Results 21 to 30 of about 58,601 (260)
Improved iteratively reweighted least squares algorithms for sparse recovery problem
IET Image Processing, 2022 In this paper, some new algorithms based on the iteratively reweighted least squares (IRLS) method are proposed for sparse recovery problem. There are two important parameters in the IRLS method: a weighted parameter and a regularization parameter.
Yufeng Liu, Zhibin Zhu, Benxin Zhang
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Sensor Alignment for Ballistic Trajectory Estimation via Sparse Regularization
Information, 2018 Sensor alignment plays a key role in the accurate estimation of the ballistic trajectory. A sparse regularization-based sensor alignment method coupled with the selection of a regularization parameter is proposed in this paper.
Dong Li, Lei Gong
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Parameter Choice Strategies for Multipenalty Regularization [PDF]
SIAM Journal on Numerical Analysis, 2014 The widespread applicability of the multipenalty regularization is limited by the fact that theoretically optimal rate of reconstruction for a given problem can be realized by a one-parameter counterpart, provided that relevant information on the problem is available and taken into account in the regularization.
Massimo Fornasier +2 more
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Zeitschrift für Medizinische Physik, 2023 Purpose: Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions.
Efe Ilicak +2 more
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l1-Regularization in Portfolio Selection with Machine Learning
Mathematics, 2022 In this work, we investigate the application of Deep Learning in Portfolio selection in a Markowitz mean-variance framework. We refer to a l1 regularized multi-period model; the choice of the l1 norm aims at producing sparse solutions. A crucial issue is
Stefania Corsaro +3 more
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Parameter selection for HOTV regularization [PDF]
Applied Numerical Mathematics, 2018 Popular methods for finding regularized solutions to inverse problems include sparsity promoting $\ell_1$ regularization techniques, one in particular which is the well known total variation (TV) regularization. More recently, several higher order (HO) methods similar to TV have been proposed, which we generally refer to as HOTV methods. In this letter,
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Earth and Space Science, 2020 Tikhonov regularization is critical for accurately specifying both the background (B) and observational (R) error covariances in four‐dimensional variational data assimilation (4DVar). The ratio of the background and observation error variances (referred
Xiangjun Tian, Rui Han, Hongqin Zhang
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Loop quantum gravity and cosmological constant
Physics Letters B, 2021 A one-parameter regularization freedom of the Hamiltonian constraint for loop quantum gravity is analyzed. The corresponding spatially flat, homogenous and isotropic model includes the two well-known models of loop quantum cosmology as special cases. The
Xiangdong Zhang, Gaoping Long, Yongge Ma
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Fractal and Fractional, 2021 In this paper, we study an inverse problem to identify the initial value problem of the homogeneous Rayleigh–Stokes equation for a generalized second-grade fluid with the Riemann–Liouville fractional derivative model.
Dun-Gang Li +3 more
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A New Method for Determining Optimal Regularization Parameter in Near-Field Acoustic Holography
Shock and Vibration, 2018 Tikhonov regularization method is effective in stabilizing reconstruction process of the near-field acoustic holography (NAH) based on the equivalent source method (ESM), and the selection of the optimal regularization parameter is a key problem that ...
Yue Xiao
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