Struct SPSA 

pub struct SPSA { /* private fields */ }

SPSA (Simultaneous Perturbation Stochastic Approximation) optimizer.

A gradient-free method that estimates gradients using random perturbations. Particularly effective for noisy objective functions (e.g., hardware execution).

Mathematical Description

$$g_k \approx \frac{f(\theta + c_k \Delta_k) - f(\theta - c_k \Delta_k)}{2 c_k} \Delta_k^{-1}$$

where $\Delta_k$ is a random perturbation vector with ±1 entries.

Implementations

impl SPSA

pub fn new() -> Self

Create a new SPSA optimizer.

pub fn with_a(self, a: f64) -> Self

Set the a parameter (step size numerator).

pub fn with_c(self, c: f64) -> Self

Set the c parameter (perturbation size).

pub fn with_a_const(self, a_const: f64) -> Self

Set the stability constant A.

pub fn estimate_gradient<F: ObjectiveFunction>( &self, objective: &F, params: &[f64], iteration: usize, ) -> Vec<f64>

Estimate gradient using simultaneous perturbation.

Trait Implementations

impl Default for SPSA

fn default() -> Self

Returns the “default value” for a type.

impl Optimizer for SPSA

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).