Suchergebnisse - Proximal majorization-minimization algorithm

A Proximal-Proximal Majorization-Minimization Algorithm for Nonconvex Rank Regression Problems von Tang, Peipei, Wang, Chengjing, Jiang, Bo

“… In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex rank regression problems …”
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Majorization-Minimization Bregman Proximal Gradient Algorithms for NMF with the Kullback–Leibler Divergence von Takahashi, Shota, Tanaka, Mirai, Ikeda, Shiro

“… In this paper, we propose new algorithms, called majorization-minimization Bregman proximal gradient algorithm (MMBPG …”
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An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal von Wang, Chengjing, Zhao, Xile, Wang, Qingsong, Ma, Zepei, Tang, Peipei

“… , the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization …”
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Majorization-Minimization Bregman Proximal Gradient Algorithms for NMF with the Kullback–Leibler Divergence: MMBPG for KL-NMF von Takahashi, Shota, Tanaka, Mirai, Ikeda, Shiro

“… In this paper, we propose new algorithms, called majorization-minimization Bregman proximal gradient algorithm (MMBPG …”
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A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems von Tang, Peipei, Wang, Chengjing, Jiang, Bo

“… In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm for nonconvex tuning-free robust regression problems …”
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An inexact proximal majorization-minimization Algorithm for remote sensing image stripe noise removal von Wang, Chengjing, Zhao, Xile, Wang, Qingsong, Ma, Zepei, Tang, Peipei

“… , the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization …”
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Cognitive waveform design with desired spectrum-autocorrelation properties von Lu, Qinghui, Cui, Guolong, Yu, Xianxiang, Chen, Shiqiang, Kuang, Hongyin, Kong, Lingjiang

“… •We propose a competitive majorization minimization proximal method of multipliers algorithm, which is efficiently implemented via the fast fourier transform and element-wise arithmetic …”
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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problems von Tang, Peipei, Wang, Chengjing, Sun, Defeng, Kim-Chuan Toh

“… In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM …”
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Majorization-minimization Bregman proximal gradient algorithms for nonnegative matrix factorization with the Kullback--Leibler divergence von Takahashi, Shota, Tanaka, Mirai, Ikeda, Shiro

“… In this paper, to solve NMF, we propose new algorithms, called majorization-minimization Bregman proximal gradient algorithm (MMBPG …”
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A proximal distance algorithm for likelihood-based sparse covariance estimation von Xu, Jason, Lange, Kenneth

“… These subproblems are generated and solved via the proximal distance version of the majorization-minimization principle …”
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Nonconvex truncated conditional value at risk-based sparse linear regression von Xie, Boyi, Wu, Zhongming, Li, Min

“… The solution equivalence between the proposed model and its approximation model is explored. To efficiently solve the approximation model, we develop a semismooth Newton-based proximal majorization-minimization algorithm …”
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Matrix completion with capped nuclear norm via majorized proximal minimization von Kuang, Shenfen, Chao, Hongyang, Li, Qia

“… ) approach, we develop a fast Majorized Proximal Minimization Impute (MPM-Impute) algorithm. At each iteration, the sub-problem is relaxed to a surrogate …”
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Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization von Yao, Yuxin, Deng, Bailin, Xu, Weiwei, Zhang, Juyong

“… xlink:href="zhang-ieq1-3247603.gif"/> </inline-formula> type robust norm to measure the alignment error and regularize the smoothness of deformation, and use a proximal algorithm to solve the resulting non-smooth optimization problem …”
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A Unified Alternating Direction Method of Multipliers by Majorization Minimization von Lu, Canyi, Feng, Jiashi, Yan, Shuicheng, Lin, Zhouchen

“… Inspired by the principle of majorization minimization, we respectively present the unified frameworks of Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating …”
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MM optimization: Proximal distance algorithms, path following, and trust regions von Landeros, Alfonso, Xu, Jason, Lange, Kenneth

“… We briefly review the majorization-minimization (MM) principle and elaborate on the closely related notion of proximal distance algorithms, a generic approach for solving constrained optimization problems via quadratic penalties …”
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Large Covariance Matrix Estimation With Oracle Statistical Rate via Majorization-Minimization von Wei, Quan, Zhao, Ziping

“… It is challenging to analyze the theoretical properties of the resulting estimator because popular iterative algorithms for convex optimization no longer have global convergence guarantees for non-convex …”
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Non-Coherent DOA Estimation via Proximal Gradient Based on a Dual-Array Structure von Wan, Zhengyu, Liu, Wei

“… It is then replaced by its convex surrogate alternative by applying the majorization-minimization technique and the proximal gradient method is employed to solve the surrogate problem …”
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Iterative Joint Image Demosaicking and Denoising Using a Residual Denoising Network von Kokkinos, Filippos, Lefkimmiatis, Stamatios

“… Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related …”
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The stochastic proximal distance algorithm von Jiang, Haoyu, Xu, Jason

“… In this work, we propose and analyze a stochastic version of the recently proposed proximal distance algorithm, a class of iterative optimization methods that recover a desired constrained estimation …”
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Proximal Distance Algorithms for Sparse Portfolio Selections von Zhao, Hong-Xin, Wang, Xin, Kong, Ling-Chen

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