Struct ComposedOptimizer 

pub struct ComposedOptimizer { /* private fields */ }

Composed optimizer that combines multiple optimizers

Implementations

impl ComposedOptimizer

pub fn new(strategy: CompositionStrategy) -> Self

pub fn add_optimizer(&mut self, name: String, optimizer: Box<dyn Optimizer>)

Add an optimizer to the composition

pub fn remove_optimizer(&mut self, name: &str) -> Option<Box<dyn Optimizer>>

Remove an optimizer from the composition

pub fn active_optimizers(&self) -> Vec<&str>

Get current active optimizers

pub fn update_metrics( &mut self, optimizer_name: &str, loss: f32, gradient_norm: f32, update_magnitude: f32, )

Update optimizer metrics

pub fn get_metrics(&self, optimizer_name: &str) -> Option<&OptimizerMetrics>

Get performance metrics for an optimizer

pub fn best_optimizer(&self) -> Option<&str>

Get the best performing optimizer based on metrics

Trait Implementations

impl Optimizer for ComposedOptimizer

fn step(&mut self) -> OptimizerResult<()>

Perform a single optimization step

fn zero_grad(&mut self)

Zero all gradients

fn get_lr(&self) -> Vec<f32>

Get the current learning rate

fn set_lr(&mut self, lr: f32)

Set one learning rate for every parameter group (broadcast).

fn add_param_group( &mut self, params: Vec<Arc<RwLock<Tensor>>>, options: HashMap<String, f32>, )

Add a parameter group

fn parameters(&self) -> Vec<Arc<RwLock<Tensor>>>

Get the parameter tensors managed by this optimizer.

fn state_dict(&self) -> OptimizerResult<OptimizerState>

Get state dict for serialization

fn load_state_dict(&mut self, state: OptimizerState) -> OptimizerResult<()>

Load state dict

fn set_lrs(&mut self, lrs: &[f32])

Set the learning rate of each parameter group individually.