Tag archive
The naive way to step a simulation forward accumulates error until your orbit spirals into the sun. RK4 fixes the accuracy. But for long orbital runs RK4 is the wrong tool, and a lower-order method wins. Here is why.
Three approaches to computing derivatives, forward-mode AD, reverse-mode AD, and finite differences, each with different trade-offs for numerical computing and machine learning.
Numerical integration meets generic programming. By requiring only ordered field operations, the quadrature routines work with dual numbers, giving you differentiation under the integral for free.
Choosing step size h for finite differences: small enough for a good approximation, not so small that floating-point errors eat your lunch.
Numerical integration is one of the central tools of scientific computing. Whenever a physical,...
Week 1: Introduction to Numerical Methods in Machine Learning Overview of...
This post covers what are numerical methods, approximation errors, and an octave code for finding square root of 6 :)
Numerical method Trapezoidal Rule in F#.
Implementation of the trapezoidal rule in Scala
“It is about becoming a mathematical thinker, not a calculator” Whenever someone asked a crowd of pe...
Start I wrote the code for the Golden Search algorithm in python for one of my university...