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arXiv 2025年

作者： Bhattacharyya, Arnab Gayen, Sutanu Meel, Kuldeep S. Myrisiotis, Dimitrios Pavan, A. Vinodchandran, N.V. University of Warwick United Kingdom IIT Kanpur India University of Toronto Georgia Institute of Technology United States CNRS@CREATE LTD. France Iowa State University United States University of Nebraska-Lincoln United States

We study the problem of computing statistical similarity between probability distributions. For distributions P and Q over a finite sample space, their statistical similarity is defined as Sstat(P, Q):= ∑x min(P (x), Q(x)). Statistical similarity is a basic measure of similarity between distributions, with several natural interpretations, and captures the Bayes error in prediction and hypothesis testing problems. Recent work has established that, somewhat surprisingly, even for the simple class of product distributions, exactly computing statistical similarity is #P-hard. This motivates the question of designing approximation algorithms for statistical similarity. Our primary contribution is a Fully Polynomial-Time deterministic approximation Scheme (FPTAS) for estimating statistical similarity between two product distributions. To obtain this result, we introduce a new variant of the Knapsack problem, which we call the Masked Knapsack problem, and design an FPTAS to estimate the number of solutions of a multidimensional version of this problem. This new technical contribution could be of independent interest. Furthermore, we also establish a complementary hardness result. We show that it is NP-hard to estimate statistical similarity when P and Q are Bayes net distributions of in-degree 2. © 2025, CC BY.

关键词： approximation algorithms

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How do you want your greedy: simultaneous or repeated?

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 3174-3260页

作者： Moran Feldman Christopher Harshaw Amin Karbasi Department of Computer Science University of Haifa Haifa Israel Simons Institute Univeristy of California Berkeley Berkeley CA Departments of Electrical Engeering Computer Science Statsitics & Data Science Yale University New Haven CT

We present SimultaneousGreedys, a deterministic algorithm for constrained submodular maximization. At a high level, the algorithm maintains ℓ solutions and greedily updates them in a simultaneous fashion. SimultaneousGreedys achieves the tightest known approximation guarantees for both k-extendible systems and the more general k-systems, which are $(k+1)^2/k = k + \mathcal{O}(1)$ and $(1 + \sqrt{k+2})^2 = k + \mathcal{O}(\sqrt{k})$, respectively. We also improve the analysis of RepeatedGreedy, showing that it achieves an approximation ratio of k+O(√k) for k-systems when allowed to run for O(√k) iterations, an improvement in both the runtime and approximation over previous analyses. We demonstrate that both algorithms may be modified to run in nearly linear time with an arbitrarily small loss in the *** SimultaneousGreedys and RepeatedGreedy are flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees: both algorithms yield an approximation guarantee of roughly $k + 2m + \mathcal{O}(\sqrt{k+m})$ for k-systems and SimultaneousGreedys enjoys an improved approximation guarantee of k + 2m + O(√m) for k-extendible systems. To complement our algorithmic contributions, we prove that no algorithm making polynomially many oracle queries can achieve an approximation better than k + 1/2 - ε. We also present ***, a Julia package which implements these algorithms. Finally, we test these algorithms on real datasets.

关键词： submodular maximization k-systems k-extendible systems approximation algorithms

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A Linear and Scalable Cutting-Plane Algorithm for Electricity Pricing

arXiv

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arXiv 2025年

作者： Romero, Matías Verástegui, Felipe Villagra, Matías GSB Columbia University New YorkNY10027 United States IEOR Columbia University New YorkNY10027 United States

We propose a linear cutting-plane pricing algorithm tailored for large-scale electricity markets, addressing nonconvexities arising from the Alternating Current Optimal Power Flow equations. We benchmark our algorithm against a Direct Current (DC) approximation and the Jabr Second-Order Cone (SOC) relaxation under both the Integer Programming and Convex Hull pricing rules. We provide numerical results for a small (617-bus) and three large (≥ 15, 000-bus) networks. Our algorithm yields price signals very close to the Jabr SOC, with computation times comparable to DC once we allow for warm-starts, including scenarios with line contingencies. © 2025, CC BY.

关键词： approximation algorithms

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Probabilistic Forecasting-Based Operational Security Region for Power Systems With Renewable Energy Sources

Probabilistic Forecasting-Based Operational Security Region ...

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Smart Grid and Green Energy (ICSGGE), International Conference on

作者： Xuejun Hu Can Wan Zhiqiang He Zhebin Chen Zhihao He Yan Peng College of Electrical Engineering Zhejiang University Hangzhou China State Grid Zhejiang Electric Power Research Institute Hangzhou China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

The increasing penetration of renewable energy sources (RES) introduces high uncertainty and poses significant challenges to operational security in power systems. This paper proposes a novel probabilistic operational security region (POSR) model for high-penetration RES in power systems, which is based on non-parametric probabilistic forecasting to quantify RES uncertainty. The POSR model provides a local equivalent approximation of RES feasible region incorporating system operational constraints, thereby enhancing computational efficiency in high-dimensional spaces. An innovative POSR-solving algorithm is also developed, leveraging region credibility hyper-box settings and convex hull boundaries to rapidly iterate boundaries. Case studies based on IEEE 33-bus and 141-bus systems validate the effectiveness of the proposed POSR model with high computational efficiency.

关键词： Uncertainty Convex hulls Computational modeling Predictive models Probabilistic logic approximation algorithms Power systems Computational efficiency Security Forecasting

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MSE-based Sampling of Bandlimited Product Graph Signals via Joint Low-pass Impulse Responses

MSE-based Sampling of Bandlimited Product Graph Signals via ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fen Wang Baoyi Xu Xuyao Kang Peng Ren Long Yang Hangzhou Institute of Technology Xidian University Hangzhou China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Matrix graph signals, which are associated with two factor graphs, are ubiquitous in daily life, such as time-varying physical signals in sensor networks and rating matrices in recommendation systems. In practice, due to the row-wise and column-wise smoothness, they are modeled as bandlimited (BL) graph signals on the product of two factor graphs. In this paper, we propose a fast sampling method for BL product graph signals by minimizing the reconstruction mean squared error (MSE). First, we formulate the MSE-based sampling objective for BL signals on a product graph. Next, we simplify the greedy subproblem using matrix inversion approximation, which involves straightforward computations between the defined joint low-pass impulse responses (JLIRs). To eliminate the huge computation and storage complexity, we propose a strategy to directly compute the greedy evaluation score based on low-dimensional LIRs on two factor graphs, leveraging the property of Kronecker product. Employing this strategy, we also extend existing popular LIR-based methods to sample signals from single graphs to product graphs with low complexity. Extensive experiments on synthetic and real-world product graph signals demonstrate that our proposed method maintains superior MSE performance compared to other extended graph sampling schemes.

关键词： Signal processing algorithms Signal processing Sampling methods approximation algorithms Acoustics Complexity theory Speech processing Time-varying systems Recommender systems

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Active Location Detection of Adversaries in 802.11 wireless LAN (WLAN)

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Chinese Journal of Electronics 2023年第3期19卷 525-531页

作者： Siyu Zhan Chuan Han Yaling Yang Jianping Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University USA

The growing interest in location based service and wireless security in WLAN necessitates the development of an effective scheme which can locate an attacker in a WLAN to make him account for his misdemeanors and expel him out of the network. On the other hand, while localization has been an active area of research recently, current research of secure localization mostly focuses on getting correct locations of legitimate users despite the existence of malicious attacks. There is little effort on how to locate an attacker who is equipped with advanced radio technologies to distort traditional localization system's location estimation. To fill in this challenging technical void, in this paper, a novel localization scheme is proposed, called ALD, which can locate the attacker with traditional range-free localization equipments. The main idea here is to use coordination of multiple APs to locate the attacker and optimize the process with a finite horizon discrete Markov decision process (MDP). An approximation algorithm is proposed to pre-compute the MDP efficiently. The solution then can be stored in APs without requiring any strong computation capability and special hardware. The ALD scheme can be supported by IEEE 802.11 and many other wireless network standards.

关键词： Location awareness Wireless LAN Estimation error Wireless networks Simulation Markov processes approximation algorithms

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A multi-objective decomposition estimation of distribution algorithm for multilevel thresholding image segmentation

A multi-objective decomposition estimation of distribution a...

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Computational Intelligence in Image, Signal Processing and Synthetic Media Companion (CISM Companion), IEEE Symposium on

作者： Jorge Ramos-Frutos Oscar Ramos-Soto Angel Casas-Ordaz Diego Oliva Itzel Aranguren Saúl Zapotecas-Martínez Israel Miguel-Andrés Centro de Innovación Aplicada en Tecnologías Competitivas Guanajuato México Universidad de Guadalajara CUCEI Jalisco México Instituto Nacional de Astrofísica Optica y Electrónica Puebla México

ISBN: (数字)9798331508517

ISBN: (纸本)9798331508524

Melanoma is a skin cancer that can cause death in humans. Using tools such as digital image processing helps professionals diagnose it with greater reliability. Image segmentation by multilevel thresholding is used to extract homogeneous regions of an image using information from pixel intensities. This work presents a methodology based on multi-objective optimization for medical image segmentation. The between-class variance and the minimum cross-entropy are used as a criterion for the segmentation. The optimization is done using an Estimation of Distribution Algorithm (EDA) and compared with a Decomposition-based Multi-objective Evolutionary Algorithm EDA (MOEA/D-EDA). The thresholds obtained with the multi-objective algorithm are selected by approximating the Pareto Fronts that balance the two objectives. The results compared to a single optimization approach are better visually.

关键词： Image segmentation Thresholding (Imaging) Signal processing algorithms Estimation Evolutionary computation Melanoma Signal processing approximation algorithms Reliability Optimization

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Evasion Attacks on Tree-Based Machine Learning Models

Evasion Attacks on Tree-Based Machine Learning Models

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Carson Koball Yong Wang The Beacom College of Computer and Cyber Sciences Dakota State University Madison SD USA

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Various machine learning algorithms exist, each maintaining different assumptions about the underlying data or the function they aim to approximate. One such class of algorithms include tree-based models, which develop decision graphs that determine model behavior. In this research, we focus on developing two attacks that target tree-based models: one attack targets single tree-based models and the other targets ensemble tree-based models. We evaluate the two proposed attack algorithms on the popular MNIST handwritten digits dataset. Evading samples generated from the testing session are analyzed through the generated perturbation from each sample. We find that the evasion attacks are largely successful in the main objectives of evading: forcing incorrect model output and maintaining the stealthiness of the attack. Additionally, we find that the proposed attack algorithms perform well in the more stricter attack setting of targeted evasion.

关键词： Adaptation models Visualization Electric potential Machine learning algorithms Perturbation methods approximation algorithms Decision trees Random forests Testing Periodic structures

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Improved Sliding Mode Control for Airborne Electro-Optical Stabilized Platforms Based on Disturbance Observer

Improved Sliding Mode Control for Airborne Electro-Optical S...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Mengchen Yan Xiangwei Mou Rijun Wang Juan Hu College of Electronic and Information Engineering/Integrated Circuits Guangxi Normal University GXNU Guilin China Teachers College for Vocational and Technical Education Guangxi Normal University GXNU Guilin China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

To address low control accuracy, long adjustment times, and disturbances such as external interference and internal parameter variations, this paper proposes an Improved Non-Singular Fast Terminal Sliding Mode Control (I-NFTSMC) algorithm based on a disturbance observer. The algorithm estimates and compensates for unknown disturbances in real-time and introduces a time-varying switching gain to reduce chattering from high-frequency switching. Simulation results show that the I-NFTSMC algorithm improves response speed by approximately 42% and reduces rise time by about 44% under disturbance conditions compared to traditional methods. This approach achieves overshoot-free and accurate tracking of input commands, significantly enhancing the stability and disturbance rejection capabilities of airborne electro-optical platforms.

关键词： Accuracy Simulation Switches Electro-optic effects approximation algorithms Disturbance observers Stability analysis Robustness Real-time systems Sliding mode control

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Cost-Aware and Distance-Constrained Collective Spatial Keyword Query (Extended Abstract)

Cost-Aware and Distance-Constrained Collective Spatial Keywo...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Chan, Harry Kai-Ho Liu, Shengxin Long, Cheng Wong, Raymond Chi-Wing Roskilde Univ Roskilde Denmark Harbin Inst Technol Shenzhen Shenzhen Peoples R China Nanyang Technol Univ Singapore Singapore Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geotextual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). Many types of spatial keyword queries have been proposed on geo-textual data. Among them, one prominent type is to find, for a query consisting of a query location and some query keywords, a set of multiple objects such that the objects in the set collectively cover all the query keywords, and the object set is of good quality according to some criterion. Existing studies define the criterion either based on the geospatial information of the objects solely, or simply treat the geospatial information and the attribute information of the objects together without differentiation though they may have different semantics and scales. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of two algorithms including an exact algorithm and an approximation algorithm with provable guarantees for the problem. We conducted extensive experiments on both real and synthetic datasets, which verified the efficiency and effectiveness of proposed algorithms.

关键词： Costs Conferences Semantics approximation algorithms Data engineering Geospatial analysis

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