Struct LBFGS 

pub struct LBFGS { /* private fields */ }

L-BFGS (Limited-memory BFGS) optimizer.

A quasi-Newton method that approximates the inverse Hessian using a limited number of past gradient differences.

Best For

• Smooth, well-conditioned objectives
• VQE with hardware-efficient ansätze
• When second-order information is beneficial

Implementations

impl LBFGS

pub fn new() -> Self

Create a new L-BFGS optimizer.

pub fn with_memory_size(self, size: usize) -> Self

Set the memory size (number of past gradients to store).

pub fn with_learning_rate(self, lr: f64) -> Self

Set the learning rate (step size multiplier).

pub fn with_convergence(self, criteria: ConvergenceCriteria) -> Self

Set convergence criteria.

pub fn minimize<F: ObjectiveFunction>( &self, objective: &F, initial_params: &[f64], ) -> Result<OptimizationResult, OptimizerError>

Minimize an objective function.

Trait Implementations

impl Default for LBFGS

fn default() -> Self

Returns the “default value” for a type.

impl Optimizer for LBFGS

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).