Uniform RIP analysis for the $\ell_p- \omega \ell_q$ minimization

Nonparametric regression and quantile regression (62G08) Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

Stable recovery of sparse signals via $\ell_p$-minimization
On the $\ell_1$-norm invariant convex $k$-sparse decomposition of signals
Analysis of the ratio of $\ell_1$ and $\ell_2$ norms in compressed sensing
Optimal RIP bounds for sparse signals recovery via $\ell_p$ minimization
The Dantzig selector: statistical estimation when $p$ is much larger than $n$. (With discussions and rejoinder).
A necessary and sufficient condition for sparse vector recovery via $\ell_1-\ell_2$ minimization
Restricted isometry properties and nonconvex compressive sensing
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
Decoding by Linear Programming
A Proof of Conjecture on Restricted Isometry Property Constants $\delta _{tk}\ \left(0<t<\frac {4}{3}\right)$
Sparse Approximate Solutions to Linear Systems
Accelerated Schemes for the $L_1/L_2$ Minimization
Uniform RIP Conditions for Recovery of Sparse Signals by $\ell _p\,(0< p\leq 1)$ Minimization
New RIP Bounds for Recovery of Sparse Signals With Partial Support Information via Weighted ${\ell_{p}}$ -Minimization
Minimization of $\ell_{1-2}$ for Compressed Sensing
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices
New Restricted Isometry Property Analysis for $\ell_1-\ell_2$ Minimization Methods
Compressed sensing
$\boldsymbol{L_1-\beta L_q}$ Minimization for Signal and Image Recovery
Analysis of the ratio of $\ell_1$ and $\ell_2$ norms for signal recovery with partial support information

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Uniform RIP analysis for the \(\ell_p- \omega \ell_q\) minimization