Now showing items 1-6 of 6
Solving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithm
The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requires computing singular value ...
Decentralized Jointly Sparse Optimization by Reweighted Lq Minimization
A set of vectors (or signals) are jointly sparse if their nonzero entries are commonly supported on a small subset of locations. Consider a network of agents which collaborative recover a set of joint sparse vectors. This ...
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
This paper introduces a novel algorithm for the nonnegative matrix factorization and completion problem, which aims to nd nonnegative matrices X and Y from a subset of entries of a nonnegative matrix M so that XY approximates ...
Dynamic Compressive Spectrum Sensing for Cognitive Radio Networks
In the recently proposed collaborative compressive sensing, the cognitive radios (CRs) sense the occupied spectrum channels by measuring linear combinations of channel powers, instead of sweeping a set of channels sequentially. ...
A Feasible Method for Optimization with Orthogonality Constraints
Minimization with orthogonality constraints (e.g., X'X = I) and/or spherical constraints (e.g., ||x||_2 = 1) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, ...
Trust, But Verify: Fast and Accurate Signal Recovery from 1-bit Compressive Measurements
The recently emerged compressive sensing (CS) framework aims to acquire signals at reduced sample rates compared to the classical Shannon-Nyquist rate. To date, the CS theory has assumed primarily real-valued measurements; ...