{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:3W52F6WW2TKQGJ6JDIMQ2FCC7V","short_pith_number":"pith:3W52F6WW","schema_version":"1.0","canonical_sha256":"ddbba2fad6d4d50327c91a190d1442fd4b958cae8da35e9dbe69200c8848e656","source":{"kind":"arxiv","id":"1102.3749","version":1},"attestation_state":"computed","paper":{"title":"Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Anupam Gupta, Marco Molinaro, Ravishankar Krishnaswamy, R. Ravi","submitted_at":"2011-02-18T04:05:21Z","abstract_excerpt":"In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed?\n  The stochastic knapsack problem is of interest in its own right, but technique"},"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":"1102.3749","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2011-02-18T04:05:21Z","cross_cats_sorted":[],"title_canon_sha256":"a752674533ee88228c9894e12cd380e9b755fb35948b568623c522361eb2c9e7","abstract_canon_sha256":"e0ed7b57f3f5b3779608c183a9a6fe3623c2a40a55a60488b87daff86bb886d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:28:21.063340Z","signature_b64":"M4dBbotpfmJNmRlrPiMb6vposXfBwaUcgPEctE/EVUf9R7wEh8z4Xh7jtzB4F5IJO93rSfmTR4BYA/wTupyoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddbba2fad6d4d50327c91a190d1442fd4b958cae8da35e9dbe69200c8848e656","last_reissued_at":"2026-05-18T04:28:21.062824Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:28:21.062824Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DS","authors_text":"Anupam Gupta, Marco Molinaro, Ravishankar Krishnaswamy, R. Ravi","submitted_at":"2011-02-18T04:05:21Z","abstract_excerpt":"In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed?\n  The stochastic knapsack problem is of interest in its own right, but technique"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1102.3749","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1102.3749","created_at":"2026-05-18T04:28:21.062909+00:00"},{"alias_kind":"arxiv_version","alias_value":"1102.3749v1","created_at":"2026-05-18T04:28:21.062909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1102.3749","created_at":"2026-05-18T04:28:21.062909+00:00"},{"alias_kind":"pith_short_12","alias_value":"3W52F6WW2TKQ","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_16","alias_value":"3W52F6WW2TKQGJ6J","created_at":"2026-05-18T12:26:20.644004+00:00"},{"alias_kind":"pith_short_8","alias_value":"3W52F6WW","created_at":"2026-05-18T12:26:20.644004+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/3W52F6WW2TKQGJ6JDIMQ2FCC7V","json":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V.json","graph_json":"https://pith.science/api/pith-number/3W52F6WW2TKQGJ6JDIMQ2FCC7V/graph.json","events_json":"https://pith.science/api/pith-number/3W52F6WW2TKQGJ6JDIMQ2FCC7V/events.json","paper":"https://pith.science/paper/3W52F6WW"},"agent_actions":{"view_html":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V","download_json":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V.json","view_paper":"https://pith.science/paper/3W52F6WW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1102.3749&json=true","fetch_graph":"https://pith.science/api/pith-number/3W52F6WW2TKQGJ6JDIMQ2FCC7V/graph.json","fetch_events":"https://pith.science/api/pith-number/3W52F6WW2TKQGJ6JDIMQ2FCC7V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V/action/storage_attestation","attest_author":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V/action/author_attestation","sign_citation":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V/action/citation_signature","submit_replication":"https://pith.science/pith/3W52F6WW2TKQGJ6JDIMQ2FCC7V/action/replication_record"}},"created_at":"2026-05-18T04:28:21.062909+00:00","updated_at":"2026-05-18T04:28:21.062909+00:00"}