Now showing items 21-30 of 67
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 ...
Alternating Direction Algorithms for L1-Problems in Compressive Sensing
In this paper, we propose and study the use of alternating direction algorithms for several L1-norm minimization problems arising from sparse solution recovery in compressive sensing, including the basis pursuit problem, ...
On Numerical Solution of the Maximum Volume Ellipsoid Problem
In this paper we study practical solution methods for finding the maximum-volume ellipsoid inscribing a given full-dimensional polytope in R n defined by a finite set of affine inequalities. Our goal is to design a ...
Solving the Double Digestion Problem as a Mixed-Integer Linear Program
The double digestion problem for DNA restriction mapping is known to be NP-complete. Several approaches to the problem have been used including exhaustive search, simulated annealing, branch-and-bound. In this paper, we ...
User's Guide For YALL1: Your Algorithms for L1 Optimization
This User's Guide describes the functionality and basic usage of the Matlab package YALL1 for L1 minimization. The one-for-six algorithm used in the YALL1 solver is briefly introduced in the appendix.
A Fast TVL1-L2 Minimization Algorithm for Signal Reconstruction from Partial Fourier Data
Recent compressive sensing results show that it is possible to accurately reconstruct certain compressible signals from relatively few linear measurements via solving nonsmooth convex optimization problems. In this paper, ...
Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions
In many data-intensive applications, the use of principal component analysis (PCA) and other related techniques is ubiquitous for dimension reduction, data mining or other transformational purposes. Such transformations ...
An Alternating Direction and Projection Algorithm for Structure-enforced Matrix Factorization
Structure-enforced matrix factorization (SeMF) represents a large class of mathematical models ap- pearing in various forms of principal component analysis, sparse coding, dictionary learning and other machine learning ...
Augmented Lagrangian Alternating Direction Method for Matrix Separation Based on Low-Rank Factorization
The matrix separation problem aims to separate a low-rank matrix and a sparse matrix from their sum. This problem has recently attracted considerable research attention due to its wide range of potential applications. ...
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 ...