{"paper":{"title":"On the solution of stochastic optimization and variational problems in imperfect information regimes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Hao Jiang, Uday V. Shanbhag","submitted_at":"2014-02-06T19:05:11Z","abstract_excerpt":"We consider the solution of a stochastic convex optimization problem $\\mathbb{E}[f(x;\\theta^*,\\xi)]$ over a closed and convex set $X$ in a regime where $\\theta^*$ is unavailable and $\\xi$ is a suitably defined random variable. Instead, $\\theta^*$ may be obtained through the solution of a learning problem that requires minimizing a metric $\\mathbb{E}[g(\\theta;\\eta)]$ in $\\theta$ over a closed and convex set $\\Theta$. Traditional approaches have been either sequential or direct variational approaches. In the case of the former, this entails the following steps: (i) a solution to the learning pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.1457","kind":"arxiv","version":2},"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"}