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IEEE Conference on Wireless Communications and Networking

作者： Qi Shi Yuze Jin Wei Wang Chenhui Gu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou P.R. China National (Hangzhou) Novel Internet Exchange Hangzhou P.R. China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The prevalence of computational-intensive tasks stimulate the emergence of Computing Power Networks (CPN), where Software Defined Network (SDN) is utilized for flexible control. Considering the coexistence of in-execution tasks in the network and the arrival of emergency tasks, pre-migration is essential for reserving computing resources in advance, while it results in extra transmission cost in data plane and control cost in control plane. In this paper, we propose an analytical framework of task pre-migration policy in the SDN-based CPN to reduce system cost while meeting the delay budget. Specially, we consider the delay in SDN control plane induced by pre-migration flows, which involves $\ell_approximation-\mathbf{norm}$ and causes the problem non-convex and discontinuous. To overcome the challenges, we propose an iterative approximation algorithm to handle $\ell_approximation-\mathbf{norm}$ constraints, which transforms the original problem into a continuous function. Then we propose a dynamic programming based algorithm to minimize the approximation error, aiming at delay guarantee. The superiority of the proposed algorithm is verified through simulations, especially for heavy task offloading.

关键词： Energy consumption Costs Heuristic algorithms Software algorithms Transforms approximation algorithms approximation error Iterative algorithms Delays Software defined networking

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Approximate GCD by relaxed NewtonSLRA algorithm

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ACM COMMUNICATIONS IN COMPUTER ALGEBRA 2021年第3期55卷 97-101页

作者： Nagasaka, Kosaku Kobe Univ Kobe Hyogo Japan

We propose a better algorithm for approximate greatest common divisor (approximate GCD) of univariate polynomials in terms of robustness and distance, based on the NewtonSLRA algorithm that is a solver for the structured low rank approximation (SLRA) problem. Our algorithm mainly enlarges the tangent space in the NewtonSLRA algorithm and adapts it to a certain weighted Frobenius norm. Moreover, we propose some improvement in computing time.

关键词： approximation algorithms

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Exact and Approximate High-Multiplicity Scheduling on Identical Machines 14th

Exact and Approximate High-Multiplicity Scheduling on Iden...

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14th International Conference on algorithms and Complexity, CIAC 2025

作者： Jansen, Klaus Kahler, Kai Zwanger, Esther Department of Computer Science Kiel University Kiel Germany

ISBN: (纸本)9783031929311

Goemans and Rothvoss (SODA’14) gave a framework for solving problems which can be described as finding a point in ***(P∩ZN)∩Q, where P,Q⊂RN are (bounded) polyhedra. The running time for solving such a problem is ⟨P⟩2O(N)⟨Q⟩O(1). This framework can be used to solve various scheduling problems, but the encoding length ⟨P⟩ usually involves parameters like the makespan or deadlines (which can be very large compared to the processing times). We describe three tools to improve the framework by Goemans and Rothvoss:Problem-specific preprocessing can be used to greatly reduce ⟨P⟩.By solving a certain LP relaxation, one can obtain bounds for the points in P. Combined with the classical result by Frank and Tardos (J. Comb. ’87), these yield a more compact encoding of P in general.A result by Jansen and Klein (SODA’17) changes the running time of the algorithm by Goemans and Rothvoss to |V|2O(N)⟨P⟩O(1)⟨Q⟩O(1), where V is the set of vertices of the convex hull of P∩ZN. We provide a new bound for |V| that is similar to the one by Berndt et al. (SOSA’21) but better for our setting;this gives an alternative way to improve the framework. Problem-specific preprocessing can be used to greatly reduce ⟨P⟩. By solving a certain LP relaxation, one can obtain bounds for the points in P. Combined with the classical result by Frank and Tardos (J. Comb. ’87), these yield a more compact encoding of P in general. A result by Jansen and Klein (SODA’17) changes the running time of the algorithm by Goemans and Rothvoss to |V|2O(N)⟨P⟩O(1)⟨Q⟩O(1), where V is the set of vertices of the convex hull of P∩ZN. We provide a new bound for |V| that is similar to the one by Berndt et al. (SOSA’21) but better for our setting;this gives an alternative way to improve the framework. For example, applied to the scheduling problems P||{Cmax,Cmin,Cenvy}, these tools improve the running time from (log(Cmax))2O(d)⟨I⟩O(1) to the possibly much better (log(pmax))2O(d)⟨I⟩O(1). Here, pmax is the largest processing t

关键词： approximation algorithms

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Preconditioning of LSQR and CGLS: Variants, Properties and Relations

Preconditioning of LSQR and CGLS: Variants, Properties and...

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European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2023

作者： Havelková, Eva Hnětynková, Iveta Department of Numerical Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783031861727

LSQR and CGLS represent well known Krylov subspace methods for the solution of linear approximation problems including ill-posed ones. In order to accelerate convergence or impose solution constraints, various preconditioning strategies have been proposed. Basic LSQR and CGLS are mathematically equivalent, but relations among their preconditioned variants have been studied only partially. In this paper, we do not restrict to particular applications, but we algebraically describe underlying subspaces, optimality conditions, and relate quantities generated by selected preconditioned variants. Furthermore, motivated by available right preconditioned CGLS avoiding transformation of coordinates, we give a transformation free right preconditioned LSQR algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： approximation algorithms

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SUM-OF-SQUARES TO APPROXIMATE KNAPSACK

arXiv

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arXiv 2025年

作者： Kothari, Pravesh K. Sarkar, Sherry Carnegie Mellon University United States

These notes give a self-contained exposition of Karlin, Mathieu and Nguyen’s [KMN11] tight estimate of the integrality gap of the sum-of-squares semidefinite program for solving the knapsack problem. They are based on a sequence of three lectures in CMU course on Advanced approximation algorithms in Fall’21 that used the KMN result to introduce the Sum-of-Squares method for algorithm design. The treatment in these notes uses the pseudo-distribution view of solutions to the sum-of-squares SDPs and only rely on a few basic, reusable results about pseudo-distributions. Copyright © 2025, The Authors. All rights reserved.

关键词： approximation algorithms

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Robust and Sample Optimal algorithms for PSD Low Rank approximation

Robust and Sample Optimal Algorithms for PSD Low Rank Approx...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Ainesh Bakshi Nadiia Chepurko David P. Woodruff CMU MIT

ISBN: (数字)9781728196213

ISBN: (纸本)9781728196220

Recently, Musco and Woodruff (FOCS, 2017) showed that given an n×n positive semidefinite (PSD) matrix A, it is possible to compute a (1+ε-approximate relative-error low-rank approximation to A by querying Õ(nk/ε 2.5 ) entries of A in time Õ(nk/ε 2.5 +nk ω-1 /ε 2(ω-1) ). They also showed that any relative-error low-rank approximation algorithm must query Ω(nk/ε) entries of A, this gap has since remained open. Our main result is to resolve this question by obtaining an optimal algorithm that queries Õ(nk/ε) entries of A and outputs a relative-error low-rank approximation in Õ(n·(k/ε) ω-1 ) time. Note, our running time improves that of Musco and Woodruff, and matches the information-theoretic lower bound if the matrix-multiplication exponent ω is 2. We then extend our techniques to negative-type distance matrices. Here, our input is a pair-wise distance matrix A corresponding to a point set P={x 1 , x 2 , ..., x n } such that A i, j =||x i -x j ||2 2 . Bakshi and Woodruff (NeurIPS, 2018) showed a bi-criteria, relative-error low-rank approximation for negative-type metrics. Their algorithm queries Õ(nk/ε 2.5 ) entries and outputs a rank-( k+4) matrix. We show that the bi-criteria guarantee is not necessary and obtain an Õ(nk/ε) query algorithm, which is optimal. Our algorithm applies to all distance matrices that arise from metrics satisfying negative-type inequalities, including l 1 ,l 2 , spherical metrics, hypermetrics and effective resistances on a graph. We also obtain faster algorithms for ridge regression. Next, we introduce a new robust low-rank approximation model which captures PSD matrices that have been corrupted with noise. We assume that the Frobenius norm of the corruption is bounded. Here, we relax the notion of approximation to additive-error, since it is information-theoretically impossible to obtain a relative-error approximation in this setting. While a sample complexity lower bound precludes sublinear algorithms for arbitrary PSD matrices, we provid

关键词： approximation algorithms Measurement Covariance matrices Complexity theory Correlation Matrix decomposition Computational modeling

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Development of Algorithm for Filtering the Parameters of Angular Orientation of the Object on Signals of GNSS

Development of Algorithm for Filtering the Parameters of Ang...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Stepan V. Orobchenko Kirill V. Kochka Artyom D. Evseev Alexander A. Chugunov Sergey V. Chernyh Alexander P. Malyshev Radio Systems Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This article is dedicated to the development of two-stage algorithm for filtering the parameters of angular orientation of the object on radio signals of satellite navigation system. For implement such an algorithm the algorithms of quasi-optimal filtering in the Gaussian approximation of the posterior probability density based on the Kalman filtering apparatus are applied. The object of this work is a two-stage algorithm for filtering the parameters of the angular orientation of the object on radio signals of satellite navigation systems. As a result of the study, a two-step algorithm for filtering the parameters of the object's angular orientation on radio signals of satellite navigation systems was developed and tested in simulation modeling. The practical significance of the developed algorithm for evaluating the orientation of the consumer on radio signals of satellite navigation systems is to ensure a standard deviation of the error of estimating the angles of orientation to units of angular minutes, at the intensity of the object's own dynamics, which is several times better than in similar algorithms used. Application: navigation of military and public segment customers.

关键词： Power engineering Filtering Heuristic algorithms Posterior probability Satellite broadcasting Radio navigation Satellite navigation systems approximation algorithms Kalman filters Standards

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Machine Learning-Driven Surrogate Modeling and Quantum Metaheuristics for Gurney Flap Optimization in Darrieus Wind Turbines

Machine Learning-Driven Surrogate Modeling and Quantum Metah...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Subhrajit Roy Siddharth Joshi Vishal Jaiswal Raj Kumar Singh Department of Mechanical Engineering Delhi Technological University Delhi India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The optimization of Darrieus wind turbine designs is critical to improving their aerodynamic performance and energy efficiency. The present study focuses to enhance the (C P ) of the turbine by optimizing the design parameters of Gurney flaps attached to the trailing edges of the turbine rotors. Specifically, the flap angle and height are treated as the design variables. To explore the design space and generate random design points efficiently, Latin Hypercube Sampling (LHS) is employed. URANS simulations are carried out on these design points to provide high-fidelity data. A RBF surrogate model is then trained on this data, and is integrated with the Quantum-based Salp Swarm Algorithm (QSSA). The findings indicate a significant improvement in C P , with the optimized Gurney flap configurations outperforming the smooth baseline case by approximately 158% and the initial Gurney flap setup by 188%. Further, the performance of QSSA is shown with another popular evolutionary algorithm— the Harris Hawks Algorithm. This approach demonstrates the potential of data-driven optimization techniques in the energy sector.

关键词： Accuracy Computational modeling Computational fluid dynamics Metaheuristics Rotors Hypercubes approximation algorithms Data models Wind turbines Space exploration

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Deep Learning Based Enhanced Approximate Message Passing for mmWave Massive MIMO Channel Estimation

Deep Learning Based Enhanced Approximate Message Passing for...

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International Conference on Signal Processing and Communication (ICSC)

作者： Anusaya Swain Athira K Shrishail M. Hiremath Sarat Kumar Patra Department of Electronics and Communication National Institute of Technology Rourkela Odisha

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This paper presents a new approach to channel estimation in millimeter-wave beamspace massive MIMO systems. The proposed method is an approximate message passing algorithm that utilizes a flexible discriminative denoiser. The denoiser consists of two parts: a noise level map identifier and a convolutional neural network. By learning the channel structure and estimating the noise characteristics, the denoiser enhances the performance of the message passing algorithm. Simulation results demonstrate that the proposed network outperforms networks using DnCNN denoisers and existing compressed sensing-based algorithms.

关键词： Deep learning Message passing Simulation Channel estimation Signal processing algorithms Massive MIMO approximation algorithms Millimeter wave communication Signal to noise ratio Antenna arrays

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Exploration of Crop Planting Strategies Based on Simulated Annealing Algorithm and VIF Tests

Exploration of Crop Planting Strategies Based on Simulated A...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Yuyan Liu Xinyi Li Yuzhou Chu Rui Jiang Yu Gan Xiangtan University Xiangtan China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

The aim of this paper is to explore the planting strategy based on simulated annealing algorithm and VIF test by analyzing the planting decision in order to achieve the efficient use of resources and sustainable development of green industry. The study first assumed the ratio of total planted crop production to expected sales in 2023, and constructed a multiobjective planning model using the planting of different types of plots from 2024 to 2030 as decision variables. The improved simulated annealing algorithm solves the model through the outer-point penalty function method and the centered log-ratio transformation to derive the expected returns under the two sales strategies and analyze the sensitivity and stability of the model. In addition, this paper establishes a stochastic planning model based on the consideration of potential risks, and applies the sampling approximate average method to solve the model to further optimize the planting strategy. The problem of multicollinearity among crops was verified through the VIF test, and ridge regression was utilized to improve the interpretability of the model. Ultimately, the results of the study showed an increase in expected returns under different cropping strategies and suggested feasible cropping improvements. This study provides a practical theoretical basis for agricultural decisionmaking, which is of great significance for improving agricultural productivity and promoting sustainable development.

关键词： Analytical models Adaptation models Sensitivity Crops Stochastic processes Simulated annealing approximation algorithms Stability analysis Planning Sustainable development

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