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...
Main Author: | Goyens, F |
---|---|
Other Authors: | Cartis, C |
Format: | Thesis |
Language: | English |
Published: |
2021
|
Subjects: |
Similar Items
-
Nonlinear matrix recovery using optimization on the Grassmann manifold
by: Goyens, F, et al.
Published: (2022) -
Control perspectives on numerical algorithms and matrix problems /
by: 444990 Bhaya, Amit, et al.
Published: (2006) -
Basis set approach in the constrained interpolation profile method /
by: Utsumi, T., et al.
Published: (2003) -
Fast iterative solvers for PDE-constrained optimization problems
by: Pearson, J
Published: (2013) -
Efficient algorithms for compressed sensing and matrix completion
by: Wei, K
Published: (2014)