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@article{Ho:2022:10.1080/10556788.2019.1700256,
author = {Ho, CP and Kocvara, M and Parpas, P},
doi = {10.1080/10556788.2019.1700256},
journal = {Optimization Methods and Software},
pages = {45--78},
title = {Newton-type multilevel optimization method},
url = {http://dx.doi.org/10.1080/10556788.2019.1700256},
volume = {37},
year = {2022}
}

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TY  - JOUR
AB  - Inspired by multigrid methods for linear systems of equations, multilevel optimization methods have been proposed to solve structured optimization problems. Multilevel methods make more assumptions regarding the structure of the optimization model, and as a result, they outperform single-level methods, especially for large-scale models. The impressive performance of multilevel optimization methods is an empirical observation, and no theoretical explanation has so far been proposed. In order to address this issue, we study the convergence properties of a multilevel method that is motivated by second-order methods. We take the first step toward establishing how the structure of an optimization problem is related to the convergence rate of multilevel algorithms.
AU  - Ho,CP
AU  - Kocvara,M
AU  - Parpas,P
DO  - 10.1080/10556788.2019.1700256
EP  - 78
PY  - 2022///
SN  - 1029-4937
SP  - 45
TI  - Newton-type multilevel optimization method
T2  - Optimization Methods and Software
UR  - http://dx.doi.org/10.1080/10556788.2019.1700256
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000502443800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/10556788.2019.1700256
UR  - http://hdl.handle.net/10044/1/76748
VL  - 37
ER  -

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