{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/lbfgs","entry":"lbfgs","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":1,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":7,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":6},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2406.06714","paper":"/paper/coprocessor-actor-critic-a-model-based","title":"Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michelllepan/neural-coprocessors","path":"coprocessors/utils/optimization_methods.py","file_url":"https://github.com/michelllepan/neural-coprocessors/blob/HEAD/coprocessors/utils/optimization_methods.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5feda08e89b8c11","mcp_get_code":{"code_sha256":"c5feda08e89b8c11"}},{"arxiv_id":"2306.10989","paper":"/paper/scaling-of-class-wise-training-losses-for","title":"Scaling of Class-wise Training Losses for Post-hoc Calibration","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeungjinJung/SCTL","path":"models/optimizer.py","file_url":"https://github.com/SeungjinJung/SCTL/blob/HEAD/models/optimizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"26016a0b4c7d54b2","mcp_get_code":{"code_sha256":"26016a0b4c7d54b2"}},{"arxiv_id":"2207.03084","paper":"/paper/pre-training-helps-bayesian-optimization-too","title":"Pre-training helps Bayesian optimization too","date":"2022-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/hyperbo","path":"hyperbo/basics/lbfgs.py","file_url":"https://github.com/google-research/hyperbo/blob/HEAD/hyperbo/basics/lbfgs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b268783ba1900dfd","mcp_get_code":{"code_sha256":"b268783ba1900dfd"}},{"arxiv_id":"2109.08215","paper":"/paper/automatic-prior-selection-for-meta-bayesian","title":"Pre-trained Gaussian Processes for Bayesian Optimization","date":"2021-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"evensgn/hyperbo-mphd","path":"hyperbo/basics/lbfgs.py","file_url":"https://github.com/evensgn/hyperbo-mphd/blob/HEAD/hyperbo/basics/lbfgs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d5e0d85fb447b99f","mcp_get_code":{"code_sha256":"d5e0d85fb447b99f"}},{"arxiv_id":"2010.07916","paper":"/paper/multi-agent-trust-region-policy-optimization","title":"Multi-Agent Trust Region Policy Optimization","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hepengli/matrpo","path":"matrpo/common/lbfgs.py","file_url":"https://github.com/hepengli/matrpo/blob/HEAD/matrpo/common/lbfgs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2bf246102dee2c97","mcp_get_code":{"code_sha256":"2bf246102dee2c97"}},{"arxiv_id":"1905.10479","paper":"/paper/robust-learning-with-implicit-residual","title":"Robust learning with implicit residual networks","date":"2019-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vreshniak/ImplicitResNet","path":"implicitresnet/solvers/nonlinear.py","file_url":"https://github.com/vreshniak/ImplicitResNet/blob/HEAD/implicitresnet/solvers/nonlinear.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bec6efb67a9d390","mcp_get_code":{"code_sha256":"4bec6efb67a9d390"}},{"arxiv_id":"1802.06627","paper":"/paper/robustness-of-rotation-equivariant-networks","title":"Robustness of Rotation-Equivariant Networks to Adversarial Perturbations","date":"2018-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rakutentech/stAdv","path":"stadv/optimization.py","file_url":"https://github.com/rakutentech/stAdv/blob/HEAD/stadv/optimization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"82619ad2c7a74ece","mcp_get_code":{"code_sha256":"82619ad2c7a74ece"}}]}