Suchergebnisse - \(D\)-function proximal minimization algorithm

Convergence rate analysis of proximal gradient methods with applications to composite minimization problems von Sahu, D. R., Yao, J. C., Verma, M., Shukla, K. K.

“… First-order methods such as proximal gradient, which use Forward-Backward Splitting techniques have proved to be very effective in solving nonsmooth convex minimization problem, which is useful …”
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Tensor Completion via Complementary Global, Local, and Nonlocal Priors von Zhao, Xi-Le, Yang, Jing-Hua, Ma, Tian-Hui, Jiang, Tai-Xiang, Ng, Michael K., Huang, Ting-Zhu

“… Completing missing entries in multidimensional visual data is a typical ill-posed problem that requires appropriate exploitation of prior information of the …”
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Proximal point algorithms based on S-iterative technique for nearly asymptotically quasi-nonexpansive mappings and applications von Sahu, D. R., Kumar, Ajeet, Kang, Shin Min

“… , 8 (1), 61–79 2007 ) with the proximal point algorithm introduced by Rockafellar ( SIAM J. Control Optim. , 14 , 877–898 1976 ) to propose a new modified proximal …”
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Inexact Proximal Operators for \ell-Quasinorm Minimization von O'Brien, Cian, Plumbley, Mark D.

“… Proximal methods are an important tool in signal processing applications, where many problems can be characterized by the minimization of an expression involving a smooth fitting term and a convex …”
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An implementable proximal point algorithmic framework for nuclear norm minimization von Liu, Yong-Jin, Sun, Defeng, Toh, Kim-Chuan

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A unified hybrid iterative method for hierarchical minimization problems von Sahu, D.R., Ansari, Q.H., Yao, J.C.

“… In this paper, we introduce and analyze a new unified hybrid iterative method to compute the approximate solution of the general optimization problem defined over the set D=Fix(T)∩Ω[GMEP(Φ,Ψ,φ)], where Fix …”
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Parseval Proximal Neural Networks von Hasannasab, Marzieh, Hertrich, Johannes, Neumayer, Sebastian, Plonka, Gerlind, Setzer, Simon, Steidl, Gabriele

“… Second, we use our findings to establish so-called proximal neural networks (PNNs) and stable tight frame proximal neural networks …”
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Energy Efficient Spectrum Allocation and Mode Selection for D2D Communications in Heterogeneous Networks von Galanopoulos, Apostolos, Foukalas, Fotis, Khattab, Tamer

“… This problem is solved using a state of the art optimization method known as proximal algorithm …”
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Convergence analysis of two-step inertial Douglas-Rachford algorithm and application von Dixit, Avinash, Sahu, D. R., Gautam, Pankaj, Som, T.

“… In this paper, we propose a novel two-step inertial Douglas-Rachford algorithm to solve the monotone inclusion problem of the sum of two maximally monotone operators based on the normal S-iteration method (Sahu, D.R …”
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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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Novel Proximal Group ADMM for Placement Considering Fogging and Proximity Effects von Chen, Jianli, Huang, Zhipeng, Zhu, Ziran, Peng, Zheng, Zhu, Wenxing, Chang, Yao-Wen

“… In this article, we propose an analytical placement algorithm that considers both FPEs. We formulate the global placement problem as a separable minimization problem …”
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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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Dynamic State Estimation for Large Scale Systems Based on a Parallel Proximal Algorithm von Molina-Machado, Cristhian D, Martinez-Vargas, Juan D, Giraldo, Eduardo

“… Since this task requires a high amount of computational resources, a novel solution is presented based on a minimization problem, including spatial and temporal constraints solved with a parallel proximal dual approach …”
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VAGA: a novel viscosity-based accelerated gradient algorithm: Convergence analysis and applications von Verma, Mridula, Sahu, D. R., Shukla, K. K.

“… Proximal Algorithms are known to be very popular in the area of signal processing, image reconstruction, variational inequality and convex optimization due to their small iteration costs …”
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Applications of accelerated computational methods for quasi-nonexpansive operators to optimization problems von Sahu, D. R.

“… This paper studies the convergence rates of two accelerated computational methods without assuming nonexpansivity of the underlying operators with convex and …”
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Column distribution reconstruction algorithm via the alternating direction method von Wang, Linyuan, Cai, Ailong, Liu, Hongkui, Zhang, Hanming, Yan, Bin, Li, Lei, Hu, Guoen

“… In this study, a column distributed reconstruction algorithm based on TV minimization and the alternating direction method (ADM) has been developed …”
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Best proximity point theorems in the frameworks of fairly and proximally complete spaces von Basha, Sadiq

“… x ∗ is as close to g x ∗ as possible. To be precise, if T is from A to B and g is from A to A , where A and B are subsets of a metric space, one is concerned with the computation of a global minimizer of the mapping x ⟶ d ( g x , T x …”
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URLLC Latency Minimization in Interweave CRNs Using Digital Twin and DRL Approach von Paul, Anal, Singh, Keshav, Li, Chih-Peng, Duong, Trung Q.

“… ) framework incorporating a modified proximal policy optimization …”
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Piecewise Rigid Scene Flow with Implicit Motion Segmentation von Gorlitz, Andreas, Geiping, Jonas, Kolb, Andreas

“… Yet, we also show that this energy can be efficiently minimized by a proximal primal-dual algorithm …”
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On the iteration-complexity of a non-Euclidean hybrid proximal extragradient framework and of a proximal ADMM von Gonçalves, Max L. N., Melo, Jefferson G., Monteiro, Renato D. C.

“… Pointwise and ergodic iteration-complexity results for the proximal alternating direction method of multipliers (ADMM …”
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