You mean the concept of a sparse matrix ? Well, for e.g. if you use FEM (Finite Element method) you usually end up with a huge linear system which has the nice property that more than 90% of his coefficients are zeros. A sparse matrix will store only the non-zero elements, this is a huge gain from the point of view of memory usage.
Similar considerations applies for using Finite Difference methods.
It's really useful for testing FEA algorithms/code. Tim Davis has done a ton of work developing numerical algorithms for sparse systems, his code is used by software like MATLAB and also bespoke supercomputer systems.
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And if you're just in it for the pretty pictures, here are more visualizations of graphs from the dataset by the same guy: http://yifanhu.net/GALLERY/GRAPHS/index1.html
Just curious, what purpose does this serve/in what applications is this useful?
You mean the concept of a sparse matrix ? Well, for e.g. if you use FEM (Finite Element method) you usually end up with a huge linear system which has the nice property that more than 90% of his coefficients are zeros. A sparse matrix will store only the non-zero elements, this is a huge gain from the point of view of memory usage.
Similar considerations applies for using Finite Difference methods.
They are also at the heart of the SPICE circuit simulator[1]
[1] http://en.wikipedia.org/wiki/SPICE#Introduction
Although for finite difference schemes we dont usually form the matrix but rather perform convolution/ forward stencil math.
I used it for testing Spare Matrix kernels I wrote for my Uni thesis.
However, god knows why this site is considered news worthy. I mean it's the same as it was yesterday, and the day before...
It's really useful for testing FEA algorithms/code. Tim Davis has done a ton of work developing numerical algorithms for sparse systems, his code is used by software like MATLAB and also bespoke supercomputer systems.
Thanks guys
Another set http://math.nist.gov/MatrixMarket/collections/hb.html
This is just stunning.