{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:AYP2OS6556AZGXTZJN4DRZZQXI","short_pith_number":"pith:AYP2OS65","schema_version":"1.0","canonical_sha256":"061fa74bddef81935e794b7838e730ba3c7890fd334999ecec55e4e47632ffac","source":{"kind":"arxiv","id":"1603.03236","version":4},"attestation_state":"computed","paper":{"title":"Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC","stat.ML"],"primary_cat":"cs.MS","authors_text":"James Townsend, Niklas Koep, Sebastian Weichwald","submitted_at":"2016-03-10T12:23:12Z","abstract_excerpt":"Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold. While many optimization problems are of the described form, technicalities of differential geometry and the laborious calculation of derivatives pose a significant barrier for experimenting with these methods.\n  We introduce Pymanopt (available at https://pymanopt.github.io), a toolbox for optimization on manifolds, implemented in Python, that---sim"},"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":"1603.03236","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2016-03-10T12:23:12Z","cross_cats_sorted":["cs.LG","math.OC","stat.ML"],"title_canon_sha256":"d92b2e28fdb8526b360236fb3a5315cfa54090c9bf58b116fb40dbcda7f017e6","abstract_canon_sha256":"67f5b0e6bdc5dba5ee83cf2c48e7d32473adb636a78f7be75e9cd8ce75d51b0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:32:20.576341Z","signature_b64":"sPYDNRFxSNAAYTcp1+i//Io/uk64y3noXixdig/ZrPzETsZnK9a4982X5p2VAB9SUc5kJNfpRO9CzDFWknFnDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"061fa74bddef81935e794b7838e730ba3c7890fd334999ecec55e4e47632ffac","last_reissued_at":"2026-07-05T01:32:20.575985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:32:20.575985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC","stat.ML"],"primary_cat":"cs.MS","authors_text":"James Townsend, Niklas Koep, Sebastian Weichwald","submitted_at":"2016-03-10T12:23:12Z","abstract_excerpt":"Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold. While many optimization problems are of the described form, technicalities of differential geometry and the laborious calculation of derivatives pose a significant barrier for experimenting with these methods.\n  We introduce Pymanopt (available at https://pymanopt.github.io), a toolbox for optimization on manifolds, implemented in Python, that---sim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.03236","kind":"arxiv","version":4},"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/1603.03236/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":"1603.03236","created_at":"2026-07-05T01:32:20.576040+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.03236v4","created_at":"2026-07-05T01:32:20.576040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.03236","created_at":"2026-07-05T01:32:20.576040+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYP2OS6556AZ","created_at":"2026-07-05T01:32:20.576040+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYP2OS6556AZGXTZ","created_at":"2026-07-05T01:32:20.576040+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYP2OS65","created_at":"2026-07-05T01:32:20.576040+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23560","citing_title":"A modified Riemannian Levenberg-Marquardt Algorithm for robust or constraint optimization on manifolds","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2507.08080","citing_title":"Diagonal Isometric Form for Tensor Network States in Two Dimensions","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2512.11967","citing_title":"Holographic Representation of One-Dimensional Many-Body Quantum States via Isometric Tensor Networks","ref_index":81,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02279","citing_title":"Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05070","citing_title":"Nonconvex optimization methods for ground states in disordered continuous-spin models","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI","json":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI.json","graph_json":"https://pith.science/api/pith-number/AYP2OS6556AZGXTZJN4DRZZQXI/graph.json","events_json":"https://pith.science/api/pith-number/AYP2OS6556AZGXTZJN4DRZZQXI/events.json","paper":"https://pith.science/paper/AYP2OS65"},"agent_actions":{"view_html":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI","download_json":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI.json","view_paper":"https://pith.science/paper/AYP2OS65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.03236&json=true","fetch_graph":"https://pith.science/api/pith-number/AYP2OS6556AZGXTZJN4DRZZQXI/graph.json","fetch_events":"https://pith.science/api/pith-number/AYP2OS6556AZGXTZJN4DRZZQXI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI/action/storage_attestation","attest_author":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI/action/author_attestation","sign_citation":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI/action/citation_signature","submit_replication":"https://pith.science/pith/AYP2OS6556AZGXTZJN4DRZZQXI/action/replication_record"}},"created_at":"2026-07-05T01:32:20.576040+00:00","updated_at":"2026-07-05T01:32:20.576040+00:00"}