{"paper":{"title":"Constrained adaptive sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Andrew K. Massimino, Deanna Needell, Mark A. Davenport, Tina Woolf","submitted_at":"2015-06-19T07:10:45Z","abstract_excerpt":"Suppose that we wish to estimate a vector $\\mathbf{x} \\in \\mathbb{C}^n$ from a small number of noisy linear measurements of the form $\\mathbf{y} = \\mathbf{A x} + \\mathbf{z}$, where $\\mathbf{z}$ represents measurement noise. When the vector $\\mathbf{x}$ is sparse, meaning that it has only $s$ nonzeros with $s \\ll n$, one can obtain a significantly more accurate estimate of $\\mathbf{x}$ by adaptively selecting the rows of $\\mathbf{A}$ based on the previous measurements provided that the signal-to-noise ratio (SNR) is sufficiently large. In this paper we consider the case where we wish to realize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.05889","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"}