Note on sampling without replacing from a finite collection of matrices
arXiv:1001.2738
Abstract
This technical note supplies an affirmative answer to a question raised in a recent pre-print [arXiv:0910.1879] in the context of a "matrix recovery" problem. Assume one samples m Hermitian matrices X_1, ..., X_m with replacement from a finite collection. The deviation of the sum X_1+...+X_m from its expected value in terms of the operator norm can be estimated by an "operator Chernoff-bound" due to Ahlswede and Winter. The question arose whether the bounds obtained this way continue to hold if the matrices are sampled without replacement. We remark that a positive answer is implied by a classical argument by Hoeffding. Some consequences for the matrix recovery problem are sketched.
3 pages. Answers a question raised in arXiv:0910.1879. v2: minus one typo.
References in corpus (3)
Cited by in corpus (27)
- Operational Resource Theory of Coherence
- Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
- Incoherence-Optimal Matrix Completion
- A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
- Convergence rates of sub-sampled Newton methods
- RIPless compressed sensing from anisotropic measurements
- Sub-Sampled Newton Methods II: Local Convergence Rates
- Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
- Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
- Optimal mini-batch and step sizes for SAGA
- Modified log-Sobolev inequalities, Beckner inequalities and moment estimates
- Exact tensor completion using t-SVD
- Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
- Improved matrix algorithms via the Subsampled Randomized Hadamard Transform
- Cross: Efficient Low-rank Tensor Completion
- Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion
- The Effect of Coherence on Sampling from Matrices with Orthonormal Columns, and Preconditioned Least Squares Problems
- Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization
- SPAN: A Stochastic Projected Approximate Newton Method
- Low-rank Matrix Completion in a General Non-orthogonal Basis
- kappa_SQ: A Matlab package for randomized sampling of matrices with orthonormal columns
- Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning
- Fast Convex Quadratic Optimization Solvers with Adaptive Sketching-based Preconditioners
- On the role of total variation in compressed sensing
- Structured Matrix Completion with Applications to Genomic Data Integration
- Non-PSD Matrix Sketching with Applications to Regression and Optimization
- Graph Approximation and Clustering on a Budget