Gradient descent (minimize)

Convenience wrapper for gradient_ascent with maximize=FALSE.

Usage

gradient_descent(
  objective_fn,
  params,
  lr = 0.01,
  max_iter = 1000,
  tol = 1e-06,
  grad_clip = NULL,
  verbose = 0
)

Arguments

objective_fn

Function taking list of value parameters, returns scalar

params

List of value objects (initial parameter values)

lr

Learning rate (step size), default 0.01

max_iter

Maximum iterations, default 1000

tol

Convergence tolerance on gradient norm, default 1e-6

grad_clip

Maximum gradient norm (NULL for no clipping)

verbose

Print progress every N iterations (0 for silent)