{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OSCIV7Q7KEPEAM3X65LUROIMEE","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":"03f49c29626a58d7f5763bb02ff0d725a0f79aed0850aa90551e5fc8aca6a8ed","cross_cats_sorted":["cs.LG","math.OC","q-fin.PM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-15T09:01:07Z","title_canon_sha256":"c24dfb3d56c551c13b9dcf3fa3dddf0593570d3f287deffc600d8581ee267db6"},"schema_version":"1.0","source":{"id":"2505.10099","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.10099","created_at":"2026-07-05T11:03:33Z"},{"alias_kind":"arxiv_version","alias_value":"2505.10099v1","created_at":"2026-07-05T11:03:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10099","created_at":"2026-07-05T11:03:33Z"},{"alias_kind":"pith_short_12","alias_value":"OSCIV7Q7KEPE","created_at":"2026-07-05T11:03:33Z"},{"alias_kind":"pith_short_16","alias_value":"OSCIV7Q7KEPEAM3X","created_at":"2026-07-05T11:03:33Z"},{"alias_kind":"pith_short_8","alias_value":"OSCIV7Q7","created_at":"2026-07-05T11:03:33Z"}],"graph_snapshots":[{"event_id":"sha256:eb31b577c15fdc1d898fa1bbd1896a5117631b86ebff3dde3aa498afb7ae920c","target":"graph","created_at":"2026-07-05T11:03:33Z","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/2505.10099/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse portfolio selection extends this by imposing a cardinality constraint: only $k$ assets from a universe of $p$ may be included. The standard approach models this problem as a mixed-integer quadratic program and relies on commercial solvers to find the optimal solution. However, the computational costs of such methods increase exponentially with $k$ and $p$, making them too slow for problems of even moderate size. We propo","authors_text":"Matias Quiroz, Samuel Muller, Sarat Moka, Vali Asimit","cross_cats":["cs.LG","math.OC","q-fin.PM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-15T09:01:07Z","title":"A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10099","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":"sha256:007cba1a7d0e96e8bf9c159fa297a9eaa0e691e9dab251d569cb2d2d716f4b10","target":"record","created_at":"2026-07-05T11:03:33Z","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":"03f49c29626a58d7f5763bb02ff0d725a0f79aed0850aa90551e5fc8aca6a8ed","cross_cats_sorted":["cs.LG","math.OC","q-fin.PM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-05-15T09:01:07Z","title_canon_sha256":"c24dfb3d56c551c13b9dcf3fa3dddf0593570d3f287deffc600d8581ee267db6"},"schema_version":"1.0","source":{"id":"2505.10099","kind":"arxiv","version":1}},"canonical_sha256":"74848afe1f511e403377f75748b90c2107047a1870b386da6d033271b4d2ce34","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74848afe1f511e403377f75748b90c2107047a1870b386da6d033271b4d2ce34","first_computed_at":"2026-07-05T11:03:33.249816Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:03:33.249816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xDR5JxU/dLK8CjgQLuj8ZTzT5UyeLXGSHE2PNwaRSW5s0v3LSVcDYP50QvEf019fJJFB22FYN/f2fSOt2Zd3CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:03:33.250272Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.10099","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:007cba1a7d0e96e8bf9c159fa297a9eaa0e691e9dab251d569cb2d2d716f4b10","sha256:eb31b577c15fdc1d898fa1bbd1896a5117631b86ebff3dde3aa498afb7ae920c"],"state_sha256":"105e980b9e4b19da21f3eb5c68bf8c5244193628512382d3bec66a769f778e7c"}