Vote for your favorite mineral in #MinCup25! - Carpathite vs. Tugtupite
Battle of mineralogical oddities as organic mineral #carpathite battles against vs #tugtupite, a mineral that looks like reindeer blood!
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Jiang, Hong, Wilford, Paul (2012) A stochastic conjugate gradient method for the approximation of functions. Journal of Computational and Applied Mathematics, 236 (9). 2529-2544 doi:10.1016/j.cam.2011.12.012

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA stochastic conjugate gradient method for the approximation of functions
JournalJournal of Computational and Applied Mathematics
AuthorsJiang, HongAuthor
Wilford, PaulAuthor
Year2012 (March)Volume236
Issue9
PublisherElsevier BV
DOIdoi:10.1016/j.cam.2011.12.012Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9498085Long-form Identifiermindat:1:5:9498085:1
GUID0
Full ReferenceJiang, Hong, Wilford, Paul (2012) A stochastic conjugate gradient method for the approximation of functions. Journal of Computational and Applied Mathematics, 236 (9). 2529-2544 doi:10.1016/j.cam.2011.12.012
Plain TextJiang, Hong, Wilford, Paul (2012) A stochastic conjugate gradient method for the approximation of functions. Journal of Computational and Applied Mathematics, 236 (9). 2529-2544 doi:10.1016/j.cam.2011.12.012
In(2012, March) Journal of Computational and Applied Mathematics Vol. 236 (9) Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Helfenstein, Rudi, Koko, Jonas (2012) Parallel preconditioned conjugate gradient algorithm on GPU. Journal of Computational and Applied Mathematics, 236 (15). 3584-3590 doi:10.1016/j.cam.2011.04.025
Fatemi, Masoud (2016) A new efficient conjugate gradient method for unconstrained optimization. Journal of Computational and Applied Mathematics, 300. 207-216 doi:10.1016/j.cam.2015.12.035
Xiao, Yunhai, Song, Huina, Wang, Zhiguo (2012) A modified conjugate gradient algorithm with cyclic Barzilai–Borwein steplength for unconstrained optimization. Journal of Computational and Applied Mathematics, 236 (13). 3101-3110 doi:10.1016/j.cam.2012.01.032
Ziadi, Raouf, Ellaia, Rachid, Bencherif-Madani, Abdelatif (2017) Global optimization through a stochastic perturbation of the Polak–Ribière conjugate gradient method. Journal of Computational and Applied Mathematics, 317. 672-684 doi:10.1016/j.cam.2016.12.021
Narushima, Yasushi, Yabe, Hiroshi (2012) Conjugate gradient methods based on secant conditions that generate descent search directions for unconstrained optimization. Journal of Computational and Applied Mathematics, 236 (17). 4303-4317 doi:10.1016/j.cam.2012.01.036
Ahmedov, Anvarjon A., Abdul Aziz, Norashikin (2012) The approximation of functions from LlogL(loglogL)(SN) by Fourier–L. Journal of Computational and Applied Mathematics, 236 (15). 3827-3834 doi:10.1016/j.cam.2011.08.018
Gnandt, Christian, Callies, Rainer (2018) CGRS — An advanced hybrid method for global optimization of continuous functions closely coupling extended random search and conjugate gradient method. Journal of Computational and Applied Mathematics, 333. 99-115 doi:10.1016/j.cam.2017.10.018
Mastronardi, Nicola, Van Dooren, Paul (2012) Recursive approximation of the dominant eigenspace of an indefinite matrix. Journal of Computational and Applied Mathematics, 236 (16). 4090-4104 doi:10.1016/j.cam.2012.02.032
SangĂĽesa, C. (2011) On the approximation of functions satisfying defective renewal equations. Journal of Computational and Applied Mathematics, 236 (6). 1042-1054 doi:10.1016/j.cam.2011.07.014
Andrei, Neculai (2009) Accelerated conjugate gradient algorithm with finite difference Hessian/vector product approximation for unconstrained optimization. Journal of Computational and Applied Mathematics, 230 (2). 570-582 doi:10.1016/j.cam.2008.12.024
Almutairi, Hanan, Meurant, Gérard, Reichel, Lothar, Spalević, Miodrag M. (2025) New error estimates for the conjugate gradient method. Journal of Computational and Applied Mathematics, 459. doi:10.1016/j.cam.2024.116357
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 20, 2025 15:00:42
Go to top of page