{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PNQAIZJ2WA5DNZQS6FCYKVRQYG","short_pith_number":"pith:PNQAIZJ2","schema_version":"1.0","canonical_sha256":"7b6004653ab03a36e612f145855630c1bab45c7d35f01f9a39e4e8e4dab79e85","source":{"kind":"arxiv","id":"2203.11875","version":1},"attestation_state":"computed","paper":{"title":"Condensed interior-point methods: porting reduced-space approaches on GPU hardware","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daniel Adrian Maldonado, Fran\\c{c}ois Pacaud, Michel Schanen, Mihai Anitescu, Sungho Shin","submitted_at":"2022-03-22T16:54:22Z","abstract_excerpt":"The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the Karush--Kuhn--Tucker (KKT) system at each iteration of the algorithm. When solving large-scale nonlinear problems, state-of-the art IPM solvers rely on efficient sparse linear solvers to solve the KKT system. Instead, we propose a novel reduced-space IPM algorithm that condenses the KKT system into a dense matrix whose size is proportional to the number of degrees of freedom in the problem. Depending on where the re"},"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":"2203.11875","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2022-03-22T16:54:22Z","cross_cats_sorted":[],"title_canon_sha256":"4dd9587e92b18e3873fb8218b539cf7f3a58eb118d6fe486efea2cf68dddbdf4","abstract_canon_sha256":"9649275eedd544c12fa1447e0a0ece7d6a2e67f1e78d4cc7f352d6d38d1ec59c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:07:33.747418Z","signature_b64":"nc6GpZ3aXFD2lzHiz9GftipuFT9SQRINfLtGbIWsBJvRfEdN5hV1fmSbw/kbkJowNQDG3+8PkDhZ4CZ5q5cMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7b6004653ab03a36e612f145855630c1bab45c7d35f01f9a39e4e8e4dab79e85","last_reissued_at":"2026-07-05T04:07:33.747028Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:07:33.747028Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Condensed interior-point methods: porting reduced-space approaches on GPU hardware","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Daniel Adrian Maldonado, Fran\\c{c}ois Pacaud, Michel Schanen, Mihai Anitescu, Sungho Shin","submitted_at":"2022-03-22T16:54:22Z","abstract_excerpt":"The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the Karush--Kuhn--Tucker (KKT) system at each iteration of the algorithm. When solving large-scale nonlinear problems, state-of-the art IPM solvers rely on efficient sparse linear solvers to solve the KKT system. Instead, we propose a novel reduced-space IPM algorithm that condenses the KKT system into a dense matrix whose size is proportional to the number of degrees of freedom in the problem. Depending on where the re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.11875","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2203.11875/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2203.11875","created_at":"2026-07-05T04:07:33.747085+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.11875v1","created_at":"2026-07-05T04:07:33.747085+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.11875","created_at":"2026-07-05T04:07:33.747085+00:00"},{"alias_kind":"pith_short_12","alias_value":"PNQAIZJ2WA5D","created_at":"2026-07-05T04:07:33.747085+00:00"},{"alias_kind":"pith_short_16","alias_value":"PNQAIZJ2WA5DNZQS","created_at":"2026-07-05T04:07:33.747085+00:00"},{"alias_kind":"pith_short_8","alias_value":"PNQAIZJ2","created_at":"2026-07-05T04:07:33.747085+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2209.13049","citing_title":"Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG","json":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG.json","graph_json":"https://pith.science/api/pith-number/PNQAIZJ2WA5DNZQS6FCYKVRQYG/graph.json","events_json":"https://pith.science/api/pith-number/PNQAIZJ2WA5DNZQS6FCYKVRQYG/events.json","paper":"https://pith.science/paper/PNQAIZJ2"},"agent_actions":{"view_html":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG","download_json":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG.json","view_paper":"https://pith.science/paper/PNQAIZJ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.11875&json=true","fetch_graph":"https://pith.science/api/pith-number/PNQAIZJ2WA5DNZQS6FCYKVRQYG/graph.json","fetch_events":"https://pith.science/api/pith-number/PNQAIZJ2WA5DNZQS6FCYKVRQYG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG/action/storage_attestation","attest_author":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG/action/author_attestation","sign_citation":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG/action/citation_signature","submit_replication":"https://pith.science/pith/PNQAIZJ2WA5DNZQS6FCYKVRQYG/action/replication_record"}},"created_at":"2026-07-05T04:07:33.747085+00:00","updated_at":"2026-07-05T04:07:33.747085+00:00"}