This is a post that on how to optimize and efficiently compute linear solvers with several systems of matrices.
Implementation of various fitting procedures concerning the chebyshev polynomials. This kind of polynomials are orthogonal to each other, this gives enough motivation use them.
Fitting a 2D function with scipy curve_fit. It can be to fit data to any function or if you have noisy data and you have a prior about the function. We explore also a little bit more with linear least squares.
Model related to optimization given a change in reference frame and simultaneously a double integral. It can be used anywhere you have to fit an element given a cover surface.
It's a small piece of code with the mathematical formulation. It's based on the mahalanobis distance and the lagrange multipliers.
This is a easy to visualize problem. Given a set of points, we look for the circle with the minimum radius such as it covers all the points. The problem is analogous with an ellipsis and it is extended to hyper dimensional spaces.