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   Articles with "bfgs" as a keyword



Three-dimensional magnetotelluric inversion using L-BFGS

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recommendations! Published in 2020 at "Acta Geophysica"

DOI: 10.1007/s11600-020-00456-7

Abstract: The gradient-based optimization methods are preferable for the large-scale three-dimensional (3D) magnetotelluric (MT) inverse problem. Compared with the popular nonlinear conjugate gradient (NLCG) method, however, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method is less adopted. This paper… read more here.

Keywords: three dimensional; dimensional magnetotelluric; bfgs; inversion ... See more keywords

A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms

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recommendations! Published in 2017 at "Neurocomputing"

DOI: 10.1016/j.neucom.2017.05.061

Abstract: Abstract Working up with deep learning techniques requires profound understanding of the mechanisms underlying the optimization of the internal parameters of complex structures. The major factor limiting this understanding is that there exist only a… read more here.

Keywords: limited memory; training strategy; optimization; bfgs ... See more keywords
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A hybrid scaling parameter for the scaled memoryless BFGS method based on the ℓ∞ matrix norm

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recommendations! Published in 2019 at "International Journal of Computer Mathematics"

DOI: 10.1080/00207160.2018.1465940

Abstract: ABSTRACT An upper bound for condition number of the scaled memoryless Broyden–Fletcher–Goldfarb–Shanno (BFGS) updating formula in the matrix norm is given. Then, in order to increase numerical stability of the related method, the suggested bound… read more here.

Keywords: bfgs; method; matrix norm; scaling parameter ... See more keywords
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Pre-conditioned BFGS-based uncertainty quantification in elastic full-waveform inversion

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recommendations! Published in 2021 at "Geophysical Journal International"

DOI: 10.1093/gji/ggab375

Abstract: Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior… read more here.

Keywords: full waveform; bfgs; uncertainty; uncertainty quantification ... See more keywords
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An Efficient Linear Detection Scheme Based on L-BFGS Method for Massive MIMO Systems

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recommendations! Published in 2022 at "IEEE Communications Letters"

DOI: 10.1109/lcomm.2021.3121445

Abstract: For massive multiple-input multiple-output (MIMO) systems, minimum mean square error (MMSE) detection is near-optimal, but requires high-complexity matrix inversion. To avoid matrix inversion, we formulate MMSE detection as a strictly convex quadratic optimization problem, which… read more here.

Keywords: mimo systems; detection; scheme; massive mimo ... See more keywords
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An Accelerated Linearly Convergent Stochastic L-BFGS Algorithm

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recommendations! Published in 2019 at "IEEE Transactions on Neural Networks and Learning Systems"

DOI: 10.1109/tnnls.2019.2891088

Abstract: The limited memory version of the Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm is the most popular quasi-Newton algorithm in machine learning and optimization. Recently, it was shown that the stochastic L-BFGS (sL-BFGS) algorithm with the variance-reduced stochastic gradient… read more here.

Keywords: stochastic bfgs; accelerated linearly; bfgs; bfgs algorithm ... See more keywords
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Decentralized Quasi-Newton Methods

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recommendations! Published in 2017 at "IEEE Transactions on Signal Processing"

DOI: 10.1109/tsp.2017.2666776

Abstract: We introduce the decentralized Broyden–Fletcher–Goldfarb–Shanno (D-BFGS) method as a variation of the BFGS quasi-Newton method for solving decentralized optimization problems. Decentralized quasi-Newton methods are of interest in problems that are not well conditioned, making first-order… read more here.

Keywords: order; bfgs; quasi newton; newton methods ... See more keywords

HLRF-BFGS-Based Algorithm for Inverse Reliability Analysis

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recommendations! Published in 2017 at "Mathematical Problems in Engineering"

DOI: 10.1155/2017/4317670

Abstract: This study proposes an algorithm to solve inverse reliability problems with a single unknown parameter. The proposed algorithm is based on an existing algorithm, the inverse first-order reliability method (inverse-FORM), which uses the Hasofer Lind… read more here.

Keywords: hlrf; bfgs; inverse form; reliability ... See more keywords