{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FPCDXH2BR5TG25QMM3UB5HYU43","short_pith_number":"pith:FPCDXH2B","canonical_record":{"source":{"id":"1808.07181","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T01:47:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f2e1956c0208782ac825b22bcfcd9181c3e2f52f773f2f2bc47df40c0af81443","abstract_canon_sha256":"06bbf5488c3a521aae3d9a0b6597cd5464d0091d73aadd9cb8d974e07bc653fd"},"schema_version":"1.0"},"canonical_sha256":"2bc43b9f418f666d760c66e81e9f14e6d49c269984d57ef1d98c2852353893db","source":{"kind":"arxiv","id":"1808.07181","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.07181","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"1808.07181v3","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07181","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"FPCDXH2BR5TG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FPCDXH2BR5TG25QM","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FPCDXH2B","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FPCDXH2BR5TG25QMM3UB5HYU43","target":"record","payload":{"canonical_record":{"source":{"id":"1808.07181","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T01:47:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f2e1956c0208782ac825b22bcfcd9181c3e2f52f773f2f2bc47df40c0af81443","abstract_canon_sha256":"06bbf5488c3a521aae3d9a0b6597cd5464d0091d73aadd9cb8d974e07bc653fd"},"schema_version":"1.0"},"canonical_sha256":"2bc43b9f418f666d760c66e81e9f14e6d49c269984d57ef1d98c2852353893db","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:15.961950Z","signature_b64":"zCLfPZE6a3Mu6UbuHrIQkDW29bGVp/QkUGRGBpm1ykHg8f4fI+lFDs/JcILWidKZ66IcmPoL009L1j8J/LGJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bc43b9f418f666d760c66e81e9f14e6d49c269984d57ef1d98c2852353893db","last_reissued_at":"2026-05-17T23:47:15.961559Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:15.961559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.07181","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Awt7HuV9OJNpLNmnpTHiQ4gdj37rjp94FQbO0XuZ5bzNwc1HDi4+q9wqrt7YOz3/3jPJfeeZRZf5ap5izN4cBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:16:41.774987Z"},"content_sha256":"ef7940f95f759fe9b2afcc757b5a89199975115fcb1f092f15c805651585f032","schema_version":"1.0","event_id":"sha256:ef7940f95f759fe9b2afcc757b5a89199975115fcb1f092f15c805651585f032"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FPCDXH2BR5TG25QMM3UB5HYU43","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient sparse semismooth Newton methods for the clustered lasso problem","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"math.OC","authors_text":"Defeng Sun, Kim-Chuan Toh, Meixia Lin, Yong-jin Liu","submitted_at":"2018-08-22T01:47:53Z","abstract_excerpt":"We focus on solving the clustered lasso problem, which is a least squares problem with the $\\ell_1$-type penalties imposed on both the coefficients and their pairwise differences to learn the group structure of the regression parameters. Here we first reformulate the clustered lasso regularizer as a weighted ordered-lasso regularizer, which is essential in reducing the computational cost from $O(n^2)$ to $O(n\\log (n))$. We then propose an inexact semismooth Newton augmented Lagrangian ({\\sc Ssnal}) algorithm to solve the clustered lasso problem or its dual via this equivalent formulation, depe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07181","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lOS/m95zsfqpcrKDjbwdAFO5OQ/cgtSwpr/INIu1KU41LKzQsU0EeAfgLEiZTy4Qkb27tTyigCuEIQkIZgmCDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:16:41.775639Z"},"content_sha256":"a5b65790c7ef410055cce5799ac1e6d565772659fda62e431282571132288bdb","schema_version":"1.0","event_id":"sha256:a5b65790c7ef410055cce5799ac1e6d565772659fda62e431282571132288bdb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FPCDXH2BR5TG25QMM3UB5HYU43/bundle.json","state_url":"https://pith.science/pith/FPCDXH2BR5TG25QMM3UB5HYU43/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FPCDXH2BR5TG25QMM3UB5HYU43/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T17:16:41Z","links":{"resolver":"https://pith.science/pith/FPCDXH2BR5TG25QMM3UB5HYU43","bundle":"https://pith.science/pith/FPCDXH2BR5TG25QMM3UB5HYU43/bundle.json","state":"https://pith.science/pith/FPCDXH2BR5TG25QMM3UB5HYU43/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FPCDXH2BR5TG25QMM3UB5HYU43/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FPCDXH2BR5TG25QMM3UB5HYU43","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":"06bbf5488c3a521aae3d9a0b6597cd5464d0091d73aadd9cb8d974e07bc653fd","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T01:47:53Z","title_canon_sha256":"f2e1956c0208782ac825b22bcfcd9181c3e2f52f773f2f2bc47df40c0af81443"},"schema_version":"1.0","source":{"id":"1808.07181","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.07181","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"1808.07181v3","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07181","created_at":"2026-05-17T23:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"FPCDXH2BR5TG","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FPCDXH2BR5TG25QM","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FPCDXH2B","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:a5b65790c7ef410055cce5799ac1e6d565772659fda62e431282571132288bdb","target":"graph","created_at":"2026-05-17T23:47:15Z","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"},"paper":{"abstract_excerpt":"We focus on solving the clustered lasso problem, which is a least squares problem with the $\\ell_1$-type penalties imposed on both the coefficients and their pairwise differences to learn the group structure of the regression parameters. Here we first reformulate the clustered lasso regularizer as a weighted ordered-lasso regularizer, which is essential in reducing the computational cost from $O(n^2)$ to $O(n\\log (n))$. We then propose an inexact semismooth Newton augmented Lagrangian ({\\sc Ssnal}) algorithm to solve the clustered lasso problem or its dual via this equivalent formulation, depe","authors_text":"Defeng Sun, Kim-Chuan Toh, Meixia Lin, Yong-jin Liu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T01:47:53Z","title":"Efficient sparse semismooth Newton methods for the clustered lasso problem"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07181","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:ef7940f95f759fe9b2afcc757b5a89199975115fcb1f092f15c805651585f032","target":"record","created_at":"2026-05-17T23:47:15Z","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":"06bbf5488c3a521aae3d9a0b6597cd5464d0091d73aadd9cb8d974e07bc653fd","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-08-22T01:47:53Z","title_canon_sha256":"f2e1956c0208782ac825b22bcfcd9181c3e2f52f773f2f2bc47df40c0af81443"},"schema_version":"1.0","source":{"id":"1808.07181","kind":"arxiv","version":3}},"canonical_sha256":"2bc43b9f418f666d760c66e81e9f14e6d49c269984d57ef1d98c2852353893db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2bc43b9f418f666d760c66e81e9f14e6d49c269984d57ef1d98c2852353893db","first_computed_at":"2026-05-17T23:47:15.961559Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:15.961559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zCLfPZE6a3Mu6UbuHrIQkDW29bGVp/QkUGRGBpm1ykHg8f4fI+lFDs/JcILWidKZ66IcmPoL009L1j8J/LGJBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:15.961950Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.07181","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ef7940f95f759fe9b2afcc757b5a89199975115fcb1f092f15c805651585f032","sha256:a5b65790c7ef410055cce5799ac1e6d565772659fda62e431282571132288bdb"],"state_sha256":"f063142fde6f23e043c4ddee44494c217c2d6baf829a03ec664b5a684fdd9eb5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0TiTeY0pQPrHgYLtwx2JD7lQZIOT59lUttjFHkQLqLifNNPn9n9zY4mBT6E9cQPZdL2XgbJ1ceb2Ef+vvLdhBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:16:41.779654Z","bundle_sha256":"e2945f27abc46a222ecd6c0651170a344ba0ebaeefe4c0b71d0dec3556bb56bb"}}