{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:3SYZKWIAMYHAZKK3QGXPVSFA5K","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":"e17be2c4f3267968d350f611eca28d54ad859ea9eedd3f5f1ca7c2fe8c8d0fa6","cross_cats_sorted":["math.OC","math.PR","math.ST","q-fin.MF","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2020-07-02T17:11:57Z","title_canon_sha256":"1d1d20f2781da8d0d2275441e3f1dbbb5a12795a04f43bf6e18a68dac6f172fe"},"schema_version":"1.0","source":{"id":"2007.01672","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.01672","created_at":"2026-07-05T01:16:03Z"},{"alias_kind":"arxiv_version","alias_value":"2007.01672v1","created_at":"2026-07-05T01:16:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.01672","created_at":"2026-07-05T01:16:03Z"},{"alias_kind":"pith_short_12","alias_value":"3SYZKWIAMYHA","created_at":"2026-07-05T01:16:03Z"},{"alias_kind":"pith_short_16","alias_value":"3SYZKWIAMYHAZKK3","created_at":"2026-07-05T01:16:03Z"},{"alias_kind":"pith_short_8","alias_value":"3SYZKWIA","created_at":"2026-07-05T01:16:03Z"}],"graph_snapshots":[{"event_id":"sha256:8469d03305023d2c4c35b8e4a1ea3426111040d820bd60d84dddda1378754bc8","target":"graph","created_at":"2026-07-05T01:16:03Z","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/2007.01672/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics (SGLD) algorithms, which is a variant of stochastic gradient decent (SGD) methods, allows us to efficiently approximate global minimizers of possibly complicated, high-dimensional landscapes. With this in mind, we extend here the non-asymptotic analysis of SGLD to the case of discontinuous stochastic gradients. We are thus able to provide theoretical guarantees for the algorithm's convergence in (standard) Wasserstein distances for both convex and non-convex objective functions. We also provide expli","authors_text":"Sotirios Sabanis, Ying Zhang","cross_cats":["math.OC","math.PR","math.ST","q-fin.MF","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2020-07-02T17:11:57Z","title":"A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.01672","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":"sha256:9fdc7278ac220614010b2a4f0028602bef893ffc8bd4ce1d1b5b75eebdb52093","target":"record","created_at":"2026-07-05T01:16:03Z","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":"e17be2c4f3267968d350f611eca28d54ad859ea9eedd3f5f1ca7c2fe8c8d0fa6","cross_cats_sorted":["math.OC","math.PR","math.ST","q-fin.MF","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-fin.PM","submitted_at":"2020-07-02T17:11:57Z","title_canon_sha256":"1d1d20f2781da8d0d2275441e3f1dbbb5a12795a04f43bf6e18a68dac6f172fe"},"schema_version":"1.0","source":{"id":"2007.01672","kind":"arxiv","version":1}},"canonical_sha256":"dcb1955900660e0ca95b81aefac8a0ea8a73aa83c00d6a4033a5c4dd77c6bb33","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcb1955900660e0ca95b81aefac8a0ea8a73aa83c00d6a4033a5c4dd77c6bb33","first_computed_at":"2026-07-05T01:16:03.972229Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:16:03.972229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SlJ8CkvpAfkHHmFLy+gIG4vp3O011tis/zi0CEzJCu+wSnEmntlNqethRlibXHVyUviJzRJzW2iEQ4puPWIKCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:16:03.972762Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.01672","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9fdc7278ac220614010b2a4f0028602bef893ffc8bd4ce1d1b5b75eebdb52093","sha256:8469d03305023d2c4c35b8e4a1ea3426111040d820bd60d84dddda1378754bc8"],"state_sha256":"530647a381eb6b221461303fa385a6edb80c203a11c10244ac842a42b9e722e1"}