{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EEPPK7KKSKTPKVZCDHGLWQZSMT","short_pith_number":"pith:EEPPK7KK","schema_version":"1.0","canonical_sha256":"211ef57d4a92a6f5572219ccbb433264f6d3cb69cc12f34c8393ea240035b850","source":{"kind":"arxiv","id":"1606.03815","version":3},"attestation_state":"computed","paper":{"title":"Finding a Hadamard Matrix by Simulated Annealing of Spin-Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Andriyan Bayu Suksmono","submitted_at":"2016-06-13T05:24:39Z","abstract_excerpt":"Reformulation of a combinatorial problem into optimization of a statistical-mechanics system, enables finding a better solution using heuristics derived from a physical process, such as by the SA (Simulated Annealing). In this paper, we present a Hadamard matrix (H-matrix) searching method based on the SA on an Ising model. By equivalence, an H-matrix can be converted into an SH (Seminormalized Hadamard) matrix; whose first columns are unity vector and the rest ones are vectors with equal number of -1 and +1 called SH-vectors. We define SH spin-vectors to represent the SH vectors, which play a"},"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":"1606.03815","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2016-06-13T05:24:39Z","cross_cats_sorted":[],"title_canon_sha256":"222487ec9b727ca79dc9388a55e6d3f0513bb0261b23a8aef017148e28f05912","abstract_canon_sha256":"24c0645a1eecf1048fbfa70766866c8f1647481022762754861c67a076efaf54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:41.742982Z","signature_b64":"f0C7+GVKPU7L2cVoe+Ljkq5WmSjbe8G/aBr3h3jIKrJssArT7GXvCITW9S9hZ76DS8gYLZwadHcV6S8JvhqiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"211ef57d4a92a6f5572219ccbb433264f6d3cb69cc12f34c8393ea240035b850","last_reissued_at":"2026-05-18T00:41:41.742446Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:41.742446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding a Hadamard Matrix by Simulated Annealing of Spin-Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.comp-ph","authors_text":"Andriyan Bayu Suksmono","submitted_at":"2016-06-13T05:24:39Z","abstract_excerpt":"Reformulation of a combinatorial problem into optimization of a statistical-mechanics system, enables finding a better solution using heuristics derived from a physical process, such as by the SA (Simulated Annealing). In this paper, we present a Hadamard matrix (H-matrix) searching method based on the SA on an Ising model. By equivalence, an H-matrix can be converted into an SH (Seminormalized Hadamard) matrix; whose first columns are unity vector and the rest ones are vectors with equal number of -1 and +1 called SH-vectors. We define SH spin-vectors to represent the SH vectors, which play a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03815","kind":"arxiv","version":3},"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":"1606.03815","created_at":"2026-05-18T00:41:41.742518+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.03815v3","created_at":"2026-05-18T00:41:41.742518+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03815","created_at":"2026-05-18T00:41:41.742518+00:00"},{"alias_kind":"pith_short_12","alias_value":"EEPPK7KKSKTP","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"EEPPK7KKSKTPKVZC","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"EEPPK7KK","created_at":"2026-05-18T12:30:12.583610+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/EEPPK7KKSKTPKVZCDHGLWQZSMT","json":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT.json","graph_json":"https://pith.science/api/pith-number/EEPPK7KKSKTPKVZCDHGLWQZSMT/graph.json","events_json":"https://pith.science/api/pith-number/EEPPK7KKSKTPKVZCDHGLWQZSMT/events.json","paper":"https://pith.science/paper/EEPPK7KK"},"agent_actions":{"view_html":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT","download_json":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT.json","view_paper":"https://pith.science/paper/EEPPK7KK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.03815&json=true","fetch_graph":"https://pith.science/api/pith-number/EEPPK7KKSKTPKVZCDHGLWQZSMT/graph.json","fetch_events":"https://pith.science/api/pith-number/EEPPK7KKSKTPKVZCDHGLWQZSMT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT/action/storage_attestation","attest_author":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT/action/author_attestation","sign_citation":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT/action/citation_signature","submit_replication":"https://pith.science/pith/EEPPK7KKSKTPKVZCDHGLWQZSMT/action/replication_record"}},"created_at":"2026-05-18T00:41:41.742518+00:00","updated_at":"2026-05-18T00:41:41.742518+00:00"}