{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:WT6SF4OEH7TVMEEMW3FGIOCGXJ","short_pith_number":"pith:WT6SF4OE","canonical_record":{"source":{"id":"2107.10254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-21T17:59:34Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"61780d4611a336f61718d0744b9c026d0a33251634bdc7bdf07c55036b12b306","abstract_canon_sha256":"21799f9f792802aacd4c7795112b17474c25ef44fda91ec21514b22257b729b8"},"schema_version":"1.0"},"canonical_sha256":"b4fd22f1c43fe756108cb6ca643846ba7c706fbf3531b56a09acc368a41e96cd","source":{"kind":"arxiv","id":"2107.10254","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10254","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10254v2","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10254","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_12","alias_value":"WT6SF4OEH7TV","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_16","alias_value":"WT6SF4OEH7TVMEEM","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_8","alias_value":"WT6SF4OE","created_at":"2026-07-05T03:00:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:WT6SF4OEH7TVMEEMW3FGIOCGXJ","target":"record","payload":{"canonical_record":{"source":{"id":"2107.10254","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-21T17:59:34Z","cross_cats_sorted":["cs.AI","math.OC"],"title_canon_sha256":"61780d4611a336f61718d0744b9c026d0a33251634bdc7bdf07c55036b12b306","abstract_canon_sha256":"21799f9f792802aacd4c7795112b17474c25ef44fda91ec21514b22257b729b8"},"schema_version":"1.0"},"canonical_sha256":"b4fd22f1c43fe756108cb6ca643846ba7c706fbf3531b56a09acc368a41e96cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:10.626620Z","signature_b64":"LfVwSZOu1qqibtzIg9dwtzYD4x235RoCJFNtSaryLVCTWQU+SOHwtyucPW+I8wbI4v710ySG3b+Z+S+kWo42CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4fd22f1c43fe756108cb6ca643846ba7c706fbf3531b56a09acc368a41e96cd","last_reissued_at":"2026-07-05T03:00:10.626180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:10.626180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.10254","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":"2026-07-05T03:00:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"37KH5OjPjsIqOQ3IFCTYyK+ONB+IGSsHin2DFao6Vz13ya4FRhnebSpFRxJ/tTsBoYLpujHnUF5gpjVkZyEbDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:05:22.717434Z"},"content_sha256":"5d4792d067d1e99eeacb7bf7633742233f91cd483d7c38883118457eaabea9eb","schema_version":"1.0","event_id":"sha256:5d4792d067d1e99eeacb7bf7633742233f91cd483d7c38883118457eaabea9eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:WT6SF4OEH7TVMEEMW3FGIOCGXJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Neural Fixed-Point Acceleration for Convex Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.OC"],"primary_cat":"cs.LG","authors_text":"Brandon Amos, Shobha Venkataraman","submitted_at":"2021-07-21T17:59:34Z","abstract_excerpt":"Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy. We present neural fixed-point acceleration which combines ideas from meta-learning and classical acceleration methods to automatically learn to accelerate fixed-point problems that are drawn from a distribution. We apply our framework to SCS, the state-of-the-art solver for convex cone programming, and design models and loss functions to overcome the challenges of learning over unrolled optimization and accel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10254","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/2107.10254/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":"2026-07-05T03:00:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lomtPBlWeFY4E5kR1XLkiUR/ZN15hwA/DD60xQpY2ui4+TWLsGGxuge3PcxVuUS5cJY7Gab5xRYilBJxdBhNAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:05:22.718351Z"},"content_sha256":"2df3ee3f01f52f2113c6e28c3de4b2067b2d7085d9680b019b4805babd9424dd","schema_version":"1.0","event_id":"sha256:2df3ee3f01f52f2113c6e28c3de4b2067b2d7085d9680b019b4805babd9424dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/bundle.json","state_url":"https://pith.science/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/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":"2026-08-13T04:05:22Z","links":{"resolver":"https://pith.science/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ","bundle":"https://pith.science/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/bundle.json","state":"https://pith.science/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WT6SF4OEH7TVMEEMW3FGIOCGXJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:WT6SF4OEH7TVMEEMW3FGIOCGXJ","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":"21799f9f792802aacd4c7795112b17474c25ef44fda91ec21514b22257b729b8","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-21T17:59:34Z","title_canon_sha256":"61780d4611a336f61718d0744b9c026d0a33251634bdc7bdf07c55036b12b306"},"schema_version":"1.0","source":{"id":"2107.10254","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10254","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10254v2","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10254","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_12","alias_value":"WT6SF4OEH7TV","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_16","alias_value":"WT6SF4OEH7TVMEEM","created_at":"2026-07-05T03:00:10Z"},{"alias_kind":"pith_short_8","alias_value":"WT6SF4OE","created_at":"2026-07-05T03:00:10Z"}],"graph_snapshots":[{"event_id":"sha256:2df3ee3f01f52f2113c6e28c3de4b2067b2d7085d9680b019b4805babd9424dd","target":"graph","created_at":"2026-07-05T03:00:10Z","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/2107.10254/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy. We present neural fixed-point acceleration which combines ideas from meta-learning and classical acceleration methods to automatically learn to accelerate fixed-point problems that are drawn from a distribution. We apply our framework to SCS, the state-of-the-art solver for convex cone programming, and design models and loss functions to overcome the challenges of learning over unrolled optimization and accel","authors_text":"Brandon Amos, Shobha Venkataraman","cross_cats":["cs.AI","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-21T17:59:34Z","title":"Neural Fixed-Point Acceleration for Convex Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10254","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":"sha256:5d4792d067d1e99eeacb7bf7633742233f91cd483d7c38883118457eaabea9eb","target":"record","created_at":"2026-07-05T03:00:10Z","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":"21799f9f792802aacd4c7795112b17474c25ef44fda91ec21514b22257b729b8","cross_cats_sorted":["cs.AI","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-07-21T17:59:34Z","title_canon_sha256":"61780d4611a336f61718d0744b9c026d0a33251634bdc7bdf07c55036b12b306"},"schema_version":"1.0","source":{"id":"2107.10254","kind":"arxiv","version":2}},"canonical_sha256":"b4fd22f1c43fe756108cb6ca643846ba7c706fbf3531b56a09acc368a41e96cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4fd22f1c43fe756108cb6ca643846ba7c706fbf3531b56a09acc368a41e96cd","first_computed_at":"2026-07-05T03:00:10.626180Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:00:10.626180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LfVwSZOu1qqibtzIg9dwtzYD4x235RoCJFNtSaryLVCTWQU+SOHwtyucPW+I8wbI4v710ySG3b+Z+S+kWo42CA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:00:10.626620Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.10254","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d4792d067d1e99eeacb7bf7633742233f91cd483d7c38883118457eaabea9eb","sha256:2df3ee3f01f52f2113c6e28c3de4b2067b2d7085d9680b019b4805babd9424dd"],"state_sha256":"a5c249c5e0725aec089a03baf2ce8bca63ab95aa3839130a154bb3d32171c2f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C8ku9NcNkWe/i03nutUB1zdRsjA4Z9kYTqY3HgPGViOGveoDQOysOrr8C85+mEV4eHV3dZG4u049ZoLmo5Q3DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:05:22.722836Z","bundle_sha256":"3f9c03d73a104a38fb19f32c0677bf8b38099fbefbeefe480a062c5759451f8b"}}