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Статті в журналах з теми "Inertial Bregman proximal gradient"
Mukkamala, Mahesh Chandra, Peter Ochs, Thomas Pock, and Shoham Sabach. "Convex-Concave Backtracking for Inertial Bregman Proximal Gradient Algorithms in Nonconvex Optimization." SIAM Journal on Mathematics of Data Science 2, no. 3 (January 2020): 658–82. http://dx.doi.org/10.1137/19m1298007.
Повний текст джерелаKabbadj, S. "Inexact Version of Bregman Proximal Gradient Algorithm." Abstract and Applied Analysis 2020 (April 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/1963980.
Повний текст джерелаZhou, Yi, Yingbin Liang, and Lixin Shen. "A simple convergence analysis of Bregman proximal gradient algorithm." Computational Optimization and Applications 73, no. 3 (April 4, 2019): 903–12. http://dx.doi.org/10.1007/s10589-019-00092-y.
Повний текст джерелаHanzely, Filip, Peter Richtárik, and Lin Xiao. "Accelerated Bregman proximal gradient methods for relatively smooth convex optimization." Computational Optimization and Applications 79, no. 2 (April 7, 2021): 405–40. http://dx.doi.org/10.1007/s10589-021-00273-8.
Повний текст джерелаMahadevan, Sridhar, Stephen Giguere, and Nicholas Jacek. "Basis Adaptation for Sparse Nonlinear Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 654–60. http://dx.doi.org/10.1609/aaai.v27i1.8665.
Повний текст джерелаYang, Lei, and Kim-Chuan Toh. "Bregman Proximal Point Algorithm Revisited: A New Inexact Version and Its Inertial Variant." SIAM Journal on Optimization 32, no. 3 (July 13, 2022): 1523–54. http://dx.doi.org/10.1137/20m1360748.
Повний текст джерелаLi, Jing, Xiao Wei, Fengpin Wang, and Jinjia Wang. "IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning." Electronics 10, no. 23 (December 3, 2021): 3021. http://dx.doi.org/10.3390/electronics10233021.
Повний текст джерелаXiao, Xiantao. "A Unified Convergence Analysis of Stochastic Bregman Proximal Gradient and Extragradient Methods." Journal of Optimization Theory and Applications 188, no. 3 (January 8, 2021): 605–27. http://dx.doi.org/10.1007/s10957-020-01799-3.
Повний текст джерелаWang, Qingsong, Zehui Liu, Chunfeng Cui, and Deren Han. "A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (March 24, 2024): 15580–88. http://dx.doi.org/10.1609/aaai.v38i14.29485.
Повний текст джерелаHe, Lulu, Jimin Ye, and Jianwei E. "Nonconvex optimization with inertial proximal stochastic variance reduction gradient." Information Sciences 648 (November 2023): 119546. http://dx.doi.org/10.1016/j.ins.2023.119546.
Повний текст джерелаДисертації з теми "Inertial Bregman proximal gradient"
Godeme, Jean-Jacques. "Ρhase retrieval with nοn-Euclidean Bregman based geοmetry". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC214.
Повний текст джерелаIn this work, we investigate the phase retrieval problem of real-valued signals in finite dimension, a challenge encountered across various scientific and engineering disciplines. It explores two complementary approaches: retrieval with and without regularization. In both settings, our work is focused on relaxing the Lipschitz-smoothness assumption generally required by first-order splitting algorithms, and which is not valid for phase retrieval cast as a minimization problem. The key idea here is to replace the Euclidean geometry by a non-Euclidean Bregman divergence associated to an appropriate kernel. We use a Bregman gradient/mirror descent algorithm with this divergence to solve thephase retrieval problem without regularization, and we show exact (up to a global sign) recovery both in a deterministic setting and with high probability for a sufficient number of random measurements (Gaussian and Coded Diffraction Patterns). Furthermore, we establish the robustness of this approachagainst small additive noise. Shifting to regularized phase retrieval, we first develop and analyze an Inertial Bregman Proximal Gradient algorithm for minimizing the sum of two functions in finite dimension, one of which is convex and possibly nonsmooth and the second is relatively smooth in the Bregman geometry. We provide both global and local convergence guarantees for this algorithm. Finally, we study noiseless and stable recovery of low complexity regularized phase retrieval. For this, weformulate the problem as the minimization of an objective functional involving a nonconvex smooth data fidelity term and a convex regularizer promoting solutions conforming to some notion of low-complexity related to their nonsmoothness points. We establish conditions for exact and stable recovery and provide sample complexity bounds for random measurements to ensure that these conditions hold. These sample bounds depend on the low complexity of the signals to be recovered. Our new results allow to go far beyond the case of sparse phase retrieval
Частини книг з теми "Inertial Bregman proximal gradient"
Mukkamala, Mahesh Chandra, Felix Westerkamp, Emanuel Laude, Daniel Cremers, and Peter Ochs. "Bregman Proximal Gradient Algorithms for Deep Matrix Factorization." In Lecture Notes in Computer Science, 204–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75549-2_17.
Повний текст джерелаТези доповідей конференцій з теми "Inertial Bregman proximal gradient"
Li, Huan, Wenjuan Zhang, Shujian Huang, and Feng Xiao. "Poisson Noise Image Restoration Based on Bregman Proximal Gradient." In 2023 6th International Conference on Computer Network, Electronic and Automation (ICCNEA). IEEE, 2023. http://dx.doi.org/10.1109/iccnea60107.2023.00058.
Повний текст джерелаPu, Wenqiang, Jiawei Zhang, Rui Zhou, Xiao Fu, and Mingyi Hong. "A Smoothed Bregman Proximal Gradient Algorithm for Decentralized Nonconvex Optimization." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10448285.
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