Now showing items 1-5 of 5
Accelerated High-Performance Compressive Sensing using the Graphics Processing Unit
This thesis demonstrates the advantages of new practical implementations of compressive sensing (CS) algorithms tailored for the graphics processing unit (CPU) using a software platform called Jacket. There exist many ...
Recovering Data with Group Sparsity by Alternating Direction Methods
Group sparsity reveals underlying sparsity patterns and contains rich structural information in data. Hence, exploiting group sparsity will facilitate more efficient techniques for recovering large and complicated data in ...
Parallel Sparse Optimization
This thesis proposes parallel and distributed algorithms for solving very largescale sparse optimization problems on computer clusters and clouds. Many modern applications problems from compressive sensing, machine learning ...
Block Coordinate Descent for Regularized Multi-convex Optimization
This thesis considers regularized block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. I review some of its interesting examples ...
Block Coordinate Update Method in Tensor Optimization
Block alternating minimization (BAM) has been popularly used since the 50's of last century. It partitions the variables into disjoint blocks and cyclically updates the blocks by minimizing the objective with respect to ...