{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:VIQU7AW64ZVBLZMGXOYQ4ACCO6","short_pith_number":"pith:VIQU7AW6","canonical_record":{"source":{"id":"2510.24755","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-20T10:38:53Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"57f8655b48c5312e9badf00745c338c0b8afd3de72518606cbf3e5b8c7ffb613","abstract_canon_sha256":"87fe253574f8ef94a2a9e5e7add788aaccb280598c520eeb93d1e9775fe9f684"},"schema_version":"1.0"},"canonical_sha256":"aa214f82dee66a15e586bbb10e004277a39b3fb1f98ecf8258068e7162876a30","source":{"kind":"arxiv","id":"2510.24755","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24755","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24755v2","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24755","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_12","alias_value":"VIQU7AW64ZVB","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_16","alias_value":"VIQU7AW64ZVBLZMG","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_8","alias_value":"VIQU7AW6","created_at":"2026-07-02T01:17:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:VIQU7AW64ZVBLZMGXOYQ4ACCO6","target":"record","payload":{"canonical_record":{"source":{"id":"2510.24755","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-20T10:38:53Z","cross_cats_sorted":["quant-ph"],"title_canon_sha256":"57f8655b48c5312e9badf00745c338c0b8afd3de72518606cbf3e5b8c7ffb613","abstract_canon_sha256":"87fe253574f8ef94a2a9e5e7add788aaccb280598c520eeb93d1e9775fe9f684"},"schema_version":"1.0"},"canonical_sha256":"aa214f82dee66a15e586bbb10e004277a39b3fb1f98ecf8258068e7162876a30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:25.162694Z","signature_b64":"q2MUCqEwod7Hubuzni0H97FzAFJ7FXJI5hn1KVz+IkV79CeuP4JgDghgxUjkrw+X45QVJvFGCqz8RX7uG6ygBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa214f82dee66a15e586bbb10e004277a39b3fb1f98ecf8258068e7162876a30","last_reissued_at":"2026-07-02T01:17:25.162227Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:25.162227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.24755","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T01:17:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X3qWgbIuJOkQ6n5KqVWrgP7giYNtawdrlhrZeVwEnwKgPAILZXoSseU0Chd4I6O3EmyBvKcuAyhjHuNakeItCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:55:40.567526Z"},"content_sha256":"c83a144b19561aa47cb3d369e97cd0c89d1e9726f8fe2fd1ef398cb92b893e3e","schema_version":"1.0","event_id":"sha256:c83a144b19561aa47cb3d369e97cd0c89d1e9726f8fe2fd1ef398cb92b893e3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:VIQU7AW64ZVBLZMGXOYQ4ACCO6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["quant-ph"],"primary_cat":"math.OC","authors_text":"Baptiste Chevalier, Masahiro Takeoka, Shimpei Yamaguchi, Wojciech Roga","submitted_at":"2025-10-20T10:38:53Z","abstract_excerpt":"In this paper, we present a Monte-Carlo Compressive Optimization algorithm, a new method to tackle combinatorial optimization problems, including Black-Box or complicated objective functions. The method relies on random queries to the objective function in order to estimate generalized moments. Next, a greedy algorithm from compressive sensing is repurposed to find the global optimum when not overfitting to the samples. We provide numerical results giving evidence that our method is competitive by comparing it with dual annealing. Moreover, we give theoretical justification for the success of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.24755","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.24755/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-02T01:17:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EW1kv2gcrPW0Jy/DR9zQW7lzBCVkJ6rwSbdqJXoXYe4MkhPUc4qbLXZ/+7P8JHj9aqJfO4mnlQAEo9+8Y+lrCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T13:55:40.569489Z"},"content_sha256":"840f686a860f9e0db6bb0b00b5235afc58ca28d0f344221307f4ab4c9baace7f","schema_version":"1.0","event_id":"sha256:840f686a860f9e0db6bb0b00b5235afc58ca28d0f344221307f4ab4c9baace7f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/bundle.json","state_url":"https://pith.science/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/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":"2026-08-14T13:55:40Z","links":{"resolver":"https://pith.science/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6","bundle":"https://pith.science/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/bundle.json","state":"https://pith.science/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VIQU7AW64ZVBLZMGXOYQ4ACCO6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:VIQU7AW64ZVBLZMGXOYQ4ACCO6","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":"87fe253574f8ef94a2a9e5e7add788aaccb280598c520eeb93d1e9775fe9f684","cross_cats_sorted":["quant-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-20T10:38:53Z","title_canon_sha256":"57f8655b48c5312e9badf00745c338c0b8afd3de72518606cbf3e5b8c7ffb613"},"schema_version":"1.0","source":{"id":"2510.24755","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.24755","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"arxiv_version","alias_value":"2510.24755v2","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.24755","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_12","alias_value":"VIQU7AW64ZVB","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_16","alias_value":"VIQU7AW64ZVBLZMG","created_at":"2026-07-02T01:17:25Z"},{"alias_kind":"pith_short_8","alias_value":"VIQU7AW6","created_at":"2026-07-02T01:17:25Z"}],"graph_snapshots":[{"event_id":"sha256:840f686a860f9e0db6bb0b00b5235afc58ca28d0f344221307f4ab4c9baace7f","target":"graph","created_at":"2026-07-02T01:17:25Z","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/2510.24755/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we present a Monte-Carlo Compressive Optimization algorithm, a new method to tackle combinatorial optimization problems, including Black-Box or complicated objective functions. The method relies on random queries to the objective function in order to estimate generalized moments. Next, a greedy algorithm from compressive sensing is repurposed to find the global optimum when not overfitting to the samples. We provide numerical results giving evidence that our method is competitive by comparing it with dual annealing. Moreover, we give theoretical justification for the success of ","authors_text":"Baptiste Chevalier, Masahiro Takeoka, Shimpei Yamaguchi, Wojciech Roga","cross_cats":["quant-ph"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-20T10:38:53Z","title":"A Compressive Sensing Inspired Monte-Carlo Method for Combinatorial Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.24755","kind":"arxiv","version":2},"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:c83a144b19561aa47cb3d369e97cd0c89d1e9726f8fe2fd1ef398cb92b893e3e","target":"record","created_at":"2026-07-02T01:17:25Z","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":"87fe253574f8ef94a2a9e5e7add788aaccb280598c520eeb93d1e9775fe9f684","cross_cats_sorted":["quant-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2025-10-20T10:38:53Z","title_canon_sha256":"57f8655b48c5312e9badf00745c338c0b8afd3de72518606cbf3e5b8c7ffb613"},"schema_version":"1.0","source":{"id":"2510.24755","kind":"arxiv","version":2}},"canonical_sha256":"aa214f82dee66a15e586bbb10e004277a39b3fb1f98ecf8258068e7162876a30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa214f82dee66a15e586bbb10e004277a39b3fb1f98ecf8258068e7162876a30","first_computed_at":"2026-07-02T01:17:25.162227Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T01:17:25.162227Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"q2MUCqEwod7Hubuzni0H97FzAFJ7FXJI5hn1KVz+IkV79CeuP4JgDghgxUjkrw+X45QVJvFGCqz8RX7uG6ygBA==","signature_status":"signed_v1","signed_at":"2026-07-02T01:17:25.162694Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.24755","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c83a144b19561aa47cb3d369e97cd0c89d1e9726f8fe2fd1ef398cb92b893e3e","sha256:840f686a860f9e0db6bb0b00b5235afc58ca28d0f344221307f4ab4c9baace7f"],"state_sha256":"c2fbca7111fccf9776264bb6481a98b726ef32af5dc270461681cae5bc97e263"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9oilLBS52mV2HCxUYdeYniaBM0Y0R3SWBxMgREBf5HVqZaYxN3bOll2sMWap93QICP4/zlaoUPU0GYY5Nrf4BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T13:55:40.577962Z","bundle_sha256":"f1247cb300afe588093081c49b729bed4673e19adaea3086079ae67588bac2de"}}