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greedy Heuristics for Automatic Synthesis of Efficient Block-Structured Scheduling Processes from Declarative Specifications 14th

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14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Badica, Amelia Badica, Costin Danciulescu, Daniela Logofatu, Doina Univ Craiova Craiova Romania Univ Appl Sci Frankfurt Germany

ISBN: (纸本)9783319920078;9783319920061

This paper introduces a new greedy heuristic algorithm for the automatic synthesis of block-structured scheduling processes that satisfy a given set of declarative ordering constraints, as well as basic theoretical results that support the correctness of this algorithm. We propose two heuristics that can be used with this algorithm: hierarchical decomposition heuristic and critical path heuristic. We also present initial experimental results supporting the effectiveness and efficiency of our proposed algorithm and heuristics.

关键词： greedy algorithm Process model Ordering constraints Optimization

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Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix

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Frontiers of Information Technology & Electronic Engineering 2018年第4期19卷 503-512页

作者： Rasha SHOITAN Zaki NOSSAIR I.I.IBRAHIM Ahmed TOBAL Computer and Systems Department Electronic Research Institute Electronics Communicationsand Computer DepartmentHelwan University

Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing *** reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy ***,SAMP still suffers from relatively poor reconstruction quality especially at high compression *** the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP ***,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP *** simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing *** results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP *** addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative ***,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.

关键词： Block compressive sensing Sparsity adaptive matching pursuit greedy algorithm Wilkinson matrix

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Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第3期26卷 550-563页

作者： Zhang, Jie Chepuri, Sundeep Prabhakar Hendriks, Richard Christian Heusdens, Richard Delft Univ Technol Fac Elect Engn Math & Comp Sci NL-2628 Delft Netherlands

In large-scale wireless acoustic sensor networks (WASNs), many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response (MVDR) beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power (or signal-to-noise ratio). Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.

关键词： Sensor selection MVDR noise reduction sparsity convex optimization transmission power greedy algorithm

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Sparse radial basis function approximation with spatially variable shape parameters

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APPLIED MATHEMATICS AND COMPUTATION 2018年 330卷 170-184页

作者： Stolbunov, Valentin Nair, Prasanth B. Univ Toronto Inst Aerosp Studies Toronto ON M3H 5T6 Canada

We present an efficient greedy algorithm for constructing sparse radial basis function (RBF) approximations with spatially variable shape parameters. The central idea is to incrementally construct a sparse approximation by greedily selecting a subset of basis functions from a parameterized dictionary consisting of RBFs centered at all of the training points. An incremental thin QR update scheme based on the Gram-Schmidt process with reorthogonalization is employed to efficiently update the weights of the sparse RBF approximation at each iteration. In addition, the shape parameter of the basis function chosen at each iteration is tuned by minimizing the l(2)-norm of the training residual, while an approximate leave-one-out error metric is used as the dominant stopping criterion. Numerical studies are presented for a range of test functions to demonstrate that the proposed algorithm enables the efficient construction of RBF approximations with spatially variable shape parameters. It is shown that, compared to a classical RBF model with a single tunable shape parameter and Gaussian process models with an anisotropic Gaussian covariance function, the proposed algorithm can provide significant improvements in accuracy, cost, and sparsity, particularly for high-dimensional datasets. (c) 2018 Elsevier Inc. All rights reserved.

关键词： Function approximation Parameterized dictionary learning Radial basis functions greedy algorithm Shape parameter tuning Surrogate modeling

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GNAS: A greedy Neural Architecture Search Method for Multi-Attribute Learning 18

GNAS: A Greedy Neural Architecture Search Method for Multi-A...

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26th ACM Multimedia Conference (MM)

作者： Huang, Siyu Li, Xi Cheng, Zhi-Qi Zhang, Zhongfei Hauptmann, Alexander Zhejiang Univ Hangzhou Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450356657

A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures. Typically, the conventional deep multi-attribute learning approaches follow the pipeline of manually designing the network architectures based on task-specific expertise prior knowledge and careful network tunings, leading to the inflexibility for various complicated scenarios in practice. Motivated by addressing this problem, we propose an efficient greedy neural architecture search approach (GNAS) to automatically discover the optimal tree-like deep architecture for multi-attribute learning. In a greedy manner, GNAS divides the optimization of global architecture into the optimizations of individual connections step by step. By iteratively updating the local architectures, the global tree-like architecture gets converged where the bottom layers are shared across relevant attributes and the branches in top layers more encode attribute-specific features. Experiments on three benchmark multi-attribute datasets show the effectiveness and compactness of neural architectures derived by GNAS, and also demonstrate the efficiency of GNAS in searching neural architectures.

关键词： multi-task learning multi-attribute analysis neural architecture search greedy algorithm

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Efficient Hardware/Software Partitioning Based on a Hybrid algorithm

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IEEE ACCESS 2018年 6卷 60736-60744页

作者： Zhang, Tao Zhao, Xin Li, Xuan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Texas Instruments DSP Joint Lab Tianjin 300072 Peoples R China

Complex embedded systems with multi-processing units are important platforms for running complex tasks. In the development of complex embedded systems, hardware/software partitioning plays an important role. In practical application, there are many dynamic tasks which require the hardware/software partitioning to be done in real time. It is necessary to design efficient algorithms to do this. In this paper, the shuffled frog leaping algorithm (SFLA) and the greedy algorithm (GRA) are used to generate a hybrid algorithm named SFLA-GRA. On the basis of the SFLA, the SFLA-GRA uses the greedy idea to terminate invalid iterations and adjust the search step size. By these greedy strategies, the algorithm can be effectively accelerated. Experimental results show that compared with the other swarm intelligence (SI) algorithms, the efficiency of the proposed algorithm has been improved.

关键词： Efficient hardware/software partitioning shuffled frog leaping algorithm greedy algorithm hybrid algorithm

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Wireless sensor network minimum beacon set selection algorithm based on tree model

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NEURAL COMPUTING & APPLICATIONS 2018年第3期30卷 965-976页

作者： Wu, Bin Luo, Jian Yang, Chaoyu Xiamen Univ Dept Automat Xiamen 361005 Peoples R China Anhui Univ Sci & Technol Sch Econ & Management Huainan 232001 Peoples R China

Wireless sensor networks (WSNs) are highly attractive both in academia and in practice as a wholly new platform for information transmission. Localization technology is a key technology of WSNs. The structure of the beacon node set is very important to the positioning of the nodes. A method for constructing a minimum beacon set is proposed in this thesis based on the tree model, in which unimportant nodes are identified as early as possible and then pruned. Thus, we avoid unnecessary calculations when establishing the minimum beacon set. This method can provide a reliable guarantee for the unknown node localization. According to our experiment, this algorithm is rapid and stable.

关键词： WSNs Beacon set Localization technology greedy algorithm Partition-Cal-based algorithm

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Interference-Free LED Allocation for Visible Light Communications With Fisheye Lens

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2018年第3期36卷 626-636页

作者： Jiang, Rui Wang, Zhaocheng Zhu, Xudong Wang, Qi Tsinghua Univ Dept Elect Engn Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Due to the limitedmodulation bandwidth of commercial light emitting diodes (LEDs), imaging optical multiple-input multiple-output (MIMO) technology is applied in visible light communication (VLC) system to achieve high data rate. Since a receiver with a wide angle/field-of-view is preferred in the imaging optical MIMO VLC system, the fisheye lens can be utilized to concentrate the lights from the LEDs. To eliminate the inter-user interference and satisfy their target bit error rate (BER) requirements with the minimum number of LEDs, an interference-free LED allocation scheme is investigated in this paper, which is formulated as a combinatorial problem. The cost criterion of the combinatorial problem is defined as the number of the LEDs used to serve all users, and its discrete alternatives (i.e., feasible solutions) are the disjoint sets consisting of the LEDs that can be cooperatively utilized to satisfy the BER requirements for all users. For each LED in the disjoint set, its neighboring LEDs projected onto the same pixel are forbidden to serve different users. Moreover, due to the NP-hardness of the formulated problem, a location-based greedy algorithm is proposed, where the LEDs are allocated to the users sequentially based on their distances to the center of the LED array. Simulation results verify the effectiveness of our proposed algorithm and show that there exists no interference among all users while the target BER requirements for all users are satisfied with the proposed algorithm.

关键词： Fisheye lens greedy algorithm inter-user interference optical MIMO visible light communication

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Influence maximization in social networks under Deterministic Linear Threshold Model

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KNOWLEDGE-BASED SYSTEMS 2018年第Dec.1期161卷 111-123页

作者： Gursoy, Furkan Gunnec, Dilek Bogazici Univ Dept Management Informat Syst TR-34342 Istanbul Turkey Ozyegin Univ Dept Ind Engn TR-34794 Istanbul Turkey

We define the new Targeted and Budgeted Influence Maximization under Deterministic Linear Threshold Model problem and develop the novel and scalable TArgeted and BUdgeted Potential greedy (TABU-PG) algorithm which allows for optional methods to solve this problem. It is an iterative and greedy algorithm that relies on investing in potential future gains when choosing seed nodes. We suggest new real-world mimicking techniques for generating influence weights, thresholds, profits, and costs. Extensive computational experiments on four real network (Epinions, Academia, Pokec and Inploid) show that our proposed heuristics perform significantly better than benchmarks. We equip TABU-PG with novel scalability methods which reduce runtime by limiting the seed node candidate pool, or by selecting more nodes at once, trading-off with spread performance.

关键词： Influence Maximization Social networks Diffusion models Targeted marketing greedy algorithm

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A Simple and Effective Approach for Transmit Antenna Selection in Multiuser Massive MIMO Leveraging Submodularity

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第18期66卷 4869-4883页

作者： Konar, Aritra Sidiropoulos, Nicholas D. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22903 USA

Massive MIMO systems are expected to enable great improvements in spectral and energy efficiency. Realizing these benefits in practice, however, is hindered by the cost and complexity of implementing large-scale antenna systems. A potential solution is to use transmit antenna selection for reducing the number of radio-frequency (RF) chains at the base station. In this paper, we consider the NP-hard discrete optimization problem of performing transmit antenna selection in the downlink of a single cell, multiuser massive MIMO system by maximizing the downlink sum-rate capacity with fixed user power allocation subject to various RF switching constraints. Whereas prior work has focused on using convex relaxation based schemes, which lack theoretical performance guarantees and can be computationally demanding, we adopt a very different approach. We establish that the objective function of this antenna selection problem is monotone and satisfies an important property known as submodularity, while the RF switching constraints are expressible as the independent sets of a matroid. This implies that a simple greedy algorithm can be used to guarantee a constant-factor approximation for all problem instances. Simulations indicate that greedy selection yields a near-optimal solution in practice and captures a significant fraction of the total downlink channel capacity at substantially lower complexity relative to convex relaxation based approaches, even with very few RF chains. This paves the way for substantial reduction in hardware complexity of massive MIMO systems while using very simple algorithms.

关键词： Multi-user massive MIMO transmit antenna selection sum-rate capacity discrete optimization submodularity matroids greedy algorithm

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