Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Richard H. Byrd"'
Publikováno v:
SIAM Journal on Optimization. 29:1870-1878
The finite-dimensional McCormick second-order sufficiency theory for nonlinear programming problems with a finite number of constraints is now a classical part of the optimization literature. It wa...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac43ed2d7f358de6282415a2daa84592
https://doi.org/10.1137/19m1239337
https://doi.org/10.1137/19m1239337
Autor:
Paul T. Boggs, Richard H. Byrd
Publikováno v:
SIAM Journal on Optimization. 29:1282-1299
The limited-memory BFGS method (L-BFGS) has become the workhorse optimization strategy for many large-scale nonlinear optimization problems. A major difficulty with L-BFGS is that, although the mem...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff37206f5bd85e830b36dd0bd843a2f0
https://doi.org/10.1137/16m1065100
https://doi.org/10.1137/16m1065100
Publikováno v:
SIAM Journal on Optimization. 29:965-993
This paper presents a finite difference quasi-Newton method for the minimization of noisy functions. The method takes advantage of the scalability and power of BFGS updating, and employs an adaptive procedure for choosing the differencing interval $h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2f0cfccb2794f74f138ffb4d73d67da
https://doi.org/10.1137/18m1177718
https://doi.org/10.1137/18m1177718
Publikováno v:
NeuroImage. 149:63-84
Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13f6f44ed439d52c35cb1796d14fc0ee
https://doi.org/10.1016/j.neuroimage.2016.12.081
https://doi.org/10.1016/j.neuroimage.2016.12.081
The classical convergence analysis of quasi-Newton methods assumes that the function and gradients employed at each iteration are exact. In this paper, we consider the case when there are (bounded) errors in both computations and establish conditions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::252764476f76be200ecc240092d73103
Publikováno v:
SIAM Journal on Optimization. 26:1008-1031
The question of how to incorporate curvature information into stochastic approximation methods is challenging. The direct application of classical quasi-Newton updating techniques for deterministic optimization leads to noisy curvature estimates that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed351bd33a94c6a3bd0cdc37cefb0054
https://doi.org/10.1137/140954362
https://doi.org/10.1137/140954362
Publikováno v:
Optimization Methods and Software. 30:1213-1237
We present an active-set method for minimizing an objective that is the sum of a convex quadratic and regularization term. Unlike two-phase methods that combine a first-order active set identification step and a subspace phase consisting of a cycle o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e774a601e6516037c61955a6500ebf4e
https://doi.org/10.1080/10556788.2015.1028062
https://doi.org/10.1080/10556788.2015.1028062
In this paper, we propose a stochastic optimization method that adaptively controls the sample size used in the computation of gradient approximations. Unlike other variance reduction techniques that either require additional storage or the regular c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c057c392f46532de49b5c6f6d7d56f7
http://arxiv.org/abs/1710.11258
http://arxiv.org/abs/1710.11258
Autor:
Rajesh Nandy, Richard H. Byrd, Karthik Ramakrishnan Sreenivasan, Tim Curran, Xiaowei Zhuang, Dietmar Cordes, Zhengshi Yang, Virendra Mishra
Publikováno v:
NeuroImage. 169
Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d705bb1bec33be05d53ab87cc96c2da6
https://pubmed.ncbi.nlm.nih.gov/29248697
https://pubmed.ncbi.nlm.nih.gov/29248697
The paper studies the solution of stochastic optimization problems in which approximations to the gradient and Hessian are obtained through subsampling. We first consider Newton-like methods that employ these approximations and discuss how to coordin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80a14bda3febcc2a3ff1f3372cdb007b
http://arxiv.org/abs/1609.08502
http://arxiv.org/abs/1609.08502