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Spark Rough Hypercuboid Approach for Scalable Feature Selection

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 3130-3144页

作者： Luo, Chuan Wang, Sizhao Li, Tianrui Chen, Hongmei Lv, Jiancheng Yi, Zhang Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Sichuan Peoples R China

Feature selection refers to choose an optimal non-redundant feature subset with minimal degradation of learning performance and maximal avoidance of data overfitting. The appearance of large data explosion leads to the sequential execution of algorithms are extremely time-consuming, which necessitates the scalable parallelization of algorithms by efficiently exploiting the distributed computational capabilities. In this paper, we present parallel feature selection algorithms underpinned by a rough hypercuboid approach in order to scale for the growing data volumes. Metrics in terms of rough hypercuboid are highly suitable to parallel distributed processing, and fits well with the Apache Spark cluster computing paradigm. Two data parallelism strategies, namely, vertical partitioning and horizontal partitioning, are implemented respectively to decompose the data into concurrent iterative computing streams. Experimental results on representative datasets show that our algorithms significantly faster than its original sequential counterpart while guaranteeing the quality of the results. Furthermore, the proposed algorithms are perfectly capable of exploiting the distributed-memory clusters to accomplish the computation task that fails on a single node due to the memory constraints. Parallel scalability and extensibility analysis have confirmed that our parallelization extends well to process massive amount of data and can scales well with the increase of computational nodes.

关键词： Feature extraction Rough sets Partitioning algorithms Computational modeling approximation algorithms Parallel processing Heuristic algorithms Rough hypercuboid feature selection parallel computing scalability apache spark

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Fast Low Rank Column-Wise Compressive Sensing for Accelerated Dynamic MRI

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 409-424页

作者： Babu, Silpa Lingala, Sajan Goud Vaswani, Namrata Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Univ Iowa Dept Biomed Engn Iowa City IA 52242 USA

This work develops a novel set of algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI1 and altGDmin-MRI2), for accelerated dynamic MRI by assuming an approximate low-rank (LR) model on the matrix formed by the vectorized images of the sequence. The LR model itself is well-known in the MRI literature;our contribution is the novel GD-based algorithms which are much faster, memory-efficient, and 'general' compared with existing work;and careful use of a 3-level hierarchical LR model. By 'general,' we mean that, with a single choice of parameters, our method provides accurate reconstructions for multiple accelerated dynamic MRI applications, multiple sampling rates and sampling schemes. We show that our methods outperform many of the popular existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively undersampled multi-coil dynamic MRI applications, sampled using either 1D Cartesian or 2D pseudo-radial undersampling, at multiple sampling rates. Evaluations on some prospectively undersampled datasets are also provided. Our second contribution is a mini-batch subspace tracking extension that can process new measurements and return reconstructions within a short delay after they arrive. The recovery algorithm itself is also faster than its batch counterpart.

关键词： Magnetic resonance imaging Image reconstruction Heuristic algorithms Computational modeling Compressed sensing approximation algorithms Minimization low-rank MRI

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Improved approximation Algorithm for Capacitated Facility Location with Uniform Facility Cost 34

Improved Approximation Algorithm for Capacitated Facility Lo...

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34th International Symposium on algorithms and Computation, ISAAC 2023

作者： Kao, Mong-Jen Department of Computer Science National Yang-Ming Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9783959772891

We consider the hard-capacitated facility location problem with uniform facility cost (CFL-UFC). This problem arises as an indicator variation between the general CFL problem and the uncapacitated facility location (UFL) problem, and is related to the profound capacitated k-median problem (CKM). In this work, we present a rounding-based 4-approximation algorithm for this problem, built on a two-staged rounding scheme that incorporates a set of novel ideas and also techniques developed in the past for both facility location and capacitated covering problems. Our result improves the decades-old LP-based ratio of 5 for this problem due to Levi et al. since 2004. We believe that the techniques developed in this work are of independent interests and may further lead to insights and implications for related problems. © Mong-Jen Kao;licensed under Creative Commons License CC-BY 4.0.

关键词： approximation algorithms

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A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization With MGARCH and Small Transaction Costs

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IEEE ACCESS 2023年 11卷 16774-16792页

作者： Papanicolaou, Andrew Fu, Hao Krishnamurthy, Prashanth Khorrami, Farshad North Carolina State Univ Dept Math Raleigh NC 27695 USA NYU Tandon Sch Engn Elect & Comp Engn Brooklyn NY USA

We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) with a small penalty on trading. A numerical solution is obtained using a neural network (NN) architecture within a recursive RL loop. A fixed-point theorem proves that NN approximation error has a big-oh bound that we can reduce by increasing the number of NN parameters. The functional form of the trading penalty has a parameter epsilon > 0 that controls the magnitude of transaction costs. When epsilon is small, we can implement an NN algorithm based on the expansion of the solution in powers of epsilon. This expansion has a base term equal to a myopic solution with an explicit form, and a first-order correction term that we compute in the RL loop. Our expansion-based algorithm is stable, allows for fast computation, and outputs a solution that shows positive testing performance.

关键词： Artificial neural networks Costs approximation algorithms Optimization Portfolios Reinforcement learning Covariance matrices Deep learning Neural networks Hetereoskedasticity MGARCH fixed-point algorithms reinforcement learning deep neural networks

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New Variable Precision Reduction Algorithm for Decision Tables

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IEEE ACCESS 2023年 11卷 42701-42712页

作者： Li, Xu Xiao, Hongping Tang, Jianguo Chongqing Coll Elect Engn Coll Artificial Intelligence & Big Data Chongqing 401331 Peoples R China Chongqing Univ Technol Coll Appl Sci & Technol Chongqing 400054 Peoples R China

Variable precision reduction (VPR) and positive region reduction (PRR) are common definitions in attribute reduction. The compacted decision table is an extension of a decision table. In this paper, we propose another extension, called the weighted decision table. In both types of decision tables, VPR is defined, and the corresponding discernibility matrices for the PRR are proposed. Then, algorithms for obtaining the PRR from the discernibility matrices are presented. In both types of decision tables, the relationship between VPR and PRR is established by comparing the corresponding discernibility matrices. If the precision of the VPR meets the given conditions, then the PRR algorithms can be used to obtain the results after modifying some decision values in the decision tables. An analysis of the modification process of the decision values and the compression process of decision tables is used to propose a new algorithm for VPR in decision tables that ensures credibility. The effectiveness of the proposed algorithm was evaluated by an experimental comparison with existing VPR algorithms.

关键词： Rough sets Optimization approximation algorithms Uncertainty Time complexity Text categorization Decision making Variable precision reduction positive region reduction decision table compacted decision table weighted decision table

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Performance Analysis of Wavenumber Domain algorithms for Highly Squinted SAR

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 1563-1575页

作者： Chen, Xing Hou, Zhenyu Dong, Zhen He, Zhihua Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Peoples R China

Wavenumber domain algorithms have unique advantages in processing highly squinted synthetic aperture radar data. This article studies the performance of three commonly used wavenumber domain algorithms including the classical wavenumber domain (CWD) algorithm, extended wavenumber domain (EWD) algorithm, and squint wavenumber domain (SWD) algorithm. First, the wavenumber domain signal expression under the zero-Doppler and acquisition-Doppler reference geometries are both derived. Second, the internal relationship between three wavenumber domain algorithms is analyzed. A new interpretation of the relationship between the three algorithms and an interpolation strategy are given. The analysis not only provides a deeper understanding of the three algorithms, but also provides a basis for comparing them. Then, the performance of the three wavenumber domain algorithms is evaluated from the perspectives of computational complexity, image quality, and geometric position through theoretical analysis and simulation experiments. Aiming at the problem that the range and azimuth profiles are not orthogonal, a method to calculate resolution and extract profile is proposed. The results show that all three algorithms can obtain a well-focused images if full-resolution interpolation is performed, and the computational complexities of CWD and SWD are less than that of EWD.

关键词： Synthetic aperture radar Azimuth Geometry Doppler effect approximation algorithms Interpolation Radar imaging Highly squinted synthetic aperture radar (SAR) stolt mapping wavenumber domain algorithm

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BPA: approximation Batch-Processing Algorithm for Static Lightpath Requests in Elastic Optical Networks

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 189-193页

作者： Chatterjee, Bijoy Chand Basavaprabhu, S. Oki, Eiji South Asian University Department of Computer Science New Delhi110021 India Kyoto University Graduate School of Informatics Kyoto6068501 Japan

One of the major obstacles we face in the elastic optical network (EON) is the fragmentation which degrades the quality of service by underutilizing the spectrum resources. Typically, proactive fragmentation management approaches have been considered to overcome the above issue. In proactive fragmentation management approaches, batch formation is a major concern for performance improvement. This letter proposes an approximation static Batch Processing Algorithm for lightpath requests in EONs, named BPA, to enhance resource utilization. BPA generates temporary batches of the lightpath requests by accommodating as many lightpath requests as possible until the batch capacity is exhausted. After that, the last request from each temporary batch is replaced with the same number of hops of requests that require more slots to form batches. This reduces the number of batches by utilizing the slots left in each batch. We prove that BPA is a factor 2WL approximation, where L is the ratio of the maximum number of hops to the minimum number of hops of requests, and W is the batch capacity. Numerical results indicate that BPA outperforms the conventional heuristic algorithm as the number of requests increases. © 2019 IEEE.

关键词： approximation algorithms

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Server Placement for Edge Computing: A Robust Submodular Maximization Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第6期22卷 3634-3649页

作者： Qu, Yuben Wang, Lihao Dai, Haipeng Wang, Weijun Dong, Chao Wu, Fan Guo, Song Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 210016 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing 210093 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

In this work, we study the problem of Robust Server Placement (RSP) for edge computing, i.e., in the presence of uncertain edge server failures, how to determine a server placement strategy to maximize the expected overall workload that can be served by edge servers. We mathematically formulate the RSP problem in the form of robust max-min optimization, derived from two consequentially equivalent transformations of the problem that does not consider robustness and followed by a robust conversion. RSP is challenging to solve, because the explicit expression of the objective function in RSP is hard to obtain, and it is a robust max-min problem with knapsack constraints, which is still an unexplored problem in the literature. We reveal that the objective function is monotone submodular, and propose two solutions to RSP. First, after proving that the involved constraints form a $p$p-independence system constraint, where $p$p is a parameter determined by the coefficients in the knapsack constraints, we propose an algorithm that achieves a provable approximation ratio in polynomial time. Second, we prove that one of the knapsack constraints is a matroid contraint, and propose another polynomial time algorithm with a better approximation ratio. Furthermore, we discuss the applicability of the aforementioned algorithms to the case with an additional server number constraint. Both synthetic and trace-driven simulation results show that, given any maximum number of server failures, our proposed algorithms outperform four state-of-the-art algorithms and approaches the optimal solution, which applies exhaustive exponential searches, while the proposed latter algorithm brings extra performance gains compared with the former one.

关键词： Servers approximation algorithms Optimization Delays Costs Mobile computing Robustness Edge computing networks server placement robust submodular optimization

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A Simple Check Polytope Projection Penalized Algorithm for ADMM Decoding of LDPC Codes

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IEEE ACCESS 2023年 11卷 2524-2530页

作者： Liu, Huiyang He, Pingquan Jiang, Yun Zhang, Qinglin Xia, Qiaoqiao Cent China Normal Univ Coll Phys Sci & Technol Wuhan 430079 Peoples R China

ADMM penalized decoding method for Low-Density Parity-Check (LDPC) Codes can improve the frame error rate (FER) performance than the standard ADMM decoder by adding penalty terms to the objective function. However, penalty parameter optimization of the penalty terms is very difficult and extremely time-consuming. Additionally, the Euclidean projection onto the check polytope is the most complex part of ADMM-based decoding algorithms. In this paper, by dynamically choosing even-vertex in the check polytope closest to the input vector as the approximate projection, a simple and novel check polytope projection penalized algorithm for ADMM decoding is proposed which can avoid the tedious work of penalty parameter optimization and simplify the check polytope projection. The simulation results show that the proposed algorithm can substantially reduce the projection time while achieving better FER performance when compared with the existing penalized decoding. In particular, the proposed algorithm can save the decoding time by 23% to 41% compared with ADMM-PD-LSA algorithm.

关键词： Maximum likelihood decoding approximation algorithms Convex functions Projection algorithms Optimization Heuristic algorithms Mathematical models Alternating direction method of multipliers (ADMM) low-density parity-check (LDPC) codes penalty terms check polytope projection

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A Novel Line approximation Approach for State Updates of Grid Maps 16th

A Novel Line Approximation Approach for State Updates of G...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Niu, Jie Zhang, Dong Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9789819964826

Straight-line approximation algorithms are often used in state update processes of grid maps. They are primarily designed for drawing and displaying. Due to limitations of per-step’s single selection and sensors’ noise, historical obstacles may not be eliminated clearly on grid maps. To ensure the accuracy of robots’ environment modeling, we propose a cross-discrimination algorithm (CDA) for line approximation on grid maps. It introduces an optional extension to the single-selection strategy by considering the intersection coordinates of lines and grids. The incomplete removal problem is effectively addressed by incorporating these additional coordinate pairs for clearing, resulting in improved quality for downstream tasks. A series of experiments are conducted to assess the algorithm’s universality and robustness. The results of the experiments confirm the effectiveness of our algorithm in grid map processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： approximation algorithms

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