(Weighted) least squares fits

September 22, 2016 — October 4, 2022

Figure 1

A classic. Surprisingly deep.

A few non-comprehensive notes to approximating functions from data by the arbitrary-but-convenient expedient of minimising the sum of the squares of the deviances between two things; The linear algebra of least squares fits seems well-trodden and perenially classic. Used in many many problems. e.g. lasso regression, Gaussian belief propagation. Least squares problems are a kind of platonic ideal of convex optimisation.

Francis Bach is interested in least squares relaxations, which he connects to Fourier domain. See his Sums-of-squares for dummies: a view from the Fourier domain.

1 Introduction

2 Iteratively reweighted

3 Nonlinear least squares

Trust region and Levenberg-Marquardt methods in 2nd order optimisation.

4 Tools

OpenSLAM.org

5 Jaxopt

jax toolkit JAXopt includes lots of neatg Nonlinear least squares tooling.

6 KeOps

The KeOps library lets you compute reductions of large arrays whose entries are given by a mathematical formula or a neural network. It combines efficient C++ routines with an automatic differentiation engine and can be used with Python (NumPy, PyTorch), Matlab and R.

It is perfectly suited to the computation of kernel matrix-vector products, K-nearest neighbors queries, N-body interactions, point cloud convolutions and the associated gradients. Crucially, it performs well even when the corresponding kernel or distance matrices do not fit into the RAM or GPU memory. Compared with a PyTorch GPU baseline, KeOps provides a x10-x100 speed-up on a wide range of geometric applications, from kernel methods to geometric deep learning.

7 Incoming

8 References

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