检索结果-内蒙古大学图书馆

Distributed algorithm for Cooperative Joint Localization and Tracking Using Multiple-Input Multiple-Output Radars

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kitchen, Astrid Holm Filtenborg Brøndt, Mikkel Sebastian Lundsgaard Jensen, Marie Saugstrup Pedersen, Troels Westerkam, Anders Malthe Aalborg University Aalborg Denmark

—We propose a distributed joint localization and tracking algorithm using a message passing framework, for multiple-input multiple-output radars. We employ the mean field approach to derive an iterative algorithm. The obtained algorithm features a small communication overhead that scales linearly with the number of radars in the system. The proposed algorithm shows good estimation accuracy in two simulated scenarios even below 0 dB signal to noise ratio. In both cases the ground truth falls within the 95% confidence interval of the estimated posterior for the majority of the track. © 2025, CC BY.

关键词： consensus algorithm

THE LATENT VARIABLE PROXIMAL POINT algorithm FOR VARIATIONAL PROBLEMS WITH INEQUALITY CONSTRAINTS

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Dokken, Jørgen S. Farrell, Patrick E. Keith, Brendan Papadopoulos, Ioannis P.A. Surowiec, Thomas M. Department of Scientific Computing and Numerical Analysis Simula Research Laboratory Oslo Norway Mathematical Institute University of Oxford Oxford United Kingdom Mathematical Institute Faculty of Mathematics and Physics Charles University Prague Czech Republic Division of Applied Mathematics Brown University ProvidenceRI United States Weierstrass Institute Berlin Germany

The latent variable proximal point (LVPP) algorithm is a framework for solving infinite-dimensional variational problems with pointwise inequality constraints. The algorithm is a saddle point reformulation of the Bregman proximal point algorithm. At the continuous level, the two formulations are equivalent, but the saddle point formulation is more amenable to discretization because it introduces a structure-preserving transformation between a latent function space and the feasible set. Working in this latent space is much more convenient for enforcing inequality constraints than the feasible set, as discretizations can employ general linear combinations of suitable basis functions, and nonlinear solvers can involve general additive updates. LVPP yields numerical methods with observed mesh-independence for obstacle problems, contact, fracture, plasticity, and others besides;in many cases, for the first time. The framework also extends to more complex constraints, providing means to enforce convexity in the Monge–Ampère equation and handling quasi-variational inequalities, where the underlying constraint depends implicitly on the unknown solution. In this paper, we describe the LVPP algorithm in a general form and apply it to twelve problems from across mathematics. © 2025, CC BY.

关键词： consensus algorithm

Generalized exchange cluster algorithm to compute efficiently covariances and susceptibilities in Monte Carlo

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Assaraf, Roland Chevreau, Hilaire Sorbonne Université CNRS Paris France

We present a Monte Carlo method to compute efficiently susceptibilites or covariances of two physical variables. The method relies on a generalization of the exchange cluster algorithm to any model of interacting particles with any 2-body interactions. The principle is to select clusters of variables belonging to two independent replicas of the system. An improved estimator of the covariance of two physical variables (in one replica) is then proposed. This estimator has the zero-variance property in the limit where these variables are independent. In practice the scaling of the statistical fluctuations as a function of the number of degrees of freedom N is reduced from O(N2) to O(N). This lower scaling is illustrated on a Lennard Jones model. Copyright © 2025, The Authors. All rights reserved.

关键词： consensus algorithm

Secant Line Search for Frank-Wolfe algorithms

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Hendrych, Deborah Besançon, Mathieu Martínez-Rubio, David Pokutta, Sebastian Zuse Institute Berlin Berlin Germany Berlin Institute of Technology Berlin Germany Université Grenoble Alpes Inria Laboratoire d’Informatique de Grenoble Grenoble France Signal Theory and Communications Department Carlos III University of Madrid Madrid Spain

We present a new step-size strategy based on the secant method for Frank-Wolfe algorithms. This strategy, which requires mild assumptions about the function under consideration, can be applied to any Frank-Wolfe algorithm. It is as effective as full line search and, in particular, allows for adapting to the local smoothness of the function, such as in Pedregosa et al. (2020), but comes with a significantly reduced computational cost, leading to higher effective rates of convergence. We provide theoretical guarantees and demonstrate the effectiveness of the strategy through numerical experiments. © 2025, CC BY.

关键词： consensus algorithm

Decentralized Inference for Spatial Data Using Low-Rank Models

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Shi, Jianwei Abdulah, Sameh Sun, Ying Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. While offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This paper presents a decentralized framework tailored for parameter inference in spatial low-rank models to address these challenges. A key obstacle arises from the spatial dependence among observations, which prevents the log-likelihood from being expressed as a summation—a critical requirement for decentralized optimization approaches. To overcome this challenge, we propose a novel objective function leveraging the evidence lower bound, which facilitates the use of decentralized optimization techniques. Our approach employs a block descent method integrated with multi-consensus and dynamic consensus averaging for effective parameter optimization. We prove the convexity of the new objective function in the vicinity of the true parameters, ensuring the convergence of the proposed method. Additionally, we present the first theoretical results establishing the consistency and asymptotic normality of the estimator within the context of spatial low-rank models. Extensive simulations and real-world data experiments corroborate these theoretical findings, showcasing the robustness and scalability of the *** Codes 62M30 © 2025, CC BY.

关键词： consensus algorithm

Efficiently parallelizable kernel-based multi-scale algorithm

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lot, Federico Rieger, Christian Department of Mathematics and Computer Science Philipps-Universität Marburg Hans-Meerwein-Straße 6 Hessia Marburg35032 Germany

The kernel-based multi-scale method has been proven to be a powerful approximation method for scattered data approximation problems which is computationally superior to conventional kernel-based interpolation techniques. The multi-scale method is based of an hierarchy of point clouds and compactly supported radial basis functions, typically Wendland functions. There is a rich body of literature concerning the analysis of this method including error estimates. This article addresses the efficient parallelizable implementation of those methods. To this end, we present and analyse a monolithic approach to compute the kernel-based multi-scale approximation. Copyright © 2025, The Authors. All rights reserved.

关键词： consensus algorithm

New and Improved Bounds for Markov Paging

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Pabbaraju, Chirag Vakilian, Ali Stanford University United States Toyota Technological Institute Chicago United States

In the Markov paging model, one assumes that page requests are drawn from a Markov chain over the pages in memory, and the goal is to maintain a fast cache that suffers few page faults in expectation. While computing the optimal online algorithm (OPT) for this problem naively takes time exponential in the size of the cache, the best-known polynomial-time approximation algorithm is the dominating distribution algorithm due to Lund, Phillips and Reingold (FOCS 1994), who showed that the algorithm is 4-competitive against OPT. We substantially improve their analysis and show that the dominating distribution algorithm is in fact 2-competitive against OPT. We also show a lower bound of 1.5907-competitiveness for this algorithm—to the best of our knowledge, no such lower bound was previously known. © 2025, CC BY.

关键词： consensus algorithm

Multi-Stage Active Sequential Hypothesis Testing with Clustered Hypotheses

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Vershinin, George Cohen, Asaf Gurewitz, Omer The School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel

We consider the problem where an active Decision-Maker (DM) is tasked to identify the true hypothesis using as few as possible observations while maintaining accuracy. The DM collects observations according to its determined actions and knows the distributions under each hypothesis. We propose a deterministic and adaptive multi-stage hypothesis-elimination strategy where the DM selects an action, applies it repeatedly, and discards hypotheses in light of its obtained observations. The DM selects actions based on maximal separation expressed by the distance between the parameter vectors of each distribution under each hypothesis. Close distributions can be clustered, simplifying the search and significantly reducing the number of required observations. Our algorithms achieve vanishing Average Bayes Risk (ABR) as the error probability approaches zero, i.e., the algorithm is asymptotically optimal. Furthermore, we show that the ABR is bounded when the number of hypotheses grows. Simulations are carried out to evaluate the algorithm’s performance compared to another multi-stage hypothesis-elimination algorithm, where an improvement of several orders of magnitude in the mean number of observations required is observed. Copyright © 2025, The Authors. All rights reserved.

关键词： consensus algorithm

Solving Non-Monotone Inclusions Using Monotonicity of Pairs of Operators

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Le, Ba Khiet Dao, Minh N. Théra, Michel Optimization Research Group Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam School of Science RMIT University MelbourneVIC3000 Australia Mathematics and Computer Science Department University of Limoges 123 Avenue Albert Thomas Limoges87060 Cedex France

In this paper, under the monotonicity of pairs of operators, we propose some Generalized Proximal Point algorithms to solve non-monotone inclusions using warped resolvents and transformed resolvents. The weak, strong,... 详细信息

关键词： consensus algorithm