| bibtype |
J -
Journal Article
|
| ARLID |
0532968 |
| utime |
20240103224520.4 |
| mtime |
20201012235959.9 |
| SCOPUS |
85076357344 |
| WOS |
000502443800001 |
| DOI |
10.1080/10556788.2019.1700256 |
| title
(primary) (eng) |
Newton-type multilevel optimization method |
| specification |
| page_count |
34 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0254588 |
| ISSN |
1055-6788 |
| title
|
Optimization Methods & Software |
| volume_id |
37 |
| volume |
1 (2022) |
| page_num |
45-78 |
| publisher |
|
|
| keyword |
Newton's method |
| keyword |
multilevel algorithms |
| keyword |
multigrid methods |
| keyword |
unconstrained optimization |
| author
(primary) |
| ARLID |
cav_un_auth*0396914 |
| name1 |
Ho |
| name2 |
Ch. P. |
| country |
GB |
| share |
33 |
|
| author
|
| ARLID |
cav_un_auth*0101131 |
| name1 |
Kočvara |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| share |
34 |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0396915 |
| name1 |
Parpas |
| name2 |
P. |
| country |
GB |
|
| source |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10101 |
| reportyear |
2023 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0311787 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCESOFTWAREENGINEERING|MATHEMATICSAPPLIED|OPERATIONSRESEARCHMANAGEMENTSCIENCE |
| mrcbT16-j |
1.039 |
| mrcbT16-s |
1.079 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q2 |
| arlyear |
2022 |
| mrcbU14 |
85076357344 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000502443800001 WOS |
| mrcbU63 |
cav_un_epca*0254588 Optimization Methods & Software 1055-6788 1029-4937 Roč. 37 č. 1 2022 45 78 Taylor & Francis |
|