jeddah Dept Comp & Network Engn Jeddah 21959 Saudi Arabia Samsung Elect Samsung Res Seoul South Korea

Due to the broadcasting characteristics of wireless channels, both jammers and eavesdroppers pose a serious threat to wireless communications. In this letter, we propose a scheme using active reconfigurable intelligent surface (RIS) to assist secure communication and prevent simultaneous jamming and eavesdropping. In this scheme, we assume that the jammer only interferes with the legitimate user, due to the potential cooperation between the eavesdropper and the jammer. To maximize the secrecy rate, a design problem of joint optimization of base station beamforming, and amplification factor and phase shifts matrices at the active RIS is proposed. Due to the non-convexity and coupling variables of this problem, we propose to use alternating optimization and semidefinite relaxation techniques to solve this optimization problem. Simulation results show that active RIS leads to a higher system secrecy rate compared to existing schemes in the presence of both jamming and eavesdropping.

关键词： Jamming Reconfigurable intelligent surfaces Artificial intelligence Wireless communication Eavesdropping Signal to noise ratio Interference Array signal processing Vectors linear programming Reconfigurable intelligent surface secure communication jammer eavesdropper

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Simple One-Step Multi-View Clustering With Fast Similarity and Cluster Structure Learning

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1850-1854页

作者： Xia, Xianxian Huang, Dong Yang, Chen-Min He, Chaobo Wang, Chang-Dong South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510635 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China

Multi-view clustering (MVC) is essential for integrating heterogeneous data from multiple sources. However, many existing approaches are hindered by high computational complexity and the separate optimization of similarity and cluster structures. In light of these challenges, this paper presents a novel anchor-based MVC method termed simple one-step multi-view clustering with fast similarity and cluster structure learning (SONIC), which models adaptive anchor learning, multi-view similarity structure learning, and discrete cluster structure learning in a joint framework. In particular, we employ the anchor-based multi-view similarity learning to capture the consensus manifold structure latent in multiple views, thereby constructing a unified bipartite graph with adaptive anchor learning and view weighting. Then we impose a low-rank constraint on the bipartite graph structure to directly reveal the desired number of clusters without additional post-processing. An efficient alternating minimization algorithm is developed to optimize the model, resulting in a computational complexity that scales linearly with the number of samples. Extensive experiments on eight benchmark datasets demonstrate the superior performance of SONIC in both clustering quality and computational efficiency.

关键词： Optimization Bipartite graph Similarity learning linear programming Adaptation models Vectors Signal processing algorithms Laplace equations Clustering algorithms Training Consensus information data clustering lare-scale clustering linear time multi-view clustering

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Outlier Indicator Based Projection Fuzzy K-Means Clustering for Hyperspectral Image

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 496-500页

作者： Liu, Xinze Yang, Xiaojun Zhang, Jiale Wang, Jing Nie, Feiping Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Informat Photon Technol China Key Lab Photon Technol Integrated Sensing & Minist Educ China Guangzhou 510006 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The application of hyperspectral image (HSI) clustering has become widely used in the field of remote sensing. Traditional fuzzy K-means clustering methods often struggle with HSI data due to the significant levels of noise, consequently resulting in segmentation inaccuracies. To address this limitation, this letter introduces an innovative outlier indicator-based projection fuzzy K-means clustering (OIPFK) algorithm for clustering of HSI data, enhancing the efficacy and robustness of previous fuzzy K-means methodologies through a two-pronged strategy. Initially, an outlier indicator vector is constructed to identify noise and outliers by computing the distances between each data point in a reduced dimensional space. Subsequently, the OIPFK algorithm incorporates the fuzzy membership relationships between samples and clustering centers within this lower-dimensional framework, along with the integration of the outlier indicator vectors, to significantly mitigates the influence of noise and extraneous features. Moreover, an efficient iterative optimization algorithm is employed to address the optimization challenges inherent to OIPKM. Experimental results from three real-world hyperspectral image datasets demonstrate the effectiveness and superiority of our proposed method.

关键词： Hyperspectral imaging Optimization Noise Vectors Clustering algorithms Robustness linear programming Signal processing algorithms Photonics Indexes Projection fuzzy K-means hyperspectral image robustness outliers clustering

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Multi-Physical Field Coupling Dynamic Numerical Modeling for Augmented Rail Launchers

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IEEE TRANSACTIONS ON MAGNETICS 2025年第3期61卷

作者： Yan, Rongge Zhao, Haokai Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300401 Peoples R China

Contact resistance voltage drop and back electromotive force generated during armature motion are the important parts of muzzle voltage of augmented rail launchers. An inversion method is proposed to accurately obtain the contact resistance between the armature and the rails and the mutual inductance gradient between the inner and outer rails, whose influence on the motion characteristics of augmented rail launchers is further analyzed. First, muzzle voltage, excitation current, armature velocity, and displacement are measured for the contact resistance calculation. Then, a modified adaptive annealing algorithm is used for the inversion of the contact resistance and mutual inductance gradient. Finally, an electromagnetic-force multi-physical field coupling dynamic numerical model for augmented rail launchers is established. The changes of the electromagnetic thrust and current density with the contact resistance and mutual inductance gradient are analyzed, and the calculated armature velocity is compared with the experimental one to verify the effectiveness of the proposed method.

关键词： Rails Contact resistance Armature Inductance Electromagnetics Voltage measurement Temperature measurement Electrical resistance measurement linear programming Current measurement electromagnetic thrust parameter inversion rail launchers

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A simple 4-approximation algorithm for maximum agreement forests on multiple unrooted binary trees

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INFORMATION PROCESSING LETTERS 2025年 190卷

作者： Dempsey, Jordan van Iersel, Leo Jones, Mark Zeh, Norbert Dalhousie Univ Fac Comp Sci 6050 Univ Ave Halifax NS B3H 1W5 Canada Delft Univ Technol Delft Inst Appl Math Mekelweg 4 NL-2628 CD Delft Netherlands

Maximum agreement forests have been used as a measure of dissimilarity of two or more phylogenetic trees on a given set of taxa. An agreement forest is a set of trees that can be obtained from each of the input trees by deleting edges and suppressing degree-2 vertices. A maximum agreement forest is such a forest with the minimum number of components. We present a simple 4-approximation algorithm for computing a maximum agreement forest of multiple unrooted binary trees. This algorithm applies LP rounding to an extension of a recent ILP formulation of the maximum agreement forest problem on two trees by Van Wersch et al. [13]. We achieve the same approximation ratio as the algorithm by Chen et al. [3], but our algorithm is extremely simple. We also prove that no algorithm based on the ILP formulation by Van Wersch et al. can achieve an approximation ratio of 4 -epsilon, for any epsilon > 0, even on two trees. To this end, we prove that the integrality gap of the ILP approaches 4 as the size of the two input trees grows.

关键词： Phylogenetic trees TBR distance Agreement forests Approximation algorithms linear programming LP rounding

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Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 187-204页

作者： Stripinis, Linas Kudela, Jakub Paulavicius, Remigijus Vilnius Univ Inst Data Sci & Digital Technol LT-08663 Vilnius Lithuania Brno Univ Technol Inst Automat & Comp Sci Brno 61600 Czech Republic

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.

关键词： Optimization Partitioning algorithms Evolutionary computation Benchmark testing Stochastic processes linear programming Closed box Derivative-free global optimization deterministic algorithms evolutionary computation (EC) algorithms nature-inspired meta-heuristics numerical benchmarking

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A New Extensible Feature Matching Model for Corrosion Defects Based on Consecutive In-Line Inspections and Data Clustering

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APPLIED SCIENCES-BASEL 2025年第6期15卷 2943-2943页

作者： Shatnawi, Mohamad Foldesi, Peter Szecheny Istvan Univ Doctoral Sch Multidisciplinary Engn Sci H-9026 Gyor Hungary Szecheny Istvan Univ Dept Logist H-9026 Gyor Hungary

Featured Application The proposed framework introduces a new feature matching approach for corroded pipelines based on in-line inspections and data clustering, contributing to the broader field of pipeline integrity management. The effectiveness of this framework suggests potential for application in other domains that benefit from spatial feature *** Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique-directional epsilon neighborhood clustering (DENC)-is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management.

关键词： pipeline integrity corrosion management directional epsilon neighborhood clustering (DENC) affine transformation linear optimization linear programming

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