Struct GradientDescent 

pub struct GradientDescent { /* private fields */ }

Gradient descent with optional momentum.

Update Rule

Without momentum: $\theta_{t+1} = \theta_t - \alpha \nabla f(\theta_t)$

With momentum: $v_{t+1} = \mu v_t + \alpha \nabla f(\theta_t)$, $\theta_{t+1} = \theta_t - v_{t+1}$

Implementations

impl GradientDescent

pub fn new(learning_rate: f64) -> Self

Create a new gradient descent optimizer.

pub fn with_momentum(self, momentum: f64) -> Self

Enable momentum.

Trait Implementations

impl Optimizer for GradientDescent

fn step( &self, params: &mut [f64], gradients: &[f64], _iteration: usize, ) -> Result<(), OptimizerError>

Perform a single optimization step.

fn name(&self) -> &str

Get the optimizer name.

fn reset(&mut self)

Reset internal state (for optimizers with momentum).

fn current_learning_rate(&self, _iteration: usize) -> f64

Get current learning rate (may be adaptive).