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In this lecture we are going to consider a concrete example how to calculate the PageRank parameters for websites in a given network. The network can be represented with a G(V,E) graph where V denotes the websites (nodes) and there are the E links pointing from one website to another. PageRank algorithm is an iterative approach (we can use matrix operations as well).

What’s crucial is that we can solve the problem with linear algebra and matrix operations. This is why operations like calculating the eigenvalues, eigenvectors or singular value decomposition (SVD) are extremely crucial in real world applications.

This video is just one of the many online lectures for ‘Numerical Methods and their Applications’ course. We’ll consider an example for Google’s PageRank algorithm. There are other videos present on YouTube so feel free to watch the next parts as well.

In the Numerical Methods course we are going to consider the most relevant numerical methods that are being used on a daily basis. We’ll implement the algorithms in Java

✘ matrix operations
✘ how to calculate the inverse of a matrix (Gauss-elimination)
✘ numerical integration
✘ solving differential equations
✘ Euler’s method and Runge-Kutta method

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