Struct NeuralAdaptiveOptimizer 

pub struct NeuralAdaptiveOptimizer { /* private fields */ }

Neural Adaptive Optimizer with dynamic strategy learning

Implementations

impl NeuralAdaptiveOptimizer

pub fn new(config: LearnedOptimizationConfig) -> Self

Create new neural adaptive optimizer

pub fn adaptive_optimization_step<F>( &mut self, objective: &F, current_params: &ArrayView1<'_, f64>, step_number: usize, ) -> OptimizeResult<AdaptiveOptimizationStep>

where F: Fn(&ArrayView1<'_, f64>) -> f64,

Perform adaptive optimization step

pub fn train_networks( &mut self, training_data: &[OptimizationTrajectory], ) -> OptimizeResult<()>

Train the neural networks on optimization data

pub fn get_adaptive_stats(&self) -> &AdaptiveOptimizationStats

Get adaptive optimization statistics

Trait Implementations

impl Clone for NeuralAdaptiveOptimizer

fn clone(&self) -> NeuralAdaptiveOptimizer

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for NeuralAdaptiveOptimizer

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl LearnedOptimizer for NeuralAdaptiveOptimizer

fn meta_train(&mut self, training_tasks: &[TrainingTask]) -> OptimizeResult<()>

Meta-train the optimizer on a distribution of problems

fn adapt_to_problem( &mut self, _problem: &OptimizationProblem, _params: &ArrayView1<'_, f64>, ) -> OptimizeResult<()>

Adapt to a new problem with few-shot learning

fn optimize<F>( &mut self, objective: F, initial_params: &ArrayView1<'_, f64>, ) -> OptimizeResult<OptimizeResults<f64>>

where F: Fn(&ArrayView1<'_, f64>) -> f64,

Optimize a function using learned knowledge

fn get_state(&self) -> &MetaOptimizerState

Get current meta-optimizer state

fn reset(&mut self)

Reset the optimizer