{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:OTQXYFHZZ3U65ZKLHLE4ZVNQOC","short_pith_number":"pith:OTQXYFHZ","canonical_record":{"source":{"id":"1807.00173","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2018-06-30T13:03:48Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"dd58fd1620445e283384ace2044188a82f31fd500b7fb6a386f5fed0c205b648","abstract_canon_sha256":"46f88ed1f5196a80dfe84ba2f542f30f7862cdddc4dc265e18071b89b2308cb2"},"schema_version":"1.0"},"canonical_sha256":"74e17c14f9cee9eee54b3ac9ccd5b070880ef783255482ee8a9987a020c8fef4","source":{"kind":"arxiv","id":"1807.00173","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00173","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00173v1","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00173","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"pith_short_12","alias_value":"OTQXYFHZZ3U6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OTQXYFHZZ3U65ZKL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OTQXYFHZ","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:OTQXYFHZZ3U65ZKLHLE4ZVNQOC","target":"record","payload":{"canonical_record":{"source":{"id":"1807.00173","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2018-06-30T13:03:48Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"dd58fd1620445e283384ace2044188a82f31fd500b7fb6a386f5fed0c205b648","abstract_canon_sha256":"46f88ed1f5196a80dfe84ba2f542f30f7862cdddc4dc265e18071b89b2308cb2"},"schema_version":"1.0"},"canonical_sha256":"74e17c14f9cee9eee54b3ac9ccd5b070880ef783255482ee8a9987a020c8fef4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:55.806953Z","signature_b64":"lc0J1/SqVx2KTxLQ8KAR3tmVdAxQyvLUD8mvGHJYwhiqmBnrDcoGHetTOq80iSYVb0+IHwIqeazYWe3TVeAmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74e17c14f9cee9eee54b3ac9ccd5b070880ef783255482ee8a9987a020c8fef4","last_reissued_at":"2026-05-18T00:11:55.806369Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:55.806369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.00173","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ah+RmtwWqHRmnDLr//CRMMF2uN8Pe6eOQi9AyIGBXAayaWAo4mSi4PonC/T2LzEcIYUYFccUkjYTHAZxyUvBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:02:50.951518Z"},"content_sha256":"d8bbc28357fcad3e80985377af3368574e92dfd92fc837f752adce528f43acfb","schema_version":"1.0","event_id":"sha256:d8bbc28357fcad3e80985377af3368574e92dfd92fc837f752adce528f43acfb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:OTQXYFHZZ3U65ZKLHLE4ZVNQOC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"math.OC","authors_text":"Tomas Pevny, Vyacheslav Kungurtsev","submitted_at":"2018-06-30T13:03:48Z","abstract_excerpt":"Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems. The resulting optimization problem to solve for the optimal vector minimizing the empirical risk is, however, highly nonconvex. This alone presents a challenge to application and development of appropriate optimization algorithms for solving the problem. However, in addition, there are a number of interesting problems for which the objective function is non- smooth and nonseparable. In this paper, we summarize the primary challenges involved, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00173","kind":"arxiv","version":1},"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":"2026-05-18T00:11:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ATE6tDylGwuuL5Klt7sh6GYduiM8fAsQm3QKTwQYby7xOujzzE3OhyxajDpoZOO/9RUGrSvSZf7RG13fJB2VBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T02:02:50.951863Z"},"content_sha256":"f1f1684511c989eef5068d66749216d291c2a801db28100fb12624e13af1dbea","schema_version":"1.0","event_id":"sha256:f1f1684511c989eef5068d66749216d291c2a801db28100fb12624e13af1dbea"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/bundle.json","state_url":"https://pith.science/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/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":"2026-05-28T02:02:50Z","links":{"resolver":"https://pith.science/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC","bundle":"https://pith.science/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/bundle.json","state":"https://pith.science/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OTQXYFHZZ3U65ZKLHLE4ZVNQOC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:OTQXYFHZZ3U65ZKLHLE4ZVNQOC","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":"46f88ed1f5196a80dfe84ba2f542f30f7862cdddc4dc265e18071b89b2308cb2","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2018-06-30T13:03:48Z","title_canon_sha256":"dd58fd1620445e283384ace2044188a82f31fd500b7fb6a386f5fed0c205b648"},"schema_version":"1.0","source":{"id":"1807.00173","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.00173","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"arxiv_version","alias_value":"1807.00173v1","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00173","created_at":"2026-05-18T00:11:55Z"},{"alias_kind":"pith_short_12","alias_value":"OTQXYFHZZ3U6","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"OTQXYFHZZ3U65ZKL","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"OTQXYFHZ","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:f1f1684511c989eef5068d66749216d291c2a801db28100fb12624e13af1dbea","target":"graph","created_at":"2026-05-18T00:11:55Z","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":"Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems. The resulting optimization problem to solve for the optimal vector minimizing the empirical risk is, however, highly nonconvex. This alone presents a challenge to application and development of appropriate optimization algorithms for solving the problem. However, in addition, there are a number of interesting problems for which the objective function is non- smooth and nonseparable. In this paper, we summarize the primary challenges involved, ","authors_text":"Tomas Pevny, Vyacheslav Kungurtsev","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2018-06-30T13:03:48Z","title":"Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00173","kind":"arxiv","version":1},"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:d8bbc28357fcad3e80985377af3368574e92dfd92fc837f752adce528f43acfb","target":"record","created_at":"2026-05-18T00:11:55Z","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":"46f88ed1f5196a80dfe84ba2f542f30f7862cdddc4dc265e18071b89b2308cb2","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2018-06-30T13:03:48Z","title_canon_sha256":"dd58fd1620445e283384ace2044188a82f31fd500b7fb6a386f5fed0c205b648"},"schema_version":"1.0","source":{"id":"1807.00173","kind":"arxiv","version":1}},"canonical_sha256":"74e17c14f9cee9eee54b3ac9ccd5b070880ef783255482ee8a9987a020c8fef4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74e17c14f9cee9eee54b3ac9ccd5b070880ef783255482ee8a9987a020c8fef4","first_computed_at":"2026-05-18T00:11:55.806369Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:55.806369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lc0J1/SqVx2KTxLQ8KAR3tmVdAxQyvLUD8mvGHJYwhiqmBnrDcoGHetTOq80iSYVb0+IHwIqeazYWe3TVeAmBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:55.806953Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.00173","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8bbc28357fcad3e80985377af3368574e92dfd92fc837f752adce528f43acfb","sha256:f1f1684511c989eef5068d66749216d291c2a801db28100fb12624e13af1dbea"],"state_sha256":"ed8d97ad38f4769ba58cd3b2feebee58889b0abbfe98452fea5c21b064ac2cd3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YUtxtWugLOE5k8RlicQ3jJzWATKEgcfjgXGkKdP2UWxD3CVOT3yJUTzPWeybQpR6J1tLRbSQoiaESK0Q6CMgCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T02:02:50.953774Z","bundle_sha256":"bf0a22d08e47ca1236db13a63ea71539e40dcb93b180e867c45a1c835adbab07"}}