Linear Systems and Sparse Matrices with Numpy and Scipy

A lot of problems in numerical methods boil down to solving systems of linear equations. In many cases, the matrices involved are very large. Fortunately, often one finds most of the entries of these are zero. These are referred to as sparse matrices. There are many advantages to being able to use sparse systems. Memory storage is greatly reduced as well as the ability to use algorithms custom built to perform fasted than with dense matrices. I’ll show Numpy’s basic ability to solve systems of linear equations, the I’ll revisit the stock monte carlo code from a previous video and treat it as a system of linear equations and use sparse matrix capabilities that are built in to Scipy to generate a simulated run of stock prices.

[atchutram]

I appreciate your work. I think you should print your intermediate results (or a portion of large matrices) and help viewers understanding it well.

[somteezle]

Thanks. I think you should create a playlist for numerical methods

[radyoalmikyel]

i used this to find same result x = scipy.sparse.linalg.spsolve(A_sparse, y)

[AJ-etvf]

Thank you very much for this. Very helpful

[minma]

I want to like your videos more than once. Unfortunately I can't.

[monzz]

Good vid! But what about solving a system of non-linear equations that have large sparse matrices?

[hardikkharel]

I don't have juypter notebook so whaaat?