{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:BOCKDMJJ64TJJMQFFQULQDK6TQ","short_pith_number":"pith:BOCKDMJJ","canonical_record":{"source":{"id":"1612.06003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-18T22:14:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b52a32bdc9432abf1e924dcb1a10a78b6b39fb37c291f466776648cae423ab3a","abstract_canon_sha256":"a681ffe7d8d0a5f2fac57535e747828add9856172be4780efcd651f2bda1c1ba"},"schema_version":"1.0"},"canonical_sha256":"0b84a1b129f72694b2052c28b80d5e9c3eb7a94dd3b987554634072095ddff5c","source":{"kind":"arxiv","id":"1612.06003","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.06003","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"1612.06003v2","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.06003","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"BOCKDMJJ64TJ","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BOCKDMJJ64TJJMQF","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BOCKDMJJ","created_at":"2026-05-18T12:30:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:BOCKDMJJ64TJJMQFFQULQDK6TQ","target":"record","payload":{"canonical_record":{"source":{"id":"1612.06003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-18T22:14:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b52a32bdc9432abf1e924dcb1a10a78b6b39fb37c291f466776648cae423ab3a","abstract_canon_sha256":"a681ffe7d8d0a5f2fac57535e747828add9856172be4780efcd651f2bda1c1ba"},"schema_version":"1.0"},"canonical_sha256":"0b84a1b129f72694b2052c28b80d5e9c3eb7a94dd3b987554634072095ddff5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:16.442337Z","signature_b64":"cd7gsemcq1TxhBaZAbx1Youxu+y2FTCbgJpEmeyl0AFnf2mlOK7vUgQ8z+mJpKaausRRnuxzFrKCUeQj/WwPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b84a1b129f72694b2052c28b80d5e9c3eb7a94dd3b987554634072095ddff5c","last_reissued_at":"2026-05-18T00:06:16.441880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:16.441880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.06003","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jBrOU2pXCrmGwbOGpjkelPZwDQM9BZXEBXKuyFGXnQGYNwqgPhq1rghxtxUIC03i40hJBd43NWctELdWPLOcCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:09:28.522332Z"},"content_sha256":"48b003307f21ac86f1b48263455b4f2166625ebc2e2f287813c894eb90560c1c","schema_version":"1.0","event_id":"sha256:48b003307f21ac86f1b48263455b4f2166625ebc2e2f287813c894eb90560c1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:BOCKDMJJ64TJJMQFFQULQDK6TQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bin Gu, De Wang, Heng Huang, Zhouyuan Huo","submitted_at":"2016-12-18T22:14:36Z","abstract_excerpt":"In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic solution. However, existing inexact proximal gradient methods only consider convex problems. The knowledge of inexact proximal gradient methods in the non-convex setting is very limited. % Moreover, for some machine learning models, there is still no proposed solver for exactly solv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.06003","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":""},"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":"2026-05-18T00:06:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SwqgU5sL9QPUUNN1qrmUHPBA9zDHYa0E4ON/0qMCnN1G/P2QV7HqeIJBWxSrwF6ddPZYkJtG5j91Ant0rYrIBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T13:09:28.522689Z"},"content_sha256":"2a183413f76a7e2663c23fe09b4cc46ac7c8e7f1e9f9d3525877e461319c5a9d","schema_version":"1.0","event_id":"sha256:2a183413f76a7e2663c23fe09b4cc46ac7c8e7f1e9f9d3525877e461319c5a9d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/bundle.json","state_url":"https://pith.science/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/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":"2026-06-04T13:09:28Z","links":{"resolver":"https://pith.science/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ","bundle":"https://pith.science/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/bundle.json","state":"https://pith.science/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BOCKDMJJ64TJJMQFFQULQDK6TQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:BOCKDMJJ64TJJMQFFQULQDK6TQ","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":"a681ffe7d8d0a5f2fac57535e747828add9856172be4780efcd651f2bda1c1ba","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-18T22:14:36Z","title_canon_sha256":"b52a32bdc9432abf1e924dcb1a10a78b6b39fb37c291f466776648cae423ab3a"},"schema_version":"1.0","source":{"id":"1612.06003","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.06003","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"arxiv_version","alias_value":"1612.06003v2","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.06003","created_at":"2026-05-18T00:06:16Z"},{"alias_kind":"pith_short_12","alias_value":"BOCKDMJJ64TJ","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"BOCKDMJJ64TJJMQF","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"BOCKDMJJ","created_at":"2026-05-18T12:30:07Z"}],"graph_snapshots":[{"event_id":"sha256:2a183413f76a7e2663c23fe09b4cc46ac7c8e7f1e9f9d3525877e461319c5a9d","target":"graph","created_at":"2026-05-18T00:06:16Z","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":"In machine learning research, the proximal gradient methods are popular for solving various optimization problems with non-smooth regularization. Inexact proximal gradient methods are extremely important when exactly solving the proximal operator is time-consuming, or the proximal operator does not have an analytic solution. However, existing inexact proximal gradient methods only consider convex problems. The knowledge of inexact proximal gradient methods in the non-convex setting is very limited. % Moreover, for some machine learning models, there is still no proposed solver for exactly solv","authors_text":"Bin Gu, De Wang, Heng Huang, Zhouyuan Huo","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-18T22:14:36Z","title":"Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.06003","kind":"arxiv","version":2},"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":"sha256:48b003307f21ac86f1b48263455b4f2166625ebc2e2f287813c894eb90560c1c","target":"record","created_at":"2026-05-18T00:06:16Z","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":"a681ffe7d8d0a5f2fac57535e747828add9856172be4780efcd651f2bda1c1ba","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-12-18T22:14:36Z","title_canon_sha256":"b52a32bdc9432abf1e924dcb1a10a78b6b39fb37c291f466776648cae423ab3a"},"schema_version":"1.0","source":{"id":"1612.06003","kind":"arxiv","version":2}},"canonical_sha256":"0b84a1b129f72694b2052c28b80d5e9c3eb7a94dd3b987554634072095ddff5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b84a1b129f72694b2052c28b80d5e9c3eb7a94dd3b987554634072095ddff5c","first_computed_at":"2026-05-18T00:06:16.441880Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:16.441880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cd7gsemcq1TxhBaZAbx1Youxu+y2FTCbgJpEmeyl0AFnf2mlOK7vUgQ8z+mJpKaausRRnuxzFrKCUeQj/WwPCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:16.442337Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.06003","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:48b003307f21ac86f1b48263455b4f2166625ebc2e2f287813c894eb90560c1c","sha256:2a183413f76a7e2663c23fe09b4cc46ac7c8e7f1e9f9d3525877e461319c5a9d"],"state_sha256":"ca754f39e34f0bfbe9d7cbd4dc5859b80586b5c4c52098f2095b1d62f14121ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ider4/SH+Bmj0Hy8XIYSvTKCbJkAWWzJ/0zazpxoE01ULE+5z9enjviyl5F1b3XWDBx84nH7Dwxv7+Lphn62AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T13:09:28.524725Z","bundle_sha256":"e7b547c6dc5f40275ec9b2b7c59535cbf73d6423609b313de80f6377305f500e"}}