A least squares curve fitting library for Gleam. This library uses the Nx library from Elixir under the hood to perform matrix operations.
The library provides four functions for curve fitting: least_squares, gauss_newton, levenberg_marquardt and trust_region_reflective.
The least_squares function is just an alias for the levenberg_marquardt function.
Ideal for non-linear least squares problems, particularly when the initial guess is far from the solution. It combines the benefits of the Gauss-Newton method and gradient descent, making it robust and efficient for various scenarios. However, it requires careful tuning of the damping parameter to balance convergence speed and stability.
Best suited for large-scale problems or those with constraints (upper and lower bounds for each function parameter), this method ensures that each iteration stays within a predefined "trust region," preventing large, unstable steps. It is reliable and effective for challenging optimization problems but can be computationally intensive.
Efficient for problems where residuals are small and the initial guess is close to the true solution. It approximates the Hessian matrix, leading to faster convergence for well-behaved problems. However, it may struggle with highly non-linear problems or poor initial guesses, as it lacks the robustness of the Levenberg-Marquardt and trust-region reflective methods.
gleam add gleastsqimport gleam/io
import gleastsq
fn parabola(x: Float, params: List(Float)) -> Float {
let assert [a, b, c] = params
a *. x *. x +. b *. x +. c
}
pub fn main() {
let x = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
let y = [0.0, 1.0, 4.0, 9.0, 16.0, 25.0]
let initial_guess = [1.0, 1.0, 1.0]
let assert Ok(result) =
gleastsq.least_squares(x, y, parabola, initial_guess, opts: [])
io.debug(result) // [1.0, 0.0, 0.0] (within numerical error)
}Further documentation can be found at https://hexdocs.pm/gleastsq.