On sparse approximations of solutions to linear systems with orthogonal matrices by Andrei V. Kiptenko, Ilia M. Izbiakov

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PSYCHOACOUSTICALLY MOTIVATED TIME-FREQUENCY DICTIONARY BUILDING FOR UNIVERSAL SCALABLE AUDIOCODER BASED ON THE SPARSE APPROXIMATION by V. Y. Herasimovich, Al. A. Petrovsky

“…It describes the following algorithms: sparse approximation, perceptual adaptation of the wavelet packet decomposition tree, input signal encoding/decoding schemes. …”
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PARAMETRIC AUDIO CODER BASED ON SPARSE APPROXIMATION WITH FRAME-BASED PSYCHOACOUSTIC OPTIMIZED WAVELET PACKET DICTIONARY by Al. A. Petrovsky, V. Y. Herasimovich

“…Universal audiocoder scheme based on sparse approximation with frame-based psychoacoustic optimized wavelet packet dictionary proposed.…”
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Application of a Sparsity Pattern and Region Clustering for Near Field Sparse Approximate Inverse Preconditioners in Method of Moments Simulations by Carlos Delgado, Javier Moreno, Felipe Cátedra

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Optimization of Iterative Control Algorithms for High-Resolution Adaptive Optics Systems by Shenghu Liu, Wang Zhao, Shuai Wang, Kangjian Yang, Ping Yang, Hongli Guan, Han Guo, Ruifeng He

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Intelligent Reconfigurable surface technique for Multiple Antenna Communication System by Noor Qasim, Ismail Hburi, Haider Salih Al Ammar

Subjects: “…Keywords: digital to analog pre-coding, intelligent recon-figure able surface (IRS), minimum mean squared error pre-coder (MMSE), Matching pursuit (sparse approximation) method…”
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Faber-Schauder Wavelet Sparse Grid Approach for Option Pricing with Transactions Cost by Shu-Li Mei

“…Second, using the couple technique of the variational iteration method (VIM) and the precision integration method, the sparse approximation solution of the nonlinear partial differential equations can be obtained. …”
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gptools: Scalable Gaussian Process Inference with Stan by Till Hoffmann, Jukka-Pekka Onnela

“…We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph; second, a fast, exact method for regularly spaced data modeled by GPs with stationary kernels using the fast Fourier transform. …”
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Sparse fault representation based on moment tensor interpolation by Julien Thurin

“…We demonstrate how our approach can benefit both the inverse and forward problems on the January 2024 Noto earthquake, computing a sparse approximation of the USGS NEIC source model with fewer than ten key tensors and computing full wavefield and static deformation from upscaled source distributions in a realistic 3D regional tomographic model using spectral-elements method. …”
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Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets by Steinbrinker Tabea, Spicher Nicolai

“…The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. …”
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An improved algorithm of segmented orthogonal matching pursuit based on wireless sensor networks by Xinmiao Lu, Yanwen Su, Qiong Wu, Yuhan Wei, Jiaxu Wang

“…In the framework of the stagewise orthogonal matching pursuit algorithm, the algorithm in this article uses a combination of a fixed-value strategy and a threshold strategy to screen the candidate atom sets in two rounds to improve the accuracy of atom selection, and then according to the sparsity adaptive principle, the sparse approximation and accurate signal reconstruction are realized by the variable step size method. …”
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Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…To address this issue, researchers have developed various sparse approximation methods that significantly reduce the computational burden of GPs. …”
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Application of Hierarchical Two-Level Spectral Preconditioning Method for Electromagnetic Scattering from the Rough Surface by D. Z. Ding, G. M. Li, Y. Y. An, R. S. Chen

“…The hierarchical two-level spectral preconditioner is constructed by combining the spectral preconditioner and sparse approximate inverse (SAI) preconditioner to speed up the convergence rate of iterative methods. …”
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Constructing hidden differential equations using a data-driven approach with the alternating direction method of multipliers (ADMM) by Jye Ying Sia, Yong Kheng Goh, How Hui Liew, Yun Fah Chang

“…The proposed algorithm provides a way to discover sparse approximations of differential equations from data. …”
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