Estimation of low rank density matrices: bounds in Schatten norms and other distances
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Publication:315410
DOI10.1214/16-EJS1192zbMath1403.62104arXiv1604.04600OpenAlexW2963930581MaRDI QIDQ315410
Dong Xia, Vladimir I. Koltchinskii
Publication date: 21 September 2016
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1604.04600
density matrixKullback-Leibler divergencevon Neumann entropyquantum state tomographylow rankSchatten \(p\)-normstrace regression model
Estimation in multivariate analysis (62H12) Measures of information, entropy (94A17) Quantum state estimation, approximate cloning (81P50)
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Cites Work
- Von Neumann entropy penalization and low-rank matrix estimation
- Estimation of (near) low-rank matrices with noise and high-dimensional scaling
- Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
- Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
- User-friendly tail bounds for sums of random matrices
- Matrix computations.
- A finite algorithm for finding the projection of a point onto the canonical simplex of \({\mathbb R}^ n\)
- Noisy low-rank matrix completion with general sampling distribution
- Continuity bounds on the quantum relative entropy — II
- Volume Ratio, Sparsity, and Minimaxity Under Unitarily Invariant Norms
- A Singular Value Thresholding Algorithm for Matrix Completion
- Interaction in Quantum Communication
- Stable low-rank matrix recovery via null space properties
- Sharp Oracle Inequalities in Low Rank Estimation
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Recovering Low-Rank Matrices From Few Coefficients in Any Basis
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- A remark on low rank matrix recovery and noncommutative Bernstein type inequalities
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