Smoothing and Regularization with Modified Sparse Approximate Inverses by T. Huckle, M. Sedlacek

“…Sparse approximate inverses 𝑀 which satisfy min𝑀‖𝐴𝑀−𝐼‖𝐹 have shown to be an attractive alternative to classical smoothers like Jacobi or Gauss-Seidel (Tang and Wan; 2000). …”
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Sparse Approximation for Nonrigid Structure from Motion by Yaming Wang, Xiaomeng Yan, Junbao Zheng, Mingfeng Jiang

“…This paper introduces applying a novel sparse approximation method into solving nonrigid structure from motion problem in trajectory space. …”
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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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Online Coregularization for Multiview Semisupervised Learning by Boliang Sun, Guohui Li, Li Jia, Kuihua Huang

“…For practical purpose, we also propose two sparse approximation approaches for kernel representation to reduce the computational complexity. …”
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Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes by Yaqiu Zhang, Lizhi Liu, Xinnian Yang

“…Variational inference is employed to obtain a sparse approximation of the posterior distribution, modeling the spectral field within the latent function space. …”
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On the Relation between the AINV and the FAPINV Algorithms by Davod Khojasteh Salkuyeh, Hadi Roohani

“…Both of these algorithms compute a sparse approximate inverse of matrix 𝐴 in the factored form and are based on computing two sets of vectors which are 𝐴-biconjugate. …”
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THE VARIATIONAL NONLINEAR CHIRP MODE DECOMPOSITION BASED ON CONVEX OPTIMIZATION FOR FAULT DIAGNOSIS by MA YuBo, LV Yong, YI CanCan

“…The problem in the processing of mechanical fault vibration signal by variational nonlinear chirp mode decomposition（VNCMD）,the noise leads to time-frequency surface blurring,which reduces the accuracy of time-frequency ridges extracted,and then affects the decomposition effect of VNCMD,is aimed at,so a joint fault diagnosis of convex optimization and VNCMD is proposed.The noise can be eliminated by solving the sparse approximate solution of signal via the convex optimization algorithm,which can improve the readability of the time-frequency surface,so as to obtain accurate timefrequency ridges.Then,by using these ridges,the fault features of the signal can be extracted effectively via VNCMD.Through the analysis of simulated signal and the measured bearing outer ring fault data,the results demonstrate that the proposed method can realize the accurate extraction of rolling bearing fault feature.…”
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Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning by Luca Bergamaschi, Angeles Martínez, Giorgio Pini

“…Namely, we consider the Deflation-Accelerated Conjugate Gradient (DACG) algorithm accelerated by factorized-sparse-approximate-inverse- (FSAI-) type preconditioners. …”
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Hierarchical Matrices Method and Its Application in Electromagnetic Integral Equations by Han Guo, Jun Hu, Hanru Shao, Zaiping Nie

“…In this paper, a novel sparse approximate inverse (SAI) preconditioner in multilevel fashion is proposed to accelerate the convergence rate of Krylov iterations for solving H-matrices system in electromagnetic applications, and a group of parallel fast direct solvers are developed for dealing with multiple right-hand-side cases. …”
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