Groups
Category
Level
A random walk on a graph moves from a node to one of its neighbors chosen uniformly at random at each step.
The graph Laplacian translates a graph’s connectivity into a matrix that measures how much a function varies across edges.
A Markov chain models a system that moves between states where the next step depends only on the current state, not the past.
Low-rank approximation replaces a big matrix with one that has far fewer degrees of freedom while preserving most of its action.
Matrix norms measure the size of a matrix in different but related ways, with Frobenius treating entries like a big vector, spectral measuring the strongest stretch, and nuclear summing all singular values.
Eigendecomposition expresses a matrix as a change of basis times a diagonal scaling, revealing its natural stretching directions.