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Annual Symposium on Reliability and Maintainability (RAMS)

作者： Jiliang Zhang Rivian Automotive Palo Alto CA USA

ISBN: (数字)9798350367744

ISBN: (纸本)9798350367751

With one multi-variable acceleration model family, which is commonly used in real world practice, the distributional stress condition and distributional usage are converted to single-valued equivalent stress condition and single-valued equivalent usage, respectively. The derived single-valued equivalent stress condition is exact solution without approximation. The algorithm with approximation for single-valued equivalent stress condition in more general situations is also discussed. The equivalent usage is derived independently from the stress. These results significantly simplify the reliability analysis of the products subject to the random loading conditions with multiple stress types. The applications in reliability validation test and reliability prediction are discussed.

关键词： Loading Random access memory approximation algorithms Reliability Stress Load modeling

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Circuits in a Box: Computing High-Dimensional Performance Spaces for Analog Integrated Circuits

Circuits in a Box: Computing High-Dimensional Performance Sp...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Benedikt Ohse Jürgen Kampe Christopher Schneider Ernst-Abbe-Hochschule Jena University of Applied Sciences Jena Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Performance spaces contain information about all combinations of attainable performance parameters of analog integrated circuits. Their exploration allows designers to evaluate given circuits without considering implementation details, making them a valuable tool to support the design process. The computation of performance spaces-even for a small number of considered parameters-is time-consuming because it requires solving multi-objective, non-convex optimization problems that involve costly circuit simulations. We present a numerical method for efficiently approximating high-dimensional performance spaces, which is based on the box-coverage method known from Pareto optimization. The resulting implementation not only outperforms state-of-the-art solvers based on the well-known Normal-Boundary Intersection method in terms of computational complexity, but also offers several advantages, such as a practical stopping criterion and the possibility of warm starting. Furthermore, we present an interactive visualization technique to explore performance spaces of any dimension, which can help system designers to make reliable topology decisions even without detailed technical knowledge of the underlying circuits. Numerical experiments that confirm the efficiency of our approach are performed by computing seven-dimensional performance spaces for an analog low-dropout regulator as used in the radio-frequency identification domain.

关键词： Visualization Regulators approximation algorithms SPICE Reliability engineering Topology Space exploration approximation methods Optimization Analog integrated circuits

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Multicast Optimization Transmission Method in Satellite-terrestrial Network Under Non-ideal CSI

Multicast Optimization Transmission Method in Satellite-terr...

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Information and Communication Engineering (JCICE), International Joint Conference on

作者： Hanrui Guo Zhiqi Dai Yulin Shi Yifeng Jin Zhen Xu School of Electronic and Information Engineering Beihang University Beijing China Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Beijing China

ISBN: (数字)9798350388725

ISBN: (纸本)9798350388732

The precoding technology of multibeam satellite communication systems preprocesses transmit signals by using Channel State Information (CSI) and transforms it into the signal suitable for channel transmission. This reduces InterBeam Interference (IBI) and enhances system performance. Because of long-time delay between the satellite and the Earth and fast time-varying channel phase, obtaining ideal CSI at the transmitter of satellite is difficult, which greatly influences the performance of the precoding algorithm. In this paper, we present a robust precoding optimization algorithm based on expectation building upon phase uncertainty. We use the throughput satisfaction calculated by the average achievable multicast rate as the design metric to optimize the capability of the system under non-ideal CSI and reduce the difficulty of solving the problem by approximating the closed-form expression. And simulation experiments verify the robustness of the proposed algorithm.

关键词： Multicast algorithms Satellites Uncertainty Closed-form solutions Transmitters Precoding Transforms approximation algorithms Throughput Optimization

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Dynamic Semi-Passive RIS-Assisted URLLC Communication with Finite Blocklength

Dynamic Semi-Passive RIS-Assisted URLLC Communication with F...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Xiaoqing Wang Xiongwen Zhao Fei Du Wenhui Zhu Miaozhi Huang Suiyan Geng School of Electrical and Electronic Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

Distinct from the conventional paradigms of fully passive and fully active RIS configurations, a novel semi-passive RIS composed of partially active reflecting elements and passive elements which can leverage combined advantages is proposed in this paper, Specifically, we investigate a semi-passive RIS assisted URLLC system from the energy efficient maximization perspective. In addition, efficient alternating optimization, successive convex approximation, and successive refinement algorithms are developed to jointly optimizing the transmit precoding matrices and dynamic RIS beamforming matrix. Numerical analysis shows that the proposed RIS system significantly outperforms traditional RIS and non-RIS systems. Moreover, semi-passive RIS with only few active elements can achieve superior energy efficiency compared to passive RIS even with infinite blocklength. This highlights the potential of semi-passive RIS to reduce latency, enhance reliability, and improve energy efficiency.

关键词： Array signal processing Heuristic algorithms Precoding Ultra reliable low latency communication approximation algorithms Energy efficiency Finite element analysis Resource management Reliability Optimization

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Multi-objective Flexible flow shop scheduling with limited buffers based on model-driven differential evolution algorithm

Multi-objective Flexible flow shop scheduling with limited b...

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2025 2nd International Conference on algorithms, Software Engineering and Network Security (ASENS)

作者： Lixin Liang Zhifeng Hao School of Mathematics and Computer Science Shantou University Shantou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

The multi-objective flexible flow shop scheduling problem with limited buffers (MO-FFSS-LB) is widely encountered in modern manufacturing systems. However, the existence of limited buffers greatly increases the complexity of job scheduling and needs to consider job completion time, replacement costs of different recipes, and dynamic buffer balancing simultaneously. To address the challenge of MO-FFSS-LB, this paper proposes a model-driven differential evolution (MDE) algorithm. Firstly, it designs a multi-layer coding mechanism to represent the solution of the problem according to the characteristics of the scheduling problem and then introduces an initialization mechanism to improve the quality of the initial solution and the solving efficiency. Moreover, the proposed MDE utilizes a machine learning-based model trained on historical scheduling data to approximate fitness, enabling rapid population evolution. To further improve the convergence speed and solution quality, a neighborhood search-based local optimization strategy and dynamically adjusted crossover rates and mutation factors strategy are introduced, enhancing the algorithm’s adaptability to complex scheduling problems. Experimental results demonstrate that the MDE algorithm can generate high-quality solutions for the MO-FFSS-LB and outperforms existing traditional scheduling algorithms in several performance metrics. This research not only provides an effective optimization tool for solving complex scheduling problems with limited buffer constraints but also offers new insights and methods for theoretical research and practical applications in related fields.

关键词： Job shop scheduling Machine learning algorithms Scheduling algorithms Heuristic algorithms Software algorithms Dynamic scheduling approximation algorithms Encoding Data models Optimization

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Generalized Approximate Message-Passing for Compressed Sensing with Sublinear Sparsity

Generalized Approximate Message-Passing for Compressed Sensi...

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

作者： Keigo Takeuchi Dept. Electrical and Electronic Information Engineering Toyohashi University of Technology Toyohashi Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper proposes generalized approximate message passing (GAMP) for reconstruction of sparse signals from generalized linear measurements. The signal sparsity is assumed to grow sublinearly in the signal dimension, in contrast to conventional linear sparsity. State evolution is utilized to design GAMP for signals with sublinear sparsity. When the support of nonzero signals does not include a neighborhood of zero, the so-called all-or-nothing phenomenon occurs for Bayesian GAMP: Bayesian GAMP achieves asymptotically exact signal reconstruction if and only if the prefactor in the sample complexity scaling is larger than a threshold. Numerical simulations show that Bayesian GAMP outperforms existing algorithms for the reconstruction of signals with sublinear sparsity in the linear measurement and 1-bit compressed sensing.

关键词： Message passing Signal processing algorithms Numerical simulation approximation algorithms Signal reconstruction Bayes methods Sensors Complexity theory Speech processing Compressed sensing

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Exploring the Limitations of Structured Orthogonal Dictionary Learning

arXiv

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arXiv 2025年

作者： Dash, Anirudh Siripuram, Aditya Department of Electrical Engineering Indian Institute of Technology Hyderabad Pakistan

This work is motivated by recent applications of structured dictionary learning, in particular when the dictionary is assumed to be the product of a few Householder atoms. We investigate the following two problems: 1) How do we approximate an orthogonal matrix V with a product of a specified number of Householder matrices, and 2) How many samples are required to learn a structured (Householder) dictionary from data? For 1) we discuss an algorithm that decomposes V as a product of a specified number of Householder matrices. We see that the algorithm outputs the decomposition when it exists, and give bounds on the approximation error of the algorithm when such a decomposition does not exist. For 2) given data Y = HX, we show that when assuming a binary coefficient matrix X, the structured (Householder) dictionary learning problem can be solved with just 2 samples (columns) in Y. © 2025, CC BY.

关键词： approximation algorithms

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Proportionally Fair Matching via Randomized Rounding 39

Proportionally Fair Matching via Randomized Rounding

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Duppala, Sharmila Grammel, Nathaniel Luque, Juan MacRury, Calum Srinivasan, Aravind Department of Computer Science University of Maryland College Park United States Graduate School of Business Columbia University United States

ISBN: (纸本)157735897X

Given an edge-colored graph, the goal of the proportional fair matching problem is to find a maximum weight matching while ensuring proportional representation (with respect to the number of edges) of each color. The colors may correspond to demographic groups or other protected traits where we seek to ensure roughly equal representation from each group. It is known that, assuming ETH, it is impossible to approximate the problem with colors in time 2o()nO(1) (i.e., subexponential in ) even on unweighted path graphs. Further, even determining the existence of a non-empty matching satisfying proportionality is NP-Hard. To overcome this hardness, we relax the stringent proportional fairness constraints to a probabilistic notion. We introduce a notion we call δ-PROBABLYFAIR, where we ensure proportionality up to a factor of at most (1 ± δ) for some small δ > 0 with high probability. The violation δ can be brought arbitrarily close to 0 for some good instances with large values of matching size. We propose and analyze simple and fast algorithms for bipartite graphs that achieve constant-factor approximation guarantees, and return a δ-PROBABLYFAIR matching. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： approximation algorithms

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Approximate Tree Completion and Learning-Augmented algorithms for Metric Minimum Spanning Trees

arXiv

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arXiv 2025年

作者： Veldt, Nate Stanley, Thomas Priest, Benjamin W. Steil, Trevor Iwabuchi, Keita Jayram, T.S. Sanders, Geoffrey Department of Computer Science and Engineering Texas A&M University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Finding a minimum spanning tree (MST) for n points in an arbitrary metric space is a fundamental primitive for hierarchical clustering and many other ML tasks, but this takes Ω(n2) time to even approximate. We introduce a framework for metric MSTs that first (1) finds a forest of disconnected components using practical heuristics, and then (2) finds a small weight set of edges to connect disjoint components of the forest into a spanning tree. We prove that optimally solving the second step still takes Ω(n2) time, but we provide a subquadratic 2.62-approximation algorithm. In the spirit of learning-augmented algorithms, we then show that if the forest found in step (1) overlaps with an optimal MST, we can approximate the original MST problem in subquadratic time, where the approximation factor depends on a measure of overlap. In practice, we find nearly optimal spanning trees for a wide range of metrics, while being orders of magnitude faster than exact algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： approximation algorithms

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Fast Structured Orthogonal Dictionary Learning using Householder Reflections

Fast Structured Orthogonal Dictionary Learning using Househo...

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

作者： Anirudh Dash Aditya Siripuram Department of Electrical Engineering Indian Institute of Technology Hyderabad

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose and investigate algorithms for the structured orthogonal dictionary learning problem. First, we investigate the case when the dictionary is a Householder matrix. We give sample complexity results and show theoretically guaranteed approximate recovery (in the l ∞ sense) with optimal computational complexity. We then attempt to generalize these techniques when the dictionary is a product of a few Householder matrices. We numerically validate these techniques in the sample-limited setting to show performance similar to or better than existing techniques while having much improved computational complexity.

关键词： Dictionaries Signal processing algorithms Machine learning Signal processing approximation algorithms Reflection Acoustics Computational complexity Speech processing

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