-
- Ota Ryo
- Graduate School of Library, Information and Media Studies, University of Tsukuba
-
- Hasegawa Hidehiko
- Faculty of Library, Information and Media Science, University of Tsukuba
説明
<p> The convergence of the BiConjugate Gradient (BiCG) method depends on its input matrices. We tried to predict the convergence of BiCG method by applying a Convolutional Neural Network to matrices that had been converted to grayscale images. Using 875 real non-symmetric matrices in the SuiteSparse Matrix Collection, we applied the 5-fold cross-validation method and were able to predict convergence with an average accuracy that exceeded 80\% for all cases in the test collection. </p>
収録刊行物
-
- JSIAM Letters
-
JSIAM Letters 12 (0), 45-48, 2020
一般社団法人 日本応用数理学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390566775155781120
-
- NII論文ID
- 130007881798
-
- ISSN
- 18830617
- 18830609
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
- OpenAIRE
-
- 抄録ライセンスフラグ
- 使用不可