{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:UVEPJSIJJIIBSG4OZA2O7LXDMX","short_pith_number":"pith:UVEPJSIJ","schema_version":"1.0","canonical_sha256":"a548f4c9094a10191b8ec834efaee365f7916bd6fb2e65466f3d909b5eac6da8","source":{"kind":"arxiv","id":"1709.01610","version":2},"attestation_state":"computed","paper":{"title":"A second order primal-dual method for nonsmooth convex composite optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY","nlin.AO"],"primary_cat":"math.OC","authors_text":"Mihailo R. Jovanovi\\'c, Neil K. Dhingra, Sei Zhen Khong","submitted_at":"2017-09-05T22:08:41Z","abstract_excerpt":"We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. The saddle point of this function corresponds to the solution of the original optimization problem. We employ a generalization of the Hessian to define"},"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":"1709.01610","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-05T22:08:41Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY","nlin.AO"],"title_canon_sha256":"a9d29331c845fd0e69589fc13a81e093bb4936a61dbb6bc37c7abdaac98f7ebb","abstract_canon_sha256":"a8e6846ffa493c539c3356ba918bb67a7eee0398a4c5c034b9bfabe7a008a094"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T19:10:56.851908Z","signature_b64":"tjAE0fHFlQsh6Z+bzZ3Lqmh/BuppFuZM+VRZxVlg8IoXUlZvUIAXbiT8M3KMlahrOrg8dOgEJzCFK7eAHUvPBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a548f4c9094a10191b8ec834efaee365f7916bd6fb2e65466f3d909b5eac6da8","last_reissued_at":"2026-06-04T19:10:56.851412Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T19:10:56.851412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A second order primal-dual method for nonsmooth convex composite optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY","nlin.AO"],"primary_cat":"math.OC","authors_text":"Mihailo R. Jovanovi\\'c, Neil K. Dhingra, Sei Zhen Khong","submitted_at":"2017-09-05T22:08:41Z","abstract_excerpt":"We develop a second order primal-dual method for optimization problems in which the objective function is given by the sum of a strongly convex twice differentiable term and a possibly nondifferentiable convex regularizer. After introducing an auxiliary variable, we utilize the proximal operator of the nonsmooth regularizer to transform the associated augmented Lagrangian into a function that is once, but not twice, continuously differentiable. The saddle point of this function corresponds to the solution of the original optimization problem. We employ a generalization of the Hessian to define"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01610","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/1709.01610/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":"1709.01610","created_at":"2026-06-04T19:10:56.851473+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.01610v2","created_at":"2026-06-04T19:10:56.851473+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01610","created_at":"2026-06-04T19:10:56.851473+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVEPJSIJJIIB","created_at":"2026-06-04T19:10:56.851473+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVEPJSIJJIIBSG4O","created_at":"2026-06-04T19:10:56.851473+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVEPJSIJ","created_at":"2026-06-04T19:10:56.851473+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.09043","citing_title":"Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX","json":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX.json","graph_json":"https://pith.science/api/pith-number/UVEPJSIJJIIBSG4OZA2O7LXDMX/graph.json","events_json":"https://pith.science/api/pith-number/UVEPJSIJJIIBSG4OZA2O7LXDMX/events.json","paper":"https://pith.science/paper/UVEPJSIJ"},"agent_actions":{"view_html":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX","download_json":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX.json","view_paper":"https://pith.science/paper/UVEPJSIJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.01610&json=true","fetch_graph":"https://pith.science/api/pith-number/UVEPJSIJJIIBSG4OZA2O7LXDMX/graph.json","fetch_events":"https://pith.science/api/pith-number/UVEPJSIJJIIBSG4OZA2O7LXDMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX/action/storage_attestation","attest_author":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX/action/author_attestation","sign_citation":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX/action/citation_signature","submit_replication":"https://pith.science/pith/UVEPJSIJJIIBSG4OZA2O7LXDMX/action/replication_record"}},"created_at":"2026-06-04T19:10:56.851473+00:00","updated_at":"2026-06-04T19:10:56.851473+00:00"}