Characterization of ℓ1 minimizer in one-bit compressed sensing
From MaRDI portal
Publication:5236754
DOI10.1142/S0219530519500131zbMath1458.94074OpenAlexW2963993140MaRDI QIDQ5236754
Publication date: 10 October 2019
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530519500131
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) General harmonic expansions, frames (42C15) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10)
Related Items (1)
Uses Software
Cites Work
- 1-bit compressive sensing: reformulation and RRSP-based sign recovery theory
- A mathematical introduction to compressive sensing
- The bounds of restricted isometry constants for low rank matrices recovery
- Compressed sensing with coherent and redundant dictionaries
- Iterative hard thresholding for compressed sensing
- Noisy 1-bit compressive sensing: models and algorithms
- The restricted isometry property and its implications for compressed sensing
- A simple proof of the restricted isometry property for random matrices
- Sharp RIP bound for sparse signal and low-rank matrix recovery
- One-Bit Compressed Sensing by Linear Programming
- Certifying the Restricted Isometry Property is Hard
- Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
- Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Decoding by Linear Programming
- Sparse representations in unions of bases
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Iteratively reweighted least squares minimization for sparse recovery
- Atomic Decomposition by Basis Pursuit
- Sparse Optimization Theory and Methods
- Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements
- Robust 1-bit Compressive Sensing Using Adaptive Outlier Pursuit
- RSP-Based Analysis for Sparsest and Least $\ell_1$-Norm Solutions to Underdetermined Linear Systems
- One-Bit Compressed Sensing by Greedy Algorithms
- Compressed sensing
This page was built for publication: Characterization of ℓ1 minimizer in one-bit compressed sensing