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Asymptotic optimality of the quasi-score estimator in a class of linear score estimators [PDF]
, 2006 We prove that the quasi-score estimator in a mean-variance model is optimal in the class of (unbiased) linear score estimators, in the sense that the difference of the asymptotic covariance matrices of the linear score and quasi-score estimator is ...
Kukush, Alexander, Schneeweiß, Hans
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Learning to Optimize Quasi-Newton Methods
CoRR, 2022 Fast gradient-based optimization algorithms have become increasingly essential for the computationally efficient training of machine learning models. One technique is to multiply the gradient by a preconditioner matrix to produce a step, but it is unclear what the best preconditioner matrix is.
Isaac Liao +3 more
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Journal of Harbin University of Science and Technology, 2017 A Modified Broyden algorithm is presented to solve nonlinear equations in this paper． The convergence of the new algorithml is proved． The convergent order and effectiveness of improved Broyden method can be verified by numerical experiments． Through ...
LI Fu-xiang, HUANG Jia-yue
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Remote Sensing, 2023 The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model.
Deshan Feng +5 more
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A New Sparse Quasi-Newton Update Method
Sultan Qaboos University Journal for Science, 2012 Based on the idea of maximum determinant positive definite matrix completion, Yamashita proposed a sparse quasi-Newton update, called MCQN, for unconstrained optimization problems with sparse Hessian structures.
Minghou Cheng, Yu-Hong Dai, Rui Diao
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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.
A. Hassan, Basim +3 more
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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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A non-Secant quasi-Newton Method for Unconstrained Nonlinear Optimization
Cogent Engineering, 2022 The Secant equation has long been the foundation of quasi-Newton methods, as updated Hessian approximations satisfy the equation with each iteration. Several publications have lately focused on modified versions of the Secant relation, with promising ...
Issam A.R. Moghrabi
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Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method
, 1974 To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function we need only specify a relation between the mean and variance of the observations and the quasi-likelihood can then be used ...
Wedderburn, R. W. M.
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An improved quasi-Newton equation on the quasi-Newton methods for unconstrained optimizations
, 2021 Quasi-Newton methods are a class of numerical methods for solving the problem of unconstrained optimization. To improve the overall efficiency of resulting algorithms, we use the quasi-Newton methods which is interesting for quasi-Newton equation.
Abubakar, Auwal Bala +6 more
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