{"total":1,"items":[{"citing_arxiv_id":"2509.08256","ref_index":19,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy","primary_cat":"math.OC","submitted_at":"2025-09-10T03:25:56+00:00","verdict":"REJECT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper proposes truncated, model-gradient-generated subspaces for large-scale optimization and gives conditional decrease and convergence theorems, but the stated guarantees are not fully proven.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"unconstrained derivative-free optimization.ACM Trans. Math. Softw., 49(4), December 2023. ISSN 0098-3500. doi:10.1145/3618297. URLhttps://doi.org/10.1145/3618297. [18] Dong C. Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization.Math. Program., 45:503-528, 1989. URLhttps://api.semanticscholar. org/CorpusID:5681609. [19] Michael J. D. Powell. Restart procedures for the conjugate gradient method.Math. Program., 12(1):241-254, 1977. ISSN 1436-4646. doi:10.1007/BF01593790. URLhttps: //doi.org/10.1007/BF01593790. [20] D. C. Sorensen. Newton's method with a model trust region modification.SIAM Journal on Numerical Analysis, 19(2):409-426, 1982. doi:10.1137/0719026. URLhttps://doi."}],"limit":50,"offset":0}