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Katalog Online Perpustakaan Universitas Ma Chung Villa Puncak Tidar N-01 Malang - Jawa Timur.
DDC v.22
Klasifikasi & Katalogisasi DDC versi 22
Validated
| Title | A quasi-Newton acceleration for high-dimensional optimization algorithms |
| Edition | |
| Call Number | |
| ISBN/ISSN | 0960-3174 |
| Author(s) | Hua Zhou Alexander, David Lange, Kenneth |
| Subject(s) | |
| Classification | |
| Series Title | Statistics and Computing | GMD | Electronic Journal |
| Language | English |
| Publisher | Springer Netherlands |
| Publishing Year | |
| Publishing Place | Netherlands |
| Collation | 13p |
| Abstract/Notes | In many statistical problems, maximum likelihood estimation by an EM or MM algorithm suffers from excruciatingly slow convergence. This tendency limits the application of these algorithms to modern high-dimensional problems in data mining, genomics, and imaging. Unfortunately, most existing acceleration techniques are ill-suited to complicated models involving large numbers of parameters. The squared iterative methods (SQUAREM) recently proposed by Varadhan and Roland constitute one notable exception. This paper presents a new quasi-Newton acceleration scheme that requires only modest increments in computation per iteration and overall storage and rivals or surpasses the performance of SQUAREM on several representative test problems. |
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