A Riemannian perspective on matrix recovery and constrained optimization
<p>Nonlinear matrix recovery is an emerging paradigm in which specific classes of high-rank matrices can be recovered from an underdetermined linear system of measurements. In particular, we consider matrices whose columns, seen as data points, belong to an algebraic variety, namely, a set def...
Auteur principal: | Goyens, F |
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Autres auteurs: | Cartis, C |
Format: | Thèse |
Langue: | English |
Publié: |
2021
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Sujets: |
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