{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:JC2PW5TKYILAFU6OLMJKMVJQ4M","short_pith_number":"pith:JC2PW5TK","schema_version":"1.0","canonical_sha256":"48b4fb766ac21602d3ce5b12a65530e33f27e4baa22ea00cadc1a464a65f93d0","source":{"kind":"arxiv","id":"2107.13265","version":2},"attestation_state":"computed","paper":{"title":"Learned Optimizers for Analytic Continuation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","cond-mat.str-el"],"primary_cat":"cs.LG","authors_text":"Dongchen Huang, Yi-feng Yang","submitted_at":"2021-07-28T10:57:32Z","abstract_excerpt":"Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for parameter tuning. Here we propose a neural network method by convex optimization and replace the ill-posed inverse problem by a sequence of well-conditioned surrogate problems. After training, the learned optimizers are able to give a solution of high quality with low time cost and achieve higher parameter efficiency than heuristic fully-connected networks. The output can also be used as a neural default model to improve the"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2107.13265","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-28T10:57:32Z","cross_cats_sorted":["cond-mat.stat-mech","cond-mat.str-el"],"title_canon_sha256":"9bd41c05f7a9fec10405bef857218d135c80a2cc0a9638fb3d80b14c4d5ef44d","abstract_canon_sha256":"b198db4b8113c659eaa9a7b266d56822123352dd39cb66e2245eef32befb3ebf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:54:01.309036Z","signature_b64":"2b7uUmVafiIZ7MKbC2u97O73ZBnZ5oZmWzXCCngNJHtz73aQ5sDfqtPZ7a0F9mRpeTh969N2G2iJYfMwXOJaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48b4fb766ac21602d3ce5b12a65530e33f27e4baa22ea00cadc1a464a65f93d0","last_reissued_at":"2026-07-05T03:54:01.308565Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:54:01.308565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learned Optimizers for Analytic Continuation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","cond-mat.str-el"],"primary_cat":"cs.LG","authors_text":"Dongchen Huang, Yi-feng Yang","submitted_at":"2021-07-28T10:57:32Z","abstract_excerpt":"Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for parameter tuning. Here we propose a neural network method by convex optimization and replace the ill-posed inverse problem by a sequence of well-conditioned surrogate problems. After training, the learned optimizers are able to give a solution of high quality with low time cost and achieve higher parameter efficiency than heuristic fully-connected networks. The output can also be used as a neural default model to improve the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.13265","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2107.13265/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2107.13265","created_at":"2026-07-05T03:54:01.308626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.13265v2","created_at":"2026-07-05T03:54:01.308626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.13265","created_at":"2026-07-05T03:54:01.308626+00:00"},{"alias_kind":"pith_short_12","alias_value":"JC2PW5TKYILA","created_at":"2026-07-05T03:54:01.308626+00:00"},{"alias_kind":"pith_short_16","alias_value":"JC2PW5TKYILAFU6O","created_at":"2026-07-05T03:54:01.308626+00:00"},{"alias_kind":"pith_short_8","alias_value":"JC2PW5TK","created_at":"2026-07-05T03:54:01.308626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M","json":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M.json","graph_json":"https://pith.science/api/pith-number/JC2PW5TKYILAFU6OLMJKMVJQ4M/graph.json","events_json":"https://pith.science/api/pith-number/JC2PW5TKYILAFU6OLMJKMVJQ4M/events.json","paper":"https://pith.science/paper/JC2PW5TK"},"agent_actions":{"view_html":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M","download_json":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M.json","view_paper":"https://pith.science/paper/JC2PW5TK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.13265&json=true","fetch_graph":"https://pith.science/api/pith-number/JC2PW5TKYILAFU6OLMJKMVJQ4M/graph.json","fetch_events":"https://pith.science/api/pith-number/JC2PW5TKYILAFU6OLMJKMVJQ4M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M/action/storage_attestation","attest_author":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M/action/author_attestation","sign_citation":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M/action/citation_signature","submit_replication":"https://pith.science/pith/JC2PW5TKYILAFU6OLMJKMVJQ4M/action/replication_record"}},"created_at":"2026-07-05T03:54:01.308626+00:00","updated_at":"2026-07-05T03:54:01.308626+00:00"}