{"id":"W4403809168","doi":"10.48550/arxiv.2409.18277","title":"Global Minimization of Electronic Hamiltonian 1-Norm via Linear Programming in the Block Invariant Symmetry Shift (BLISS) Method","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Colorado Boulder; Defense Advanced Research Projects Agency; Alliance de recherche numérique du Canada; University of Toronto","keywords":"BLISS; Mathematics; Hamiltonian (control theory); Invariant (physics); Norm (philosophy); Mathematical analysis; Pure mathematics; Mathematical physics; Applied mathematics; Mathematical optimization; Computer science; Political science; Law","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001069469,0.0007449083,0.0007438344,0.0004106825,0.0003605966,0.0007061235,0.0009738086,0.0007929648,0.005215334],"category_scores_gemma":[0.001930568,0.000451903,0.0005018847,0.0004618836,0.001044047,0.001004987,0.001165126,0.001447676,0.0008610418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006842806,"about_ca_system_score_gemma":0.001044708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357343,"about_ca_topic_score_gemma":0.002324548,"domain_scores_codex":[0.9995957,0.0001790614,0.00001212099,0.00005360347,0.0001131484,0.00004626226],"domain_scores_gemma":[0.9991817,0.0005762079,0.00005042465,0.00006787144,0.00009111335,0.00003274333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009172453,0.00009311746,0.0003073247,0.0001759946,0.00003151335,0.00007914994,0.00007188159,0.8249562,0.005157146,0.120312,0.003274126,0.04544972],"study_design_scores_gemma":[0.00001112983,0.00002281692,0.00003300736,0.00000633589,0.000002632752,0.000006517908,0.0000107411,0.972908,0.0008192345,0.02556161,0.0006142088,0.000003864802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01626104,0.0001182153,0.9763931,0.0002713789,0.00002633011,0.00005120685,0.00009047027,0.0002708698,0.006517498],"genre_scores_gemma":[0.3163327,0.0002532093,0.6728699,0.0002784037,0.00005976203,0.0005147532,0.0003319086,0.0005757471,0.008783612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005215334,"threshold_uncertainty_score":0.01744705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650732158329486,"score_gpt":0.2164703472188284,"score_spread":0.1899630256355335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}