{"created":"2021-03-01T07:32:22.941630+00:00","id":51891,"links":{},"metadata":{"_buckets":{"deposit":"0b06d874-4c6a-489c-9eee-6a527bee2d1c"},"_deposit":{"id":"51891","owners":[],"pid":{"revision_id":0,"type":"depid","value":"51891"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00051891","sets":["152:4944","152:6669","3:62:5592:6668"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2018-10","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"64","bibliographicPageStart":"61","bibliographicVolumeNumber":"10","bibliographic_titles":[{"bibliographic_title":"JSIAM Letters"}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Tensor renormalization group (TRG) is a coarse-graining algorithm for approximating the partition function using a tensor network in the field of elementary particle physics. Although the computational cost of TRG can be reduced using a randomized singular value decomposition, its computation time is still large. In this paper, we propose a cost-efficient cutoff method for calculating TRG by truncating small tensor elements. Numerical experiments showed that the proposed method is faster than the conventional one without degrading accuracy. ","subitem_description_type":"Abstract"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"The Japan Society for Industrial and Applied Mathematics"}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.14495/jsiaml.10.61","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2018 Japan Society for Industrial and Applied Mathematics"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1883-0609","subitem_source_identifier_type":"ISSN"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"今倉, 暁"},{"creatorName":"イマクラ, アキラ","creatorNameLang":"ja-Kana"},{"creatorName":"IMAKURA, Akira","creatorNameLang":"en"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"櫻井, 鉄也"},{"creatorName":"サクライ, テツヤ","creatorNameLang":"ja-Kana"},{"creatorName":"SAKURAI, Tetsuya","creatorNameLang":"en"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"Yamada, Haruka","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-08-09"}],"displaytype":"detail","filename":"JSIAML_10-61.pdf","filesize":[{"value":"245.2 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"JSIAML_10-61","url":"https://tsukuba.repo.nii.ac.jp/record/51891/files/JSIAML_10-61.pdf"},"version_id":"4218daa2-dda1-4820-beca-ed03027b27f8"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Cost-efficient cutoff method for tensor renormalization group with randomized singular value decomposition","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Cost-efficient cutoff method for tensor renormalization group with randomized singular value decomposition"}]},"item_type_id":"5","owner":"1","path":["6669","4944","6668"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-08-09"},"publish_date":"2019-08-09","publish_status":"0","recid":"51891","relation_version_is_last":true,"title":["Cost-efficient cutoff method for tensor renormalization group with randomized singular value decomposition"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T09:25:12.543119+00:00"}