{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:R7I6I23CIJM7VNA5BZGX2R6OEA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8857756c545a48a1ab9f17b80bd461fd116aa1c79cee5d6a0303a0116051387d","cross_cats_sorted":["cs.MS","cs.SE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2025-11-04T10:03:15Z","title_canon_sha256":"fa47ff625547cc28c0ea4b9939a782a3b388bafd4c3987cd83b7ad871e3183d2"},"schema_version":"1.0","source":{"id":"2511.02430","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.02430","created_at":"2026-06-12T01:09:15Z"},{"alias_kind":"arxiv_version","alias_value":"2511.02430v3","created_at":"2026-06-12T01:09:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.02430","created_at":"2026-06-12T01:09:15Z"},{"alias_kind":"pith_short_12","alias_value":"R7I6I23CIJM7","created_at":"2026-06-12T01:09:15Z"},{"alias_kind":"pith_short_16","alias_value":"R7I6I23CIJM7VNA5","created_at":"2026-06-12T01:09:15Z"},{"alias_kind":"pith_short_8","alias_value":"R7I6I23C","created_at":"2026-06-12T01:09:15Z"}],"graph_snapshots":[{"event_id":"sha256:5b876b99dd3384eedd31ee788cfa7fb43d8911e2c3f8ec8c807b7f0bce36c272","target":"graph","created_at":"2026-06-12T01:09:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2511.02430/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a suite of packages in R, Python, Julia, and C++ that efficiently solve the Sorted L-One Penalized Estimation (SLOPE) problem. The packages feature a highly efficient hybrid coordinate descent algorithm that fits generalized linear models (GLMs) and supports a variety of loss functions, including Gaussian, binomial, Poisson, and multinomial logistic regression. Our implementation is designed to be fast, memory-efficient, and flexible. The packages support a variety of data structures (dense, sparse, and out-of-memory matrices) and are designed to efficiently fit the full SLOPE path ","authors_text":"Johan Larsson, Jonas Wallin, Krystyna Grzesiak, Malgorzata Bogdan, Mathurin Massias","cross_cats":["cs.MS","cs.SE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2025-11-04T10:03:15Z","title":"Efficient Solvers for SLOPE in R, Python, Julia, and C++"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.02430","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6bb446b5b843c3a02cead2d4f1c5fd5ae1f2fddf686b6acfbb39a408d96ff976","target":"record","created_at":"2026-06-12T01:09:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8857756c545a48a1ab9f17b80bd461fd116aa1c79cee5d6a0303a0116051387d","cross_cats_sorted":["cs.MS","cs.SE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2025-11-04T10:03:15Z","title_canon_sha256":"fa47ff625547cc28c0ea4b9939a782a3b388bafd4c3987cd83b7ad871e3183d2"},"schema_version":"1.0","source":{"id":"2511.02430","kind":"arxiv","version":3}},"canonical_sha256":"8fd1e46b624259fab41d0e4d7d47ce201626c25218fd48d9b56c711888ad79a5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fd1e46b624259fab41d0e4d7d47ce201626c25218fd48d9b56c711888ad79a5","first_computed_at":"2026-06-12T01:09:15.620651Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:09:15.620651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pOhQHxDKxTiljfglkNl2vykD5nEe7YZ7+VfZmYz6R5t2PjKJRgL6RSmtkJLVRRgztbJHlAljEIvMiN3tBAjlBw==","signature_status":"signed_v1","signed_at":"2026-06-12T01:09:15.621580Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.02430","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6bb446b5b843c3a02cead2d4f1c5fd5ae1f2fddf686b6acfbb39a408d96ff976","sha256:5b876b99dd3384eedd31ee788cfa7fb43d8911e2c3f8ec8c807b7f0bce36c272"],"state_sha256":"6eb77c1319d0e6ba63bf50beb97485b9b0fe0ba44ca1252f809ed5340bdd1213"}