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Algorithm Unrolling for Massive Access via Deep Neural Networks With Theoretical Guarantee

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第2期21卷 945-959页

作者： Shi, Yandong Choi, Hayoung Shi, Yuanming Zhou, Yong ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Kyungpook Natl Univ Dept Math Daegu 41566 South Korea Yoke Intelligence Shanghai 201210 Peoples R China

Massive access is a critical design challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multiple-antenna base station (BS) and a large number of single-antenna IoT devices. Taking into account the sporadic nature of IoT devices, we formulate the joint activity detection and channel estimation (JADCE) problem as a group-sparse matrix estimation problem. This problem can be solved by applying the existing compressed sensing techniques, which however either suffer from high computational complexities or lack of algorithm robustness. To this end, we propose a novel algorithm unrolling framework based on the deep neural network to simultaneously achieve low computational complexity and high robustness for solving the JADCE problem. Specifically, we map the original iterative shrinkage thresholding algorithm (ISTA) into an unrolled recurrent neural network (RNN), thereby improving the convergence rate and computational efficiency through end-to-end training. Moreover, the proposed algorithm unrolling approach inherits the structure and domain knowledge of the ISTA, thereby maintaining the algorithm robustness, which can handle non-Gaussian preamble sequence matrix in massive access. With rigorous theoretical analysis, we further simplify the unrolled network structure by reducing the redundant training parameters. Furthermore, we prove that the simplified unrolled deep neural network structures enjoy a linear convergence rate. Extensive simulations based on various preamble signatures show that the proposed unrolled networks outperform the existing methods in terms of the convergence rate, robustness and estimation accuracy.

关键词： Deep learning Convergence Estimation Training Robustness Internet of Things approximation algorithms Massive access joint activity detection and channel estimation group-sparse matrix estimation algorithm unrolling deep learning

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High-Dimensional Geometric Streaming for Nearly Low Rank Data 41

High-Dimensional Geometric Streaming for Nearly Low Rank Dat...

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41st International Conference on Machine Learning, ICML 2024

作者： Esfandiari, Hossein Kacham, Praneeth Mirrokni, Vahab Woodruff, David P. Zhong, Peilin Google Research United States Computer Science Department Carnegie Mellon University Pittsburgh United States

We study streaming algorithms for the p subspace approximation problem. Given points a1, . . ., an as an insertion-only stream and a rank parameter k, the p subspace approximation problem is to find a k-dimensional subspace V such that (∑ni=1 d(ai, V )p)1/p is minimized, where d(a, V ) denotes the Euclidean distance between a and V defined as minv∈V ||a-v||2. When p = ∞, we need to find a subspace V that minimizes maxi d(ai, V ). For ∞ subspace approximation, we give a deterministic strong coreset construction algorithm and show that it can be used to compute a poly(k, log n) approximate solution. We show that the distortion obtained by our coreset is nearly tight for any sublinear space algorithm. For p subspace approximation, we show that suitably scaling the points and then using our ∞ coreset construction, we can compute a poly(k, log n) approximation. Our algorithms are easy to implement and run very fast on large datasets. We also use our strong coreset construction to improve the results in a recent work of Woodruff and Yasuda (FOCS 2022) which gives streaming algorithms for high-dimensional geometric problems such as width estimation, convex hull estimation, and volume estimation. Copyright 2024 by the author(s)

关键词： approximation algorithms

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Gerrymandering Planar Graphs 23

Gerrymandering Planar Graphs

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Dippel, Jack la Tour, Max Dupré Niu, April Roy, Sanjukta Vetta, Adrian McGill University Canada University of Leeds United Kingdom

We study the computational complexity of the map redistricting problem (gerrymandering). Mathematically, the electoral district designer (gerrymanderer) attempts to partition a weighted graph into k connected components (districts) such that its candidate (party) wins as many districts as possible. Prior work has principally concerned the special cases where the graph is a path or a tree. Our focus concerns the realistic case where the graph is planar. We prove that the gerrymandering problem is solvable in polynomial time in λ-outerplanar graphs, when the number of candidates and λ are constants and the vertex weights (voting weights) are polynomially bounded. In contrast, the problem is NP-complete in general planar graphs even with just two candidates. This motivates the study of approximation algorithms for gerrymandering planar graphs. However, when the number of candidates is large, we prove it is hard to distinguish between instances where the gerrymanderer cannot win a single district and instances where the gerrymanderer can win at least one district. This immediately implies that the redistricting problem is inapproximable in polynomial time in planar graphs, unless P=NP. This conclusion appears terminal for the design of good approximation algorithms - but it is not. The inapproximability bound can be circumvented as it only applies when the maximum number of districts the gerrymanderer can win is extremely small, say one. Indeed, for a fixed number of candidates, our main result is that there is a constant factor approximation algorithm for redistricting unweighted planar graphs, provided the optimal value is a large enough constant. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： approximation algorithms

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Improved approximations for Flexible Network Design 32

Improved Approximations for Flexible Network Design

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32nd Annual European Symposium on algorithms, ESA 2024

作者： Hyatt-Denesik, Dylan Jabal-Ameli, Afrouz Sanità, Laura Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computing Sciences Bocconi University Milan Italy

ISBN: (纸本)9783959773386

Flexible network design deals with building a network that guarantees some connectivity requirements between its vertices, even when some of its elements (like vertices or edges) fail. In particular, the set of edges (resp. vertices) of a given graph are here partitioned into safe and unsafe. The goal is to identify a minimum size subgraph that is 2-edge-connected (resp. 2-vertex-connected), and stay so whenever any of the unsafe elements gets removed. In this paper, we provide improved approximation algorithms for flexible network design problems, considering both edge-connectivity and vertex-connectivity, as well as connectivity values higher than 2. For the vertex-connectivity variant, in particular, our algorithm is the first with approximation factor strictly better than 2. © Dylan Hyatt-Denesik, Afrouz Jabal-Ameli, and Laura Sanità;licensed under Creative Commons License CC-BY 4.0.

关键词： approximation algorithms

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A 12/7-approximation algorithm for the discrete Bamboo Garden Trimming problem

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OPERATIONS RESEARCH LETTERS 2021年第5期49卷 645-649页

作者： van Ee, Martijn Netherlands Def Acad Fac Mil Sci Den Helder Netherlands

We study the discrete Bamboo Garden Trimming problem (BGT), where we are given n bamboos with different growth rates. At the end of each day, one can cut down one bamboo to height zero. The goal in BGT is to make a perpetual schedule of cuts such that the height of the tallest bamboo ever is minimized. Here, we improve the current best approximation guarantee by designing a 12/7 approximation algorithm. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Bamboo Garden Trimming Pinwheel scheduling approximation algorithms

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Senior Learning JAYA With Powell's Method and Incremental Population Strategy

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IEEE ACCESS 2022年 10卷 103765-103780页

作者： Yavuz, Gurcan Kutahya Dumlupinar Univ Comp Engn Dept TR-43100 Kutahya Turkey

JAYA algorithm is one a recently developed meta-heuristic algorithm that does not require algorithm-specific parameters. It is an algorithm based on the fact that the solutions always go towards the best when searching. This paper proposes a JAYA variant (JAYA-SIP) with three improvements to the Original JAYA algorithm. It has incorporated the senior learning strategy, the incremental population strategy, and Powell's local search method into JAYA. The improvements were tested with IEEE Congress on Evolutionary Computation (CEC) benchmark set for 30 and 50 dimensions, and the benchmark functions set from a special issue of the Soft Computing journal (SOCO) for 500 and 1000 dimensions. In addition to benchmark sets, the performance of JAYA-SIP was evaluated with nine CEC 2011 real-world test functions. The results of the proposed algorithm are compared with JAYA variants and some meta-heuristic algorithms. According to the results of the experiment and the analysis, the proposed improvements increased the performance of the JAYA algorithm. JAYA-SIP achieved better results than the other algorithms it was compared with.

关键词： Metaheuristics Statistics Social factors Clustering algorithms Benchmark testing Learning systems approximation algorithms Benchmark suite JAYA Powell's method optimization

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A Novel Localization Approach for Irregular Wireless Sensor Networks Based on Anchor Segmentation

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IEEE SENSORS JOURNAL 2022年第7期22卷 7267-7276页

作者： Wang, Jing Cheng, Li Tu, Yuanfei Gu, Shenkai Nanjing Tech Univ Dept Elect Engn & Control Sci Nanjing 211816 Peoples R China Nanjing Tech Univ Dept Comp Sci & Technol Nanjing 211816 Peoples R China

Source localization has been a crucial fundamental service in wireless sensor networks (WSNs). Existing algorithms assume a regular region or controlled deployment. In practice, however, irregular network topologies often occurs, which greatly downgrade the localization performance. In this paper, we propose a new distributed localization approach based on Anchor Segmentation and Projection for Irregular networks (ASPI). The new framework is composed of three phases: anchor segmentation boarder construction, convex hull identification and projection-based localization. An anchor based network approximate convex segmentation method is proposed to reduce the consumption of network resources in the first two phases and an improved giftwrapping based convex hull identification method is provided to reduce the complexity. In the localization phase, we formulate the localization as a convex feasibility problem to avoid the multimodality in Maximum likelihood techniques and an alternative procedure is provided for inconsistent situation in the projection-based scheme. Experiments are conducted and the results demonstrate that our algorithm outperforms other existing solutions in irregular-shaped networks in higher accuracy with low complexity.

关键词： Location awareness Sensors approximation algorithms Network topology Wireless sensor networks Complexity theory Partitioning algorithms Wireless sensor networks convex segmentation convex feasibility problem

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F2-Bubbles: Faithful Bubble Set Construction and Flexible Editing

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 422-432页

作者： Wang, Yunhai Cheng, Da Wang, Zhirui Zhang, Jian Zhou, Liang He, Gaoqi Deussen, Oliver Shandong Univ Jinan Peoples R China Chinese Acad Sci CNIC Beijing Peoples R China Peking Univ Natl Inst Hlth Data Sci Beijing Peoples R China East China Normal Univ Shanghai Peoples R China Konstanz Univ Constance Germany

In this paper, we propose F2-Bubbles, a set overlay visualization technique that addresses overlapping artifacts and supports interactive editing with intelligent suggestions. The core of our method is a new, efficient set overlay construction algorithm that approximates the optimal set overlay by considering set elements and their non-set neighbors. Thanks to the efficiency of the algorithm, interactive editing is achieved, and with intelligent suggestions, users can easily and flexibly edit visualizations through direct manipulations with local adaptations. A quantitative comparison with state-of-the-art set visualization techniques and case studies demonstrate the effectiveness of our method and suggests that F2-Bubbles is a helpful technique for set visualization.

关键词： Visualization Data visualization Task analysis Clutter approximation algorithms Tuning Micromechanical devices Set visualization Edge Crossing Minimal Spanning Tree

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Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7863-7876页

作者： Bing, Zhenshan Brucker, Matthias Morin, Fabrice O. Li, Rui Su, Xiaojie Huang, Kai Knoll, Alois Tech Univ Munich Dept Informat D-85748 Munich Germany Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China

Reinforcement learning algorithms, such as hindsight experience replay (HER) and hindsight goal generation (HGG), have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HER achieves its training success through hindsight replays of past experience with heuristic goals but underperforms in challenging tasks in which goals are difficult to explore. HGG enhances HER by selecting intermediate goals that are easy to achieve in the short term and promising to lead to target goals in the long term. This guided exploration makes HGG applicable to tasks in which target goals are far away from the object's initial position. However, the vanilla HGG is not applicable to manipulation tasks with obstacles because the Euclidean metric used for HGG is not an accurate distance metric in such an environment. Although, with the guidance of a handcrafted distance grid, grid-based HGG can solve manipulation tasks with obstacles, a more feasible method that can solve such tasks automatically is still in demand. In this article, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment. We evaluated G-HGG on four challenging manipulation tasks with obstacles, where significant enhancements in both sample efficiency and overall success rate are shown over HGG and HER. Videos can be viewed at https://***/ghgg.

关键词： Task analysis Robots Euclidean distance Training Extraterrestrial measurements Reinforcement learning approximation algorithms Hindsight experience replay (HER) path planning reinforcement learning (RL) robotic arm manipulation

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An Evaluation-Focused Framework for Visualization Recommendation algorithms

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 346-356页

作者： Zeng, Zehua Moh, Phoebe Du, Fan Hoffswell, Jane Lee, Tak Yeon Malik, Sana Koh, Eunyee Battle, Leilani Univ Maryland College Pk MD 20742 USA Adobe Res San Jose CA USA Korea Adv Inst Sci & Technol Seoul South Korea Univ Washington Seattle WA 98195 USA

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.

关键词： Data visualization Visualization Machine learning algorithms approximation algorithms Task analysis Encoding Clustering algorithms Visualization Tools Visualization Recommendation algorithms

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