Results 41 to 50 of about 1,099,998 (291)
Non-asymptotic superlinear convergence of standard quasi-Newton methods [PDF]
Mathematical programming, 2020 In this paper, we study and prove the non-asymptotic superlinear convergence rate of the Broyden class of quasi-Newton algorithms which includes the Davidon–Fletcher–Powell (DFP) method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method.
Qiu-Jiang Jin, Aryan Mokhtari
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Faster Stochastic Quasi-Newton Methods [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems such as training neural networks due to low per-iteration computational complexity.
Qingsong Zhang +3 more
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Distributed Quasi-Newton Method for Multi-Agent Optimization [PDF]
IEEE Transactions on Signal ProcessingWe present a distributed quasi-Newton (DQN) method, which enables a group of agents to compute an optimal solution of a separable multi-agent optimization problem locally using an approximation of the curvature of the aggregate objective function.
Olaoluwa Shorinwa, Mac Schwager
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Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
Algorithms, 2023 While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-
Mahsa Yousefi, Ángeles Martínez
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Algorithms, 2021 Gradient-based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving ...
S. Indrapriyadarsini +4 more
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Quasi-Newton Methods: A New Direction
CoRR, 2012 ICML2012
Hennig, P., Kiefel, M.
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Fast Converging Implementation of a Region-Based Active Contour Model
Journal of Algorithms & Computational Technology, 2015 PDE-based image segmentation based on the active contour model attracts many researchers due to its high precision of edge detection and the continuity of boundaries.
Haiping Xu, Hanxiang Zheng, Meiqing Wang
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Axioms, 2018 This work studies limited memory preconditioners for linear symmetric positive definite systems of equations. Connections are established between a partial Cholesky factorization from the literature and a variant of Quasi-Newton type preconditioners ...
Benedetta Morini
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A new class of self-scaling for quasi-newton method based on the quadratic model
, 2021 Quasi-Newton method is an efficient method for solving unconstrained optimization problems. Self-scaling is one of the common approaches in the modification of the quasi-Newton method.
Basim A. Hassan, Ranen M. Sulaiman
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A Newton-Like Trust Region Method for Large-Scale Unconstrained Nonconvex Minimization
Abstract and Applied Analysis, 2013 We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new straightforward limited memory quasi-Newton updating based on the modified quasi-Newton equation is deduced to construct the trust region subproblem, in ...
Yang Weiwei +3 more
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