{"paper":{"title":"A Derivative-Free CoMirror Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Heinz H. Bauschke, Walaa M. Moursi, Warren L. Hare","submitted_at":"2012-10-23T23:20:33Z","abstract_excerpt":"We consider $\\min\\{f(x):g(x) \\le 0, ~x\\in X\\},$ where $X$ is a compact convex subset of $\\RR^m$, and $f$ and $g$ are continuous convex functions defined on an open neighbourhood of $X$. We work in the setting of derivative-free optimization, assuming that $f$ and $g$ are available through a black-box that provides only function values for a lower-$\\mathcal{C}^2$ representation of the functions. We present a derivative-free optimization variant of the $\\eps$-comirror algorithm \\cite{BBTGBT2010}. Algorithmic convergence hinges on the ability to accurately approximate subgradients of lower-$\\math"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1210.6403","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"}