{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:U7GTAEOPJMMEXQF2NAJYHRONBE","short_pith_number":"pith:U7GTAEOP","schema_version":"1.0","canonical_sha256":"a7cd3011cf4b184bc0ba681383c5cd091273cefd522329e56b4437d6b7825b0d","source":{"kind":"arxiv","id":"1709.08308","version":2},"attestation_state":"computed","paper":{"title":"Self-tuned mirror descent schemes for smooth and nonsmooth high-dimensional stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Arash Pourhabib, Farzad Yousefian, Nahidsadat Majlesinasab","submitted_at":"2017-09-25T03:57:53Z","abstract_excerpt":"We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective functions. Our goal is to develop RBSMD schemes that achieve a rate of convergence with a minimum constant factor with respect to the choice of the stepsize sequence. To this end, we consider both subgradient and gradient RBSMD methods addressing nonsmooth and smooth problems, respectively. For each scheme, (i) we develop self-tuned stepsize rules characterized in terms of problem parameters and algorithm settings; (ii) w"},"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":"1709.08308","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-09-25T03:57:53Z","cross_cats_sorted":[],"title_canon_sha256":"55d8e2534c8ac11afadd3619aa21403f32a49c5dd77d8537fe2f0649869938e8","abstract_canon_sha256":"33f35503d2a4d92112bd764c88aa4ae563f3c88adceb3f06f705ef991d6dfb41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:03.228660Z","signature_b64":"4UFdNN059QosM2KLdxGEj31ROnIOdK+Dn5ZVTiXfiHqbGrwCYwJQse/6lIrC86yq2SVPiacZiPu4+p4lxySmCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7cd3011cf4b184bc0ba681383c5cd091273cefd522329e56b4437d6b7825b0d","last_reissued_at":"2026-05-17T23:54:03.228254Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:03.228254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-tuned mirror descent schemes for smooth and nonsmooth high-dimensional stochastic optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Arash Pourhabib, Farzad Yousefian, Nahidsadat Majlesinasab","submitted_at":"2017-09-25T03:57:53Z","abstract_excerpt":"We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective functions. Our goal is to develop RBSMD schemes that achieve a rate of convergence with a minimum constant factor with respect to the choice of the stepsize sequence. To this end, we consider both subgradient and gradient RBSMD methods addressing nonsmooth and smooth problems, respectively. For each scheme, (i) we develop self-tuned stepsize rules characterized in terms of problem parameters and algorithm settings; (ii) w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.08308","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.08308","created_at":"2026-05-17T23:54:03.228328+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.08308v2","created_at":"2026-05-17T23:54:03.228328+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.08308","created_at":"2026-05-17T23:54:03.228328+00:00"},{"alias_kind":"pith_short_12","alias_value":"U7GTAEOPJMME","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"U7GTAEOPJMMEXQF2","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"U7GTAEOP","created_at":"2026-05-18T12:31:46.661854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE","json":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE.json","graph_json":"https://pith.science/api/pith-number/U7GTAEOPJMMEXQF2NAJYHRONBE/graph.json","events_json":"https://pith.science/api/pith-number/U7GTAEOPJMMEXQF2NAJYHRONBE/events.json","paper":"https://pith.science/paper/U7GTAEOP"},"agent_actions":{"view_html":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE","download_json":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE.json","view_paper":"https://pith.science/paper/U7GTAEOP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.08308&json=true","fetch_graph":"https://pith.science/api/pith-number/U7GTAEOPJMMEXQF2NAJYHRONBE/graph.json","fetch_events":"https://pith.science/api/pith-number/U7GTAEOPJMMEXQF2NAJYHRONBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE/action/storage_attestation","attest_author":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE/action/author_attestation","sign_citation":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE/action/citation_signature","submit_replication":"https://pith.science/pith/U7GTAEOPJMMEXQF2NAJYHRONBE/action/replication_record"}},"created_at":"2026-05-17T23:54:03.228328+00:00","updated_at":"2026-05-17T23:54:03.228328+00:00"}