Suchergebnisse - Fast proximal objective function optimization

Efficient 3D inversion of potential field data using fast proximal objective function optimization algorithm von Peng, Guomin, Liu, Zhan, Xu, Kaijun, Bai, Yongliang, Du, Runlin

“… We have developed an efficient 3D potential field data inversion method using fast proximal objective function (FPOF …”
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A fast proximal gradient algorithm for decentralized composite optimization over directed networks von Zeng, Jinshan, He, Tao, Wang, Mingwen

“… This paper proposes a fast decentralized algorithm for solving a consensus optimization problem defined in a directed networked multi-agent system, where the local objective functions have the smooth …”
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Scalable Proximal Jacobian Iteration Method With Global Convergence Analysis for Nonconvex Unconstrained Composite Optimizations von Zhang, Hengmin, Qian, Jianjun, Gao, Junbin, Yang, Jian, Xu, Chunyan

“… ) for solving linearly constrained problems with separable objectives and the proximal gradient methods (PGMs …”
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An accelerated proximal gradient method for multiobjective optimization von Tanabe, Hiroki, Fukuda, Ellen H., Yamashita, Nobuo

“… This paper presents an accelerated proximal gradient method for multiobjective optimization, in which each objective function is the sum of a continuously differentiable, convex function and a closed …”
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Stochastic Proximal Gradient Consensus Over Random Networks von Hong, Mingyi, Chang, Tsung-Hui

“… Each agent has access to some local objective function, and it only has unbiased estimates of the gradients of the smooth component …”
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Smoothing Accelerated Proximal Gradient Method with Fast Convergence Rate for Nonsmooth Convex Optimization Beyond Differentiability von Wu, Fan, Bian, Wei

“… We propose a smoothing accelerated proximal gradient (SAPG) method with fast convergence rate for finding a minimizer of a decomposable nonsmooth convex function over a closed convex set …”
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A fast computational Gauss–Seidel type iPALM algorithm using an incremental aggregated gradient strategy for weakly convex composite optimization problems with application in image processing von Jia, Zehui, Hou, Junru, Dong, Ping, Liu, Zhiyu

“… ) for solving a class of nonconvex and nonsmooth composite optimization problems, whose objective function is the sum of a finite number of smooth nonconvex functions and nonsmooth weakly convex functions …”
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A fast continuous time approach for non-smooth convex optimization using Tikhonov regularization technique von Karapetyants, Mikhail A.

“… In this paper we would like to address the classical optimization problem of minimizing a proper, convex and lower semicontinuous function via the second order in time dynamics, combining viscous …”
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A fast dual proximal gradient algorithm for convex minimization and applications von Beck, Amir, Teboulle, Marc

“… We present a dual-based proximal gradient scheme for solving this problem. We show that although the rate of convergence of the dual objective function sequence converges to the optimal value with the rate O(1/k2 …”
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A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems von Ge, Zhili, Zhang, Xin, Wu, Zhongming

“… In this paper, we propose a fast proximal iteratively reweighted nuclear norm algorithm with extrapolation for solving a class of nonconvex low-rank matrix minimization problems …”
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Fast Proximal Gradient Methods for Nonsmooth Convex Optimization for Tomographic Image Reconstruction von Helou, Elias S., Zibetti, Marcelo V. W., Herman, Gabor T.

“… The Fast Proximal Gradient Method (FPGM) and the Monotone FPGM (MFPGM) for minimization of nonsmooth convex functions are introduced and applied to tomographic image reconstruction …”
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A fast continuous time approach with time scaling for nonsmooth convex optimization von Boţ, Radu Ioan, Karapetyants, Mikhail A.

“… From here, we derive fast convergence rates for the objective function along a path which is the image of the trajectory of the system through the proximal operator of the first. Moreover, we prove the weak convergence of the trajectory of the system to a global minimizer of the objective function. Finally, we provide multiple numerical examples illustrating the theoretical results …”
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A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients von Wang, Guanjin, Kwok, Stephen Wai Hang, Yousufuddin, Mohammed, Sohel, Ferdous

“… The model also should have fewer tuning hyperparameters to ensure good usability and exhibit potential for fast incremental learning …”
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Proximal gradient methods with inexact oracle of degree q for composite optimization: Proximal gradient methods with inexact oracle von Nabou, Yassine, Glineur, François, Necoara, Ion

“… We analyze the convergence behavior of a (fast) inexact proximal gradient method using such an oracle for solving (non …”
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On the convergence of an inertial proximal algorithm with a Tikhonov regularization term von László, Szilárd Csaba

“… We show that for appropriate Tikhonov regularization parameters the value of the objective function in the sequences generated by our algorithm converge fast (with arbitrary rate …”
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First-order methods of smooth convex optimization with inexact oracle von Devolder, Olivier, Glineur, François, Nesterov, Yurii

“… in particular their dependence on the accuracy of the oracle and the desired accuracy of the objective function …”
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Proximal gradient methods with inexact oracle of degree q for composite optimization von Nabou, Yassine, Glineur, François, Necoara, Ion

“… We analyze the convergence behavior of a (fast) inexact proximal gradient method using such an oracle for solving (non …”
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Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point von Lu, Songtao, Hong, Mingyi, Wang, Zhengdao

“… Alternating minimization is a simple but popular approach which has been applied to problems in optimization, machine learning, data mining, and signal processing, etc …”
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Local and global convergence of a general inertial proximal splitting scheme for minimizing composite functions von Johnstone, Patrick R., Moulin, Pierre

“… In these problems, the objective is the sum of two closed, proper, and convex functions where one is smooth and the other admits a computationally inexpensive proximal operator …”
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A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem von Xiao, Lin, Zhang, Tong

“… The standard proximal gradient method, also known as iterative soft-thresholding when applied to this problem, has low computational cost per iteration but a rather slow convergence rate …”
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