Struct ProximalGradient 

pub struct ProximalGradient { /* private fields */ }

Proximal Gradient Method optimizer

This optimizer handles non-smooth regularization terms using proximal operators, commonly used for L1 regularization.

Implementations

impl ProximalGradient

pub fn new( params: Vec<Arc<RwLock<Tensor>>>, lr: f32, l1_reg: Option<f32>, l2_reg: Option<f32>, prox_type: Option<ProximalOperator>, ) -> Self

Create a new ProximalGradient optimizer

pub fn lasso(params: Vec<Arc<RwLock<Tensor>>>, lr: f32, l1_reg: f32) -> Self

Create ProximalGradient for LASSO (L1 regularization)

pub fn elastic_net( params: Vec<Arc<RwLock<Tensor>>>, lr: f32, l1_reg: f32, l2_reg: f32, ) -> Self

Create ProximalGradient for Elastic Net

pub fn regularization(&self) -> (f32, f32)

Get regularization strengths

pub fn set_regularization(&mut self, l1_reg: f32, l2_reg: f32)

Set regularization strengths

Trait Implementations

impl Optimizer for ProximalGradient

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.