Low rank matrix recovery from rank one measurements
From MaRDI portal
Publication:347516
DOI10.1016/j.acha.2015.07.007zbMath1393.94310arXiv1410.6913OpenAlexW2963583445MaRDI QIDQ347516
Richard Kueng, Holger Rauhut, Ulrich Terstiege
Publication date: 30 November 2016
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1410.6913
convex optimizationmatrix completionphase retrievalquantum state tomographylow rank matrix recoverycomplex projective designsrandom measurements
Random matrices (probabilistic aspects) (60B20) Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20) Quantum state estimation, approximate cloning (81P50)
Related Items
The numerics of phase retrieval, WARPd: A Linearly Convergent First-Order Primal-Dual Algorithm for Inverse Problems with Approximate Sharpness Conditions, Phaselift is robust to a constant fraction of arbitrary errors, A comparative study of estimation methods in quantum tomography, Low rank tensor recovery via iterative hard thresholding, On the robustness of noise-blind low-rank recovery from rank-one measurements, Almost everywhere injectivity conditions for the matrix recovery problem, The local convexity of solving systems of quadratic equations, On connections between amplitude flow and error reduction for phase retrieval and ptychography, Dynamical quantum tomography, Phase retrieval for affine groups over prime fields, Robust sensing of low-rank matrices with non-orthogonal sparse decomposition, Sparse signal reconstruction via the approximations of \(\ell_0\) quasinorm, Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval, \( \Gamma \)-conjugate weight enumerators and invariant theory, Performance bounds of the intensity-based estimators for noisy phase retrieval, Time for dithering: fast and quantized random embeddings via the restricted isometry property, Stable low-rank matrix recovery via null space properties, Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems, Phase retrieval using random cubatures and fusion frames of positive semidefinite matrices, PhaseMax: Stable guarantees from noisy sub-Gaussian measurements, Complex phase retrieval from subgaussian measurements, Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018, Low-rank matrix recovery via rank one tight frame measurements, Norm and Trace Estimation with Random Rank-one Vectors, Phase retrieval from Fourier measurements with masks, Communication-Efficient Distributed Eigenspace Estimation, Phase retrieval with PhaseLift algorithm, Tensor theta norms and low rank recovery, Jointly low-rank and bisparse recovery: Questions and partial answers, Riemannian optimization for phase retrieval from masked Fourier measurements, Efficient Matrix Sensing Using Rank-1 Gaussian Measurements, Low-Rank Matrix Estimation from Rank-One Projections by Unlifted Convex Optimization, Proof methods for robust low-rank matrix recovery, Fast state tomography with optimal error bounds, Multi-parameter estimation beyond quantum Fisher information, Statistical analysis of compressive low rank tomography with random measurements
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Improved recovery guarantees for phase retrieval from coded diffraction patterns
- A mathematical introduction to compressive sensing
- Distinguishing multi-partite states by local measurements
- Sparse recovery under weak moment assumptions
- User-friendly tail bounds for sums of random matrices
- Painless reconstruction from magnitudes of frame coefficients
- Distinguishability of quantum states under restricted families of measurements with an application to quantum data hiding
- Averaging sets: A generalization of mean values and spherical designs
- Characterization of the subdifferential of some matrix norms
- Construction of spherical \(t\)-designs
- Spherical codes and designs
- Chebyshev-type quadrature on multidimensional domains
- Randomizing quantum states: constructions and applications
- The convex geometry of linear inverse problems
- Spherical 7-designs in \(2^n\)-dimensional Euclidean space
- Matrix recipes for hard thresholding methods
- A partial derandomization of phaselift using spherical designs
- On signal reconstruction without phase
- Phase retrieval from coded diffraction patterns
- Exact matrix completion via convex optimization
- Learning without Concentration
- Convex Recovery of a Structured Signal from Independent Random Linear Measurements
- PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
- Phase Retrieval with Polarization
- Proximal Splitting Methods in Signal Processing
- Exact and Stable Covariance Estimation From Quadratic Sampling via Convex Programming
- Incoherence-Optimal Matrix Completion
- Blind Deconvolution Using Convex Programming
- Quantum Computation and Quantum Information
- Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
- Tight informationally complete quantum measurements
- Numerical Cubature Using Error-Correcting Codes
- Bounding the Smallest Singular Value of a Random Matrix Without Concentration
- Evenly distributed unitaries: On the structure of unitary designs
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Large deviation bounds for k -designs
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- The Fourth Moment Method
- Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators
- Generating a statet-design by diagonal quantum circuits
- Living on the edge: phase transitions in convex programs with random data
- Quasi-linear Compressed Sensing
- 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
- ADMiRA: Atomic Decomposition for Minimum Rank Approximation
- Matrix Completion From a Few Entries
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- Phase Retrieval via Matrix Completion
- Compressed sensing
- The invariants of the Clifford groups
