Data Sparse Matrix Computation - Lecture 11

Data Sparse Matrix Computation - Lecture 20

Data-sparse matrix computations Lecture 25: Low Rank + Sparse Matrix Recovery

In the previous lecture, we observed that it is possible to recover a sparse solution to Ax = b by solving a minimization problem involving the 1-norm. In this lecture, we consider a matrix A that can be written as A = L+ S, where L is a low rank matrix and S is a sparse matrix, and seek a method that recovers L and S. We remark that Lecture 26 forms a sequal to these notes and addresses the te...

Data - sparse matrix computations Lecture 2 : FFT - The Fast Fourier Transform Lecturer : Anil Damle

It is without a doubt, one crucial piece of the toolbox of numerical methods. The FFT or Fast Fourier Transform is a fast algorithm used to compute the Discrete Fourier Transform. It is one of the most common discrete transforms used today. Note: While many other algorithms used throughout this course can be coded up for use in other projects, you shouldn’t attempt to do this with the FFT. The ...

Fast Reconstruction of SAR Images with Phase Error Using Sparse Representation

In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR has gained a significant research interest. This method offers the advantage of reducing the sampling rate, bu...

New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time

Graphics Processing Units (GPUs) are massive data parallel processors. High performance comes only at the cost of identifying data parallelism in the applications while using data parallel processors like GPU. This is an easy effort for applications that have regular memory access and high computation intensity. GPUs are equally attractive for sparse matrix vector multiplications (SPMV for shor...