Results 31 to 40 of about 118,287 (121)
On Quasi-Newton Forward--Backward Splitting: Proximal Calculus and Convergence [PDF]
, 2018 We introduce a framework for quasi-Newton forward--backward splitting algorithms (proximal quasi-Newton methods) with a metric induced by diagonal $\pm$ rank-$r$ symmetric positive definite matrices.
Becker, Stephen +2 more
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Reconstruction of high-resolution image from movie frames. [PDF]
, 2003 by Ling Kai Tung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 44-45).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.7Chapter 2 --- Fundamentals --- p.9Chapter 2.1 --- Digital ...
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Stabilized material point methods for coupled large deformation and fluid flow in porous materials
, 2019 The material point method (MPM) has been increasingly used for the simulation of large deformation processes in fluid-infiltrated porous materials. For undrained poromechanical problems, however, standard MPMs are numerically unstable because they use ...
Choo, Jinhyun, Zhao, Yidong
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Some diagonal preconditioners for limited memory quasi-Newton method for large Scale optimization [PDF]
, 2013 One of the well-known methods in solving large scale unconstrained optimization is limited memory quasi-Newton (LMQN) method. This method is derived from a subproblem in low dimension so that the storage requirement as well as the computation cost can be
Abu Hassan, Malik +3 more
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Large-scale Reservoir Simulations on IBM Blue Gene/Q
, 2016 This paper presents our work on simulation of large-scale reservoir models on IBM Blue Gene/Q and studying the scalability of our parallel reservoir simulators. An in-house black oil simulator has been implemented.
H Liu +7 more
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Quasi-Newton particle Metropolis-Hastings [PDF]
, 2015 Particle Metropolis-Hastings enables Bayesian parameter inference in general nonlinear state space models (SSMs). However, in many implementations a random walk proposal is used and this can result in poor mixing if not tuned correctly using tedious ...
Dahlin, Johan +2 more
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On the construction of probabilistic Newton-type algorithms
, 2017 It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions.
Schön, Thomas B., Wills, Adrian G.
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IMRO: a proximal quasi-Newton method for solving $l_1$-regularized least square problem
, 2017 We present a proximal quasi-Newton method in which the approximation of the Hessian has the special format of "identity minus rank one" (IMRO) in each iteration. The proposed structure enables us to effectively recover the proximal point.
Karimi, Sahar, Vavasis, Stephen
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Hybrid Deterministic-Stochastic Methods for Data Fitting
, 2011 Many structured data-fitting applications require the solution of an optimization problem involving a sum over a potentially large number of measurements.
Kumar S. +5 more
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Ensemble Kalman filter for neural network based one-shot inversion
, 2020 We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from ...
Guth, Philipp A. +2 more
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