{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:4PBDHFCLXGACGN7GCP42QTIYEJ","short_pith_number":"pith:4PBDHFCL","canonical_record":{"source":{"id":"1706.02730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-08T18:36:51Z","cross_cats_sorted":[],"title_canon_sha256":"754950d8319f4e7326e1426fea6dc533dff509b2fb5d73c161c34654ef85850e","abstract_canon_sha256":"7185c5f916dc0ae8213c29a329534a502905341f346f3ca775cb21b146ce958c"},"schema_version":"1.0"},"canonical_sha256":"e3c233944bb9802337e613f9a84d18226e89e19a6c1d2c784888f5872cce3410","source":{"kind":"arxiv","id":"1706.02730","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02730","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02730v1","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02730","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"4PBDHFCLXGAC","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4PBDHFCLXGACGN7G","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4PBDHFCL","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:4PBDHFCLXGACGN7GCP42QTIYEJ","target":"record","payload":{"canonical_record":{"source":{"id":"1706.02730","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-08T18:36:51Z","cross_cats_sorted":[],"title_canon_sha256":"754950d8319f4e7326e1426fea6dc533dff509b2fb5d73c161c34654ef85850e","abstract_canon_sha256":"7185c5f916dc0ae8213c29a329534a502905341f346f3ca775cb21b146ce958c"},"schema_version":"1.0"},"canonical_sha256":"e3c233944bb9802337e613f9a84d18226e89e19a6c1d2c784888f5872cce3410","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:43.268909Z","signature_b64":"PeyDGeAWm399/BY/n+RU2fOgkcvU9gQdubSiDR9QrTEwSy1tOhCW65Mu3kE7JU80N3xYYQkK2T6E1ray8+a4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3c233944bb9802337e613f9a84d18226e89e19a6c1d2c784888f5872cce3410","last_reissued_at":"2026-05-18T00:42:43.268222Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:43.268222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.02730","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JBIXp3vDQo/wqyKWH/NMsahagNFm8blLxif248vCdTw9PhXHfEBhJgITHHWOkzQgeRYi0BjtbXUq+ApDTQALAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T09:09:30.769088Z"},"content_sha256":"a70518307b4ee9f7425af88015e81c3bae80a3985734a4898840bff38960cf23","schema_version":"1.0","event_id":"sha256:a70518307b4ee9f7425af88015e81c3bae80a3985734a4898840bff38960cf23"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:4PBDHFCLXGACGN7GCP42QTIYEJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Random projections for trust region subproblems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Claudia D'Ambrosio, Ky Vu, Leo Liberti, Pierre-Louis Poirion","submitted_at":"2017-06-08T18:36:51Z","abstract_excerpt":"The trust region method is an algorithm traditionally used in the field of derivative free optimization. The method works by iteratively constructing surrogate models (often linear or quadratic functions) to approximate the true objective function inside some neighborhood of a current iterate. The neighborhood is called \"trust region in the sense that the model is trusted to be good enough inside the neighborhood. Updated points are found by solving the corresponding trust region subproblems. In this paper, we describe an application of random projections to solving trust region subproblems ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02730","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b/IL2+HwLk7mBNdmwYGoryqXysiI3pq7Q+beIH8WvYL94HIB3US8yZZi5l6yo7pzGokNNvHvHf3HU5QFNnCBAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T09:09:30.769459Z"},"content_sha256":"fab6b3a09099399b93e31038021f6bfc7a66b531bfe5634fd20eb407f7f47b50","schema_version":"1.0","event_id":"sha256:fab6b3a09099399b93e31038021f6bfc7a66b531bfe5634fd20eb407f7f47b50"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/bundle.json","state_url":"https://pith.science/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/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":"2026-05-28T09:09:30Z","links":{"resolver":"https://pith.science/pith/4PBDHFCLXGACGN7GCP42QTIYEJ","bundle":"https://pith.science/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/bundle.json","state":"https://pith.science/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4PBDHFCLXGACGN7GCP42QTIYEJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:4PBDHFCLXGACGN7GCP42QTIYEJ","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":"7185c5f916dc0ae8213c29a329534a502905341f346f3ca775cb21b146ce958c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-08T18:36:51Z","title_canon_sha256":"754950d8319f4e7326e1426fea6dc533dff509b2fb5d73c161c34654ef85850e"},"schema_version":"1.0","source":{"id":"1706.02730","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02730","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02730v1","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02730","created_at":"2026-05-18T00:42:43Z"},{"alias_kind":"pith_short_12","alias_value":"4PBDHFCLXGAC","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"4PBDHFCLXGACGN7G","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"4PBDHFCL","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:fab6b3a09099399b93e31038021f6bfc7a66b531bfe5634fd20eb407f7f47b50","target":"graph","created_at":"2026-05-18T00:42:43Z","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"},"paper":{"abstract_excerpt":"The trust region method is an algorithm traditionally used in the field of derivative free optimization. The method works by iteratively constructing surrogate models (often linear or quadratic functions) to approximate the true objective function inside some neighborhood of a current iterate. The neighborhood is called \"trust region in the sense that the model is trusted to be good enough inside the neighborhood. Updated points are found by solving the corresponding trust region subproblems. In this paper, we describe an application of random projections to solving trust region subproblems ap","authors_text":"Claudia D'Ambrosio, Ky Vu, Leo Liberti, Pierre-Louis Poirion","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-08T18:36:51Z","title":"Random projections for trust region subproblems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02730","kind":"arxiv","version":1},"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:a70518307b4ee9f7425af88015e81c3bae80a3985734a4898840bff38960cf23","target":"record","created_at":"2026-05-18T00:42:43Z","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":"7185c5f916dc0ae8213c29a329534a502905341f346f3ca775cb21b146ce958c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2017-06-08T18:36:51Z","title_canon_sha256":"754950d8319f4e7326e1426fea6dc533dff509b2fb5d73c161c34654ef85850e"},"schema_version":"1.0","source":{"id":"1706.02730","kind":"arxiv","version":1}},"canonical_sha256":"e3c233944bb9802337e613f9a84d18226e89e19a6c1d2c784888f5872cce3410","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e3c233944bb9802337e613f9a84d18226e89e19a6c1d2c784888f5872cce3410","first_computed_at":"2026-05-18T00:42:43.268222Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:43.268222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PeyDGeAWm399/BY/n+RU2fOgkcvU9gQdubSiDR9QrTEwSy1tOhCW65Mu3kE7JU80N3xYYQkK2T6E1ray8+a4Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:43.268909Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02730","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a70518307b4ee9f7425af88015e81c3bae80a3985734a4898840bff38960cf23","sha256:fab6b3a09099399b93e31038021f6bfc7a66b531bfe5634fd20eb407f7f47b50"],"state_sha256":"f755c6fd7db156434c85adf33431beaf60643e64ff19cb5d0b357cc160846703"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jQEk9ILNnhwrwsXUUcINUnO1b/rLo3z8P/ZWFmLBFx/Xv4lbwaoXtE6JyOH2aWJgojpUANAMWpI4UlEkFeCnAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T09:09:30.771502Z","bundle_sha256":"75782fe97c9ede2b209ceb4289f20e76021d31511fa1354c4f99347781f5a9ad"}}