Geo’s Notepad

I’ve recently released the Rust dogleg crate and I thought I’d document everything that’s needed to implement a least-squares Dogleg optimizer from scratch.

I recently found myself squinting at two images at work and wondering if one is actually “better” than the other.

It’s been one too many times that I’ve seen the famous skateboard, scooter, bike, motorcycle, and car image by Henrik Kniberg, which he says is about (agile) “product development in general”.

Say we have a sequence of independent Bernoulli experiments and we want to make claims about the true probability of success, given our observations.

This is a story of testability, multithreading, and the age old question of how do we send a !

This article continues the series on Variable Projection and explains how to rewrite the problem using QR decomposition, rather than the more computationally expensive singular value decomposition (SVD).

Inspired by Jack Wrenn’s post on Undroppable Types in Rust, I set out to see if it’s possible to create types that must be used exactly once.

While using compile-time builder generator crates, I realized that I had run into a niche problem that required lot more flexibility with generic structs and functions than I was getting.

In this article, I explore how to efficiently calculate the covariance matrix of the best fit parameters for global fitting problems that use the variable projection (VarPro) algorithm.

In this article, we’ll derive from scratch the well known formulas –and some not so well known ones– for nonlinear least squares fitting from a Bayesian perspective.