Now showing items 21-30 of 39
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 ...
Error Forgetting of Bregman Iteration
This short article analyzes an interesting property of the Bregman iterative procedure for minimizing a convex piece-wise linear function J(x) subject to linear constraints Ax=b. The procedure obtains its solution by solving ...
Necessary and Sufficient Conditions of Solution Uniqueness in l1 Minimizationms
This paper shows that the solutions to various convex l1 minimization problems are unique if and only if a common set of conditions are satisfied. This result applies broadly to the basis pursuit model, basis pursuit ...
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 ...
A Matlab Implementation of a Flat Norm Motivated Polygonal Edge Matching Method using a Decomposition of Boundary into Four 1-Dimensional Currents
We describe and provide code and examples for a polygonal edge matching method.
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 ...
Oil Spill Sensor using Multispectral Infrared Imaging via L1 Minimization
Early detection of oil spill events is the key to environmental protection and disaster management. Current technology lacks the sensitivity and specificity in detecting the early onset of a small-scale oil spill event. ...
On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers
The formulation min f(x)+g(y) subject to Ax+By=b arises in many application areas such as signal processing, imaging and image processing, statistics, and machine learning either naturally or after variable splitting. In ...
Group Sparse Optimization by Alternating Direction Method
This paper proposes efficient algorithms for group sparse optimization with mixed L21-regularization, which arises from the reconstruction of group sparse signals in compressive sensing, and the group Lasso problem in ...