Suchergebnisse - regularized projection method algorithm

A GPM-based algorithm for solving regularized Wasserstein barycenter problems in some spaces of probability measures von Kum, S., Duong, M.H., Lim, Y., Yun, S.

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A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction von Lin, Yizun, Schmidtlein, C. Ross, Li, Qia, Li, Si, Xu, Yuesheng

“… This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA …”
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LCD Motion Blur: Modeling, Analysis, and Algorithm von Chan, S. H., Nguyen, T. Q.

“… In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed …”
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2-D regularized locality preserving projection algorithms for temporospatial feature reduction and its application in industrial data regression von Zhou, Xiaojie, Wang, Dianhui, Shao, Zhugui

“… This paper proposes a two-dimensional regularized locality preserving projection (2DRLPP) algorithm for feature extraction, which combines locality preserving projection (LPP …”
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The row-oriented form of the regularized Kaczmarz’s method von Alexander I Zhdanov, Yuri V Sidorov

“… The basis of the method is the application the projection Kaczmarz algorithm to the augmented regularized normal system of equations …”
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Regularized projection pursuit for data with a small sample-to-variable ratio von Hou, Siyuan, Wentzell, Peter D.

“… technology and the application of multivariate data analysis methods. Exploratory data analysis, which has played a crucial role in this advance, aims to examine the natural data structure to reveal important information …”
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A Regularized Variable Projection Algorithm for Separable Nonlinear Least-Squares Problems von Chen, Guang-Yong, Gan, Min, Chen, C. L. Philip, Li, Han-Xiong

“… The variable projection (VP) method is a very powerful tool for solving such problems. In this paper, we consider the regularization of ill-conditioned SNLLS problems based on the VP method …”
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Regularized gradient-projection methods for equilibrium and constrained convex minimization problems von Tian, Ming, Huang, Li-Hua

“… In this article, based on Marino and Xu’s method, an iterative method which combines the regularized gradient-projection algorithm (RGPA …”
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Overrelaxed Sinkhorn–Knopp Algorithm for Regularized Optimal Transport von Thibault, Alexis, Chizat, Lénaïc, Dossal, Charles, Papadakis, Nicolas

“… It generalizes and improves upon the widely used iterative Bregman projections algorithm (or Sinkhorn–Knopp algorithm). We first proposed to rely on regularized nonlinear acceleration schemes …”
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Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator von Tian, Ming, Jiao, Si-Wen

“… In this paper, based on the viscosity approximation method and the regularized gradient-projection algorithm, we find a common element of the solution set of a constrained convex minimization problem …”
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Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? von Plenge, Esben, Poot, Dirk H. J., Bernsen, Monique, Kotek, Gyula, Houston, Gavin, Wielopolski, Piotr, van der Weerd, Louise, Niessen, Wiro J., Meijering, Erik

“… The methods are based on iterative back‐projection, algebraic reconstruction, and regularized least squares …”
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A general regularized gradient-projection method for solving equilibrium and constrained convex minimization problems von Tian, Ming, Jiao, Si-Wen

“… By using the idea of regularized gradient-projection algorithm (RGPA), we find a common element, which is also a solution of a variational inequality problem …”
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A Regularized Semi-Smooth Newton Method with Projection Steps for Composite Convex Programs von Xiao, Xiantao, Li, Yongfeng, Wen, Zaiwen, Zhang, Liwei

“… The goal of this paper is to study approaches to bridge the gap between first-order and second-order type methods for composite convex programs …”
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Solving Ultrasound Tomography's Inverse Problem: Automating Regularization Parameter Selection von Carevic, Anita, Slapnicar, Ivan, Almekkawy, Mohamed

“… Yet, to accomplish this, reconstruction algorithms must solve an inherent nonlinear, ill-posed inverse problem …”
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Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection von Sun, Weiwei, Du, Qian

“… A fast and robust principal component analysis on Laplacian graph (FRPCALG) method is proposed to select bands of hyperspectral imagery (HSI …”
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Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods von Attouch, Hedy, Bolte, Jérôme, Svaiter, Benar Fux

“… –backward splitting algorithm, the gradient projection and some proximal regularization of the Gauss–Seidel method in a nonconvex setting. Our results are illustrated through feasibility problems, or iterative thresholding procedures for compressive sensing …”
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Multimaterial topology optimization by volume constrained Allen–Cahn system and regularized projected steepest descent method von Tavakoli, Rouhollah

“… •Developing a computational algorithm to solve multiphase topology optimization problems with arbitrary number of phases …”
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An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems von Zhang, Huiling, Xu, Zi

“… In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA …”
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Projection-Based Regularized Dual Averaging for Stochastic Optimization von Ushio, Asahi, Yukawa, Masahiro

“… We propose a novel stochastic-optimization framework based on the regularized dual averaging (RDA) method …”
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Regularized gradient-projection methods for the constrained convex minimization problem and the zero points of maximal monotone operator: Doc 258 von Tian, Ming, Jiao, Si-wen

“… In this paper, based on the viscosity approximation method and the regularized gradient-projection algorithm, we find a common element of the solution set of a constrained convex minimization problem …”
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