Struct AdaptiveTransformerOptimizer 

pub struct AdaptiveTransformerOptimizer { /* private fields */ }

Adaptive Transformer-Enhanced Optimizer

Implementations

impl AdaptiveTransformerOptimizer

pub fn new(config: LearnedOptimizationConfig) -> Self

Create new adaptive transformer optimizer

pub fn process_optimization_step<F>( &mut self, objective: &F, current_params: &ArrayView1<'_, f64>, problem: &OptimizationProblem, ) -> OptimizeResult<OptimizationStep>

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

Process optimization step with transformer

pub fn adapt_to_problem_class( &mut self, problem_class: &str, ) -> OptimizeResult<()>

Adapt transformer to specific problem characteristics

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

Fine-tune transformer on specific optimization trajectories

pub fn get_performance_metrics(&self) -> &TransformerMetrics

Get transformer performance metrics

Trait Implementations

impl Clone for AdaptiveTransformerOptimizer

fn clone(&self) -> AdaptiveTransformerOptimizer

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AdaptiveTransformerOptimizer

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

Formats the value using the given formatter.

impl LearnedOptimizer for AdaptiveTransformerOptimizer

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, initial_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