Pages that link to "Item:Q1748256"
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The following pages link to Stable recovery of low-dimensional cones in Hilbert spaces: one RIP to rule them all (Q1748256):
Displaying 17 items.
- Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling (Q1979911) (← links)
- Structured iterative hard thresholding with on- and off-grid applications (Q2074952) (← links)
- Hierarchical isometry properties of hierarchical measurements (Q2118397) (← links)
- Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class (Q2415398) (← links)
- Generalized notions of sparsity and restricted isometry property. II: Applications (Q2657397) (← links)
- Statistical learning guarantees for compressive clustering and compressive mixture modeling (Q2664825) (← links)
- Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing (Q2696571) (← links)
- BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING (Q2971048) (← links)
- Structure and Optimisation in Computational Harmonic Analysis: On Key Aspects in Sparse Regularisation (Q3384130) (← links)
- Convergence bounds for empirical nonlinear least-squares (Q5034774) (← links)
- WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions (Q5043741) (← links)
- Sparse recovery from extreme eigenvalues deviation inequalities (Q5228345) (← links)
- On the Absence of Uniform Recovery in Many Real-World Applications of Compressed Sensing and the Restricted Isometry Property and Nullspace Property in Levels (Q5266378) (← links)
- Recovering Wavelet Coefficients from Binary Samples Using Fast Transforms (Q5864089) (← links)
- Optimal approximation of infinite-dimensional holomorphic functions (Q6151536) (← links)
- Sample complexity bounds for the local convergence of least squares approximation (Q6649924) (← links)
- A theory of optimal convex regularization for low-dimensional recovery (Q6663356) (← links)
