scirs2_optimize/learned_optimizers/

neural_adaptive_optimizer.rs

//! Neural Adaptive Optimizer
//!
//! Implementation of neural networks that learn adaptive optimization strategies
//! and can dynamically adjust their behavior based on optimization progress.

use super::{
    ActivationType, LearnedOptimizationConfig, LearnedOptimizer, MetaOptimizerState,
    OptimizationProblem, TrainingTask,
};
use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use ndarray::{Array1, Array2, ArrayView1};
use rand::Rng;
use statrs::statistics::Statistics;
use std::collections::{HashMap, VecDeque};

/// Neural Adaptive Optimizer with dynamic strategy learning
#[derive(Debug, Clone)]
pub struct NeuralAdaptiveOptimizer {
    /// Configuration
    config: LearnedOptimizationConfig,
    /// Primary optimization network
    optimization_network: OptimizationNetwork,
    /// Adaptation controller
    adaptation_controller: AdaptationController,
    /// Performance predictor
    performance_predictor: PerformancePredictor,
    /// Meta-optimizer state
    meta_state: MetaOptimizerState,
    /// Adaptive statistics
    adaptive_stats: AdaptiveOptimizationStats,
    /// Memory-efficient computation cache
    computation_cache: ComputationCache,
}

/// Memory-efficient computation cache for reusing allocations
#[derive(Debug, Clone)]
pub struct ComputationCache {
    /// Reusable gradient buffer
    gradient_buffer: Array1<f64>,
    /// Reusable feature buffer
    feature_buffer: Array1<f64>,
    /// Reusable parameter buffer
    param_buffer: Array1<f64>,
    /// Network output buffer
    network_output_buffer: Array1<f64>,
    /// Temporary computation buffer
    temp_buffer: Array1<f64>,
    /// Maximum buffer size to prevent unbounded growth
    max_buffer_size: usize,
}

/// Memory-efficient bounded history collection
#[derive(Debug, Clone)]
pub struct BoundedHistory<T> {
    /// Internal storage
    pub(crate) data: VecDeque<T>,
    /// Maximum capacity
    max_capacity: usize,
}

impl<T> BoundedHistory<T> {
    /// Create new bounded history with specified capacity
    pub fn new(capacity: usize) -> Self {
        Self {
            data: VecDeque::with_capacity(capacity),
            max_capacity: capacity,
        }
    }

    /// Add item, removing oldest if at capacity
    pub fn push(&mut self, item: T) {
        if self.data.len() >= self.max_capacity {
            self.data.pop_front();
        }
        self.data.push_back(item);
    }

    /// Get the most recent item
    pub fn back(&self) -> Option<&T> {
        self.data.back()
    }

    /// Clear all items
    pub fn clear(&mut self) {
        self.data.clear();
    }

    /// Get length
    pub fn len(&self) -> usize {
        self.data.len()
    }

    /// Check if empty
    pub fn is_empty(&self) -> bool {
        self.data.is_empty()
    }
}

impl ComputationCache {
    /// Create new computation cache
    pub fn new(max_size: usize) -> Self {
        Self {
            gradient_buffer: Array1::zeros(max_size),
            feature_buffer: Array1::zeros(max_size),
            param_buffer: Array1::zeros(max_size),
            network_output_buffer: Array1::zeros(max_size),
            temp_buffer: Array1::zeros(max_size),
            max_buffer_size: max_size,
        }
    }

    /// Get reusable gradient buffer
    pub fn get_gradient_buffer(&mut self, size: usize) -> &mut Array1<f64> {
        if self.gradient_buffer.len() < size {
            self.gradient_buffer = Array1::zeros(size);
        }
        &mut self.gradient_buffer
    }

    /// Get reusable feature buffer
    pub fn get_feature_buffer(&mut self, size: usize) -> &mut Array1<f64> {
        if self.feature_buffer.len() < size {
            self.feature_buffer = Array1::zeros(size);
        }
        &mut self.feature_buffer
    }

    /// Get reusable parameter buffer
    pub fn get_param_buffer(&mut self, size: usize) -> &mut Array1<f64> {
        if self.param_buffer.len() < size {
            self.param_buffer = Array1::zeros(size);
        }
        &mut self.param_buffer
    }

    /// Get network output buffer
    pub fn get_network_output_buffer(&mut self, size: usize) -> &mut Array1<f64> {
        if self.network_output_buffer.len() < size {
            self.network_output_buffer = Array1::zeros(size);
        }
        &mut self.network_output_buffer
    }

    /// Get temporary buffer
    pub fn get_temp_buffer(&mut self, size: usize) -> &mut Array1<f64> {
        if self.temp_buffer.len() < size {
            self.temp_buffer = Array1::zeros(size);
        }
        &mut self.temp_buffer
    }

    /// Get both gradient and param buffers simultaneously to avoid borrowing conflicts
    pub fn get_gradient_and_param_buffers(
        &mut self,
        gradient_size: usize,
        param_size: usize,
    ) -> (&mut Array1<f64>, &mut Array1<f64>) {
        if self.gradient_buffer.len() < gradient_size {
            self.gradient_buffer = Array1::zeros(gradient_size);
        }
        if self.param_buffer.len() < param_size {
            self.param_buffer = Array1::zeros(param_size);
        }
        (&mut self.gradient_buffer, &mut self.param_buffer)
    }

    /// Resize buffer if needed (up to max size)
    fn resize_buffer(&mut self, buffer: &mut Array1<f64>, requested_size: usize) {
        let size = requested_size.min(self.max_buffer_size);
        if buffer.len() != size {
            *buffer = Array1::zeros(size);
        } else {
            buffer.fill(0.0);
        }
    }
}

/// Neural network for optimization strategy
#[derive(Debug, Clone)]
pub struct OptimizationNetwork {
    /// Input layer for problem state
    input_layer: NeuralLayer,
    /// Hidden layers for strategy computation
    hidden_layers: Vec<NeuralLayer>,
    /// Output layer for optimization actions
    output_layer: NeuralLayer,
    /// Recurrent connections for memory
    recurrent_connections: RecurrentConnections,
    /// Network architecture
    architecture: NetworkArchitecture,
}

/// Neural layer
#[derive(Debug, Clone)]
pub struct NeuralLayer {
    /// Weights
    weights: Array2<f64>,
    /// Biases
    biases: Array1<f64>,
    /// Activation function
    activation: ActivationType,
    /// Layer size
    size: usize,
    /// Dropout rate
    dropout_rate: f64,
    /// Layer normalization
    layer_norm: Option<LayerNormalization>,
}

/// Layer normalization
#[derive(Debug, Clone)]
pub struct LayerNormalization {
    /// Scale parameter
    gamma: Array1<f64>,
    /// Shift parameter
    beta: Array1<f64>,
    /// Running mean
    running_mean: Array1<f64>,
    /// Running variance
    running_var: Array1<f64>,
    /// Momentum for running stats
    momentum: f64,
    /// Epsilon for numerical stability
    epsilon: f64,
}

/// Recurrent connections for memory
#[derive(Debug, Clone)]
pub struct RecurrentConnections {
    /// Hidden state
    hidden_state: Array1<f64>,
    /// Cell state (for LSTM-like behavior)
    cell_state: Array1<f64>,
    /// Recurrent weights
    recurrent_weights: Array2<f64>,
    /// Input gate weights
    input_gate_weights: Array2<f64>,
    /// Forget gate weights
    forget_gate_weights: Array2<f64>,
    /// Output gate weights
    output_gate_weights: Array2<f64>,
}

/// Network architecture specification
#[derive(Debug, Clone)]
pub struct NetworkArchitecture {
    /// Input size
    input_size: usize,
    /// Hidden sizes
    hidden_sizes: Vec<usize>,
    /// Output size
    output_size: usize,
    /// Activation functions per layer
    activations: Vec<ActivationType>,
    /// Use recurrent connections
    use_recurrent: bool,
    /// Use attention mechanisms
    use_attention: bool,
}

/// Adaptation controller for dynamic strategy adjustment
#[derive(Debug, Clone)]
pub struct AdaptationController {
    /// Strategy selector network
    strategy_selector: StrategySelector,
    /// Adaptation rate controller
    adaptation_rate_controller: AdaptationRateController,
    /// Progress monitor
    progress_monitor: ProgressMonitor,
    /// Strategy history (bounded to prevent memory growth)
    strategy_history: BoundedHistory<OptimizationStrategy>,
}

/// Strategy selector
#[derive(Debug, Clone)]
pub struct StrategySelector {
    /// Selection network
    selection_network: Array2<f64>,
    /// Strategy embeddings
    strategy_embeddings: Array2<f64>,
    /// Current strategy weights
    strategy_weights: Array1<f64>,
    /// Available strategies
    available_strategies: Vec<OptimizationStrategy>,
}

/// Optimization strategy
#[derive(Debug, Clone)]
pub struct OptimizationStrategy {
    /// Strategy identifier
    id: String,
    /// Strategy parameters
    parameters: Array1<f64>,
    /// Expected performance
    expected_performance: f64,
    /// Computational cost
    computational_cost: f64,
    /// Robustness score
    robustness: f64,
}

/// Adaptation rate controller
#[derive(Debug, Clone)]
pub struct AdaptationRateController {
    /// Controller network
    controller_network: Array2<f64>,
    /// Current adaptation rate
    current_rate: f64,
    /// Rate history (bounded)
    rate_history: BoundedHistory<f64>,
    /// Performance correlation
    performance_correlation: f64,
}

/// Progress monitor
#[derive(Debug, Clone)]
pub struct ProgressMonitor {
    /// Progress indicators
    progress_indicators: Vec<ProgressIndicator>,
    /// Monitoring network
    monitoring_network: Array2<f64>,
    /// Alert thresholds
    alert_thresholds: HashMap<String, f64>,
    /// Current progress state
    current_state: ProgressState,
}

/// Progress indicator
#[derive(Debug, Clone)]
pub struct ProgressIndicator {
    /// Indicator name
    name: String,
    /// Current value
    value: f64,
    /// Historical values (bounded)
    history: BoundedHistory<f64>,
    /// Trend direction
    trend: f64,
    /// Importance weight
    importance: f64,
}

/// Progress state
#[derive(Debug, Clone)]
pub enum ProgressState {
    Improving,
    Stagnating,
    Deteriorating,
    Converged,
    Diverging,
}

/// Performance predictor
#[derive(Debug, Clone)]
pub struct PerformancePredictor {
    /// Prediction network
    prediction_network: Array2<f64>,
    /// Feature extractor
    feature_extractor: FeatureExtractor,
    /// Prediction horizon
    prediction_horizon: usize,
    /// Prediction accuracy
    prediction_accuracy: f64,
    /// Confidence estimator
    confidence_estimator: ConfidenceEstimator,
}

/// Feature extractor for performance prediction
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Extraction layers
    extraction_layers: Vec<Array2<f64>>,
    /// Feature dimension
    feature_dim: usize,
    /// Temporal features
    temporal_features: TemporalFeatures,
}

/// Temporal features
#[derive(Debug, Clone)]
pub struct TemporalFeatures {
    /// Time series embeddings
    time_embeddings: Array2<f64>,
    /// Trend analysis
    trend_analyzer: TrendAnalyzer,
    /// Seasonality detector
    seasonality_detector: SeasonalityDetector,
}

/// Trend analyzer
#[derive(Debug, Clone)]
pub struct TrendAnalyzer {
    /// Trend coefficients
    trend_coefficients: Array1<f64>,
    /// Window size for trend analysis
    window_size: usize,
    /// Trend strength
    trend_strength: f64,
}

/// Seasonality detector
#[derive(Debug, Clone)]
pub struct SeasonalityDetector {
    /// Seasonal patterns
    seasonal_patterns: Array2<f64>,
    /// Pattern strength
    pattern_strength: Array1<f64>,
    /// Detection threshold
    detection_threshold: f64,
}

/// Confidence estimator
#[derive(Debug, Clone)]
pub struct ConfidenceEstimator {
    /// Confidence network
    confidence_network: Array2<f64>,
    /// Uncertainty quantification
    uncertainty_quantifier: UncertaintyQuantifier,
    /// Calibration parameters
    calibration_params: Array1<f64>,
}

/// Uncertainty quantification
#[derive(Debug, Clone)]
pub struct UncertaintyQuantifier {
    /// Epistemic uncertainty
    epistemic_uncertainty: f64,
    /// Aleatoric uncertainty
    aleatoric_uncertainty: f64,
    /// Uncertainty estimation method
    method: UncertaintyMethod,
}

/// Uncertainty estimation methods
#[derive(Debug, Clone)]
pub enum UncertaintyMethod {
    Dropout,
    Ensemble,
    Bayesian,
    Evidential,
}

/// Adaptive optimization statistics
#[derive(Debug, Clone)]
pub struct AdaptiveOptimizationStats {
    /// Number of strategy switches
    strategy_switches: usize,
    /// Average adaptation rate
    avg_adaptation_rate: f64,
    /// Prediction accuracy
    prediction_accuracy: f64,
    /// Computational efficiency
    computational_efficiency: f64,
    /// Robustness score
    robustness_score: f64,
}

impl NeuralAdaptiveOptimizer {
    /// Create new neural adaptive optimizer
    pub fn new(config: LearnedOptimizationConfig) -> Self {
        let architecture = NetworkArchitecture {
            input_size: config.max_parameters.min(100),
            hidden_sizes: vec![config.hidden_size, config.hidden_size / 2],
            output_size: 32, // Number of optimization actions
            activations: vec![
                ActivationType::GELU,
                ActivationType::GELU,
                ActivationType::Tanh,
            ],
            use_recurrent: true,
            use_attention: config.use_transformer,
        };

        let optimization_network = OptimizationNetwork::new(architecture);
        let adaptation_controller = AdaptationController::new(config.hidden_size);
        let performance_predictor = PerformancePredictor::new(config.hidden_size);
        let hidden_size = config.hidden_size;
        let max_buffer_size = config.max_parameters.max(1000); // Reasonable upper bound

        Self {
            config,
            optimization_network,
            adaptation_controller,
            performance_predictor,
            meta_state: MetaOptimizerState {
                meta_params: Array1::zeros(hidden_size),
                network_weights: Array2::zeros((hidden_size, hidden_size)),
                performance_history: Vec::new(),
                adaptation_stats: super::AdaptationStatistics::default(),
                episode: 0,
            },
            adaptive_stats: AdaptiveOptimizationStats::default(),
            computation_cache: ComputationCache::new(max_buffer_size),
        }
    }

    /// Perform adaptive optimization step
    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,
    {
        // Extract current state features
        let state_features = self.extract_state_features(objective, current_params, step_number)?;

        // Forward pass through optimization network
        let network_output = self.optimization_network.forward(&state_features.view())?;

        // Predict performance
        let performance_prediction = self.performance_predictor.predict(&state_features)?;

        // Select optimization strategy
        let strategy = self
            .adaptation_controller
            .select_strategy(&network_output, &performance_prediction)?;

        // Monitor progress and adapt if necessary
        self.adaptation_controller
            .monitor_and_adapt(&performance_prediction)?;

        // Create optimization step
        let step = AdaptiveOptimizationStep {
            strategy: strategy.clone(),
            predicted_performance: performance_prediction,
            confidence: self
                .performance_predictor
                .confidence_estimator
                .estimate_confidence(&state_features)?,
            adaptation_signal: self.adaptation_controller.get_adaptation_signal(),
            network_output: network_output.clone(),
        };

        // Update statistics
        self.update_adaptive_stats(&step)?;

        Ok(step)
    }

    /// Extract state features for neural network
    fn extract_state_features<F>(
        &mut self,
        objective: &F,
        current_params: &ArrayView1<f64>,
        step_number: usize,
    ) -> OptimizeResult<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut features = Array1::zeros(self.optimization_network.architecture.input_size);
        let feature_idx = 0;

        // Parameter features
        let param_features = self.extract_parameter_features(current_params);
        self.copy_features(&mut features, &param_features, feature_idx);

        // Objective features
        let obj_features = self.extract_objective_features(objective, current_params)?;
        self.copy_features(
            &mut features,
            &obj_features,
            feature_idx + param_features.len(),
        );

        // Temporal features
        let temporal_features = self.extract_temporal_features(step_number);
        self.copy_features(
            &mut features,
            &temporal_features,
            feature_idx + param_features.len() + obj_features.len(),
        );

        Ok(features)
    }

    /// Extract parameter-based features
    fn extract_parameter_features(&self, params: &ArrayView1<f64>) -> Array1<f64> {
        let mut features = Array1::zeros(20);

        if !params.is_empty() {
            features[0] = params.view().mean().tanh();
            features[1] = params.view().variance().sqrt().tanh();
            features[2] = params.fold(-f64::INFINITY, |a, &b| a.max(b)).tanh();
            features[3] = params.fold(f64::INFINITY, |a, &b| a.min(b)).tanh();
            features[4] = (params.len() as f64).ln().tanh();

            // Statistical moments
            let mean = features[0];
            let std = features[1];
            if std > 1e-8 {
                let skewness = params
                    .iter()
                    .map(|&x| ((x - mean) / std).powi(3))
                    .sum::<f64>()
                    / params.len() as f64;
                features[5] = skewness.tanh();

                let kurtosis = params
                    .iter()
                    .map(|&x| ((x - mean) / std).powi(4))
                    .sum::<f64>()
                    / params.len() as f64
                    - 3.0;
                features[6] = kurtosis.tanh();
            }

            // Norms
            features[7] =
                (params.iter().map(|&x| x.abs()).sum::<f64>() / params.len() as f64).tanh(); // L1
            features[8] = (params.iter().map(|&x| x * x).sum::<f64>()).sqrt().tanh(); // L2

            // Sparsity
            let zero_count = params.iter().filter(|&&x| x.abs() < 1e-8).count();
            features[9] = (zero_count as f64 / params.len() as f64).tanh();
        }

        features
    }

    /// Extract objective-based features
    fn extract_objective_features<F>(
        &mut self,
        objective: &F,
        params: &ArrayView1<f64>,
    ) -> OptimizeResult<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut features = Array1::zeros(15);

        let f0 = objective(params);
        features[0] = f0.abs().ln().tanh();

        // Gradient features using cached buffers
        let h = 1e-6;
        let gradient_sample_size = params.len().min(10); // Limit for efficiency
        let (gradient_buffer, param_buffer) = self
            .computation_cache
            .get_gradient_and_param_buffers(gradient_sample_size, params.len());

        // Copy parameters to buffer
        for (i, &val) in params.iter().enumerate() {
            if i < param_buffer.len() {
                param_buffer[i] = val;
            }
        }

        // Compute gradient components efficiently
        for i in 0..gradient_sample_size {
            let original_val = param_buffer[i];
            param_buffer[i] = original_val + h;
            let f_plus = objective(&param_buffer.view());
            param_buffer[i] = original_val; // Restore

            gradient_buffer[i] = (f_plus - f0) / h;
        }

        let gradient_norm = (gradient_buffer
            .iter()
            .take(gradient_sample_size)
            .map(|&g| g * g)
            .sum::<f64>())
        .sqrt();
        features[1] = gradient_norm.ln().tanh();

        if gradient_sample_size > 0 {
            let grad_mean = gradient_buffer
                .iter()
                .take(gradient_sample_size)
                .sum::<f64>()
                / gradient_sample_size as f64;
            let grad_var = gradient_buffer
                .iter()
                .take(gradient_sample_size)
                .map(|&g| (g - grad_mean).powi(2))
                .sum::<f64>()
                / gradient_sample_size as f64;

            features[2] = grad_mean.tanh();
            features[3] = grad_var.sqrt().tanh();
        }

        // Curvature approximation using cached buffer
        if params.len() > 1 {
            // Reuse param_buffer for mixed partial computation
            param_buffer[0] += h;
            param_buffer[1] += h;
            let f_plus_plus = objective(&param_buffer.view());

            param_buffer[1] -= 2.0 * h; // Now it's +h, -h
            let f_plus_minus = objective(&param_buffer.view());

            // Restore original values
            param_buffer[0] -= h;
            param_buffer[1] += h;

            let mixed_partial = (f_plus_plus - f_plus_minus) / (2.0 * h);
            features[4] = mixed_partial.tanh();
        }

        Ok(features)
    }

    /// Extract temporal features
    fn extract_temporal_features(&self, step_number: usize) -> Array1<f64> {
        let mut features = Array1::zeros(10);

        features[0] = (step_number as f64).ln().tanh();
        features[1] = (step_number as f64 / 1000.0).tanh(); // Normalized step

        // Progress from performance history
        if self.meta_state.performance_history.len() > 1 {
            let recent_performance = &self.meta_state.performance_history
                [self.meta_state.performance_history.len().saturating_sub(5)..];

            if recent_performance.len() > 1 {
                let trend = (recent_performance[recent_performance.len() - 1]
                    - recent_performance[0])
                    / recent_performance.len() as f64;
                features[2] = trend.tanh();

                let variance = recent_performance.iter().map(|&x| x * x).sum::<f64>()
                    / recent_performance.len() as f64
                    - (recent_performance.iter().sum::<f64>() / recent_performance.len() as f64)
                        .powi(2);
                features[3] = variance.sqrt().tanh();
            }
        }

        features
    }

    /// Copy features to target array
    fn copy_features(&self, target: &mut Array1<f64>, source: &Array1<f64>, start_idx: usize) {
        for (i, &value) in source.iter().enumerate() {
            if start_idx + i < target.len() {
                target[start_idx + i] = value;
            }
        }
    }

    /// Update adaptive optimization statistics
    fn update_adaptive_stats(&mut self, step: &AdaptiveOptimizationStep) -> OptimizeResult<()> {
        // Update strategy switch count
        if let Some(last_strategy) = self.adaptation_controller.strategy_history.back() {
            if last_strategy.id != step.strategy.id {
                self.adaptive_stats.strategy_switches += 1;
            }
        }

        // Update adaptation rate
        self.adaptive_stats.avg_adaptation_rate =
            0.9 * self.adaptive_stats.avg_adaptation_rate + 0.1 * step.adaptation_signal;

        // Update prediction accuracy (simplified)
        self.adaptive_stats.prediction_accuracy =
            0.95 * self.adaptive_stats.prediction_accuracy + 0.05 * step.confidence;

        Ok(())
    }

    /// Train the neural networks on optimization data
    pub fn train_networks(
        &mut self,
        training_data: &[OptimizationTrajectory],
    ) -> OptimizeResult<()> {
        for trajectory in training_data {
            // Train optimization network
            self.train_optimization_network(trajectory)?;

            // Train performance predictor
            self.train_performance_predictor(trajectory)?;

            // Update adaptation controller
            self.update_adaptation_controller(trajectory)?;
        }

        Ok(())
    }

    /// Train the optimization network
    fn train_optimization_network(
        &mut self,
        trajectory: &OptimizationTrajectory,
    ) -> OptimizeResult<()> {
        // Simplified training using trajectory data
        let learning_rate = self.config.meta_learning_rate;

        for (i, state) in trajectory.states.iter().enumerate() {
            if i + 1 < trajectory.actions.len() {
                let target_action = &trajectory.actions[i + 1];
                let predicted_action = self.optimization_network.forward(&state.view())?;

                // Compute loss (simplified MSE)
                let mut loss_gradient = Array1::zeros(predicted_action.len());
                for j in 0..loss_gradient.len().min(target_action.len()) {
                    loss_gradient[j] = 2.0 * (predicted_action[j] - target_action[j]);
                }

                // Backpropagate (simplified)
                self.optimization_network
                    .backward(&loss_gradient, learning_rate)?;
            }
        }

        Ok(())
    }

    /// Train the performance predictor
    fn train_performance_predictor(
        &mut self,
        trajectory: &OptimizationTrajectory,
    ) -> OptimizeResult<()> {
        // Simplified training for performance prediction
        let learning_rate = self.config.meta_learning_rate * 0.5;

        for (i, state) in trajectory.states.iter().enumerate() {
            if i + self.performance_predictor.prediction_horizon
                < trajectory.performance_values.len()
            {
                let target_performance = trajectory.performance_values
                    [i + self.performance_predictor.prediction_horizon];
                let predicted_performance = self.performance_predictor.predict(state)?;

                let error = target_performance - predicted_performance;

                // Update prediction network (simplified)
                for row in self.performance_predictor.prediction_network.rows_mut() {
                    for weight in row {
                        *weight += learning_rate * error * rand::rng().random::<f64>() * 0.01;
                    }
                }
            }
        }

        Ok(())
    }

    /// Update adaptation controller
    fn update_adaptation_controller(
        &mut self,
        trajectory: &OptimizationTrajectory,
    ) -> OptimizeResult<()> {
        // Analyze trajectory for adaptation patterns
        if trajectory.performance_values.len() > 2 {
            let performance_trend =
                trajectory.performance_values.last().unwrap() - trajectory.performance_values[0];

            // Update strategy selector based on performance
            if performance_trend > 0.0 {
                // Good performance, reinforce current strategy
                self.adaptation_controller.reinforce_current_strategy(0.1)?;
            } else {
                // Poor performance, encourage exploration
                self.adaptation_controller.encourage_exploration(0.1)?;
            }
        }

        Ok(())
    }

    /// Get adaptive optimization statistics
    pub fn get_adaptive_stats(&self) -> &AdaptiveOptimizationStats {
        &self.adaptive_stats
    }
}

/// Optimization trajectory for training
#[derive(Debug, Clone)]
pub struct OptimizationTrajectory {
    /// State sequence
    pub states: Vec<Array1<f64>>,
    /// Action sequence
    pub actions: Vec<Array1<f64>>,
    /// Performance values
    pub performance_values: Vec<f64>,
    /// Rewards
    pub rewards: Vec<f64>,
}

/// Adaptive optimization step result
#[derive(Debug, Clone)]
pub struct AdaptiveOptimizationStep {
    /// Selected strategy
    pub strategy: OptimizationStrategy,
    /// Predicted performance
    pub predicted_performance: f64,
    /// Confidence in prediction
    pub confidence: f64,
    /// Adaptation signal strength
    pub adaptation_signal: f64,
    /// Raw network output
    pub network_output: Array1<f64>,
}

impl OptimizationNetwork {
    /// Create new optimization network
    pub fn new(architecture: NetworkArchitecture) -> Self {
        let mut hidden_layers = Vec::new();

        // Create hidden layers
        let mut prev_size = architecture.input_size;
        for (i, &hidden_size) in architecture.hidden_sizes.iter().enumerate() {
            let activation = architecture
                .activations
                .get(i)
                .copied()
                .unwrap_or(ActivationType::ReLU);

            hidden_layers.push(NeuralLayer::new(prev_size, hidden_size, activation));
            prev_size = hidden_size;
        }

        // Create input and output layers
        let input_activation = architecture
            .activations
            .first()
            .copied()
            .unwrap_or(ActivationType::ReLU);
        let output_activation = architecture
            .activations
            .last()
            .copied()
            .unwrap_or(ActivationType::Tanh);

        let input_layer = NeuralLayer::new(
            architecture.input_size,
            architecture.input_size,
            input_activation,
        );
        let output_layer = NeuralLayer::new(prev_size, architecture.output_size, output_activation);

        let recurrent_connections = if architecture.use_recurrent {
            RecurrentConnections::new(prev_size)
        } else {
            RecurrentConnections::empty()
        };

        Self {
            input_layer,
            hidden_layers,
            output_layer,
            recurrent_connections,
            architecture,
        }
    }

    /// Forward pass through network
    pub fn forward(&mut self, input: &ArrayView1<f64>) -> OptimizeResult<Array1<f64>> {
        // Input layer
        let mut current = self.input_layer.forward(input)?;

        // Hidden layers
        for layer in &mut self.hidden_layers {
            current = layer.forward(&current.view())?;
        }

        // Apply recurrent connections if enabled
        if self.architecture.use_recurrent {
            current = self.recurrent_connections.apply(&current)?;
        }

        // Output layer
        let output = self.output_layer.forward(&current.view())?;

        Ok(output)
    }

    /// Backward pass (simplified)
    pub fn backward(&mut self, gradient: &Array1<f64>, learning_rate: f64) -> OptimizeResult<()> {
        // Simplified backpropagation
        // In practice, this would implement proper gradient computation

        // Update output layer
        for i in 0..self.output_layer.weights.nrows() {
            for j in 0..self.output_layer.weights.ncols() {
                let grad = if i < gradient.len() { gradient[i] } else { 0.0 };
                self.output_layer.weights[[i, j]] -= learning_rate * grad * 0.01;
            }
        }

        // Update hidden layers (simplified)
        for layer in &mut self.hidden_layers {
            for i in 0..layer.weights.nrows() {
                for j in 0..layer.weights.ncols() {
                    layer.weights[[i, j]] -= learning_rate * rand::rng().random::<f64>() * 0.001;
                }
            }
        }

        Ok(())
    }
}

impl NeuralLayer {
    /// Create new neural layer
    pub fn new(input_size: usize, output_size: usize, activation: ActivationType) -> Self {
        let xavier_scale = (2.0 / (input_size + output_size) as f64).sqrt();

        Self {
            weights: Array2::from_shape_fn((output_size, input_size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 2.0 * xavier_scale
            }),
            biases: Array1::zeros(output_size),
            size: output_size,
            dropout_rate: 0.1,
            layer_norm: Some(LayerNormalization::new(output_size)),
            activation: ActivationType::ReLU,
        }
    }

    /// Forward pass through layer
    pub fn forward(&mut self, input: &ArrayView1<f64>) -> OptimizeResult<Array1<f64>> {
        let mut output = Array1::zeros(self.size);

        // Linear transformation
        for i in 0..self.size {
            for j in 0..input.len().min(self.weights.ncols()) {
                output[i] += self.weights[[i, j]] * input[j];
            }
            output[i] += self.biases[i];
        }

        // Layer normalization
        if let Some(ref mut layer_norm) = self.layer_norm {
            output = layer_norm.normalize(&output)?;
        }

        // Activation
        output.mapv_inplace(|x| self.activation.apply(x));

        // Dropout (simplified - just scaling)
        if self.dropout_rate > 0.0 {
            output *= 1.0 - self.dropout_rate;
        }

        Ok(output)
    }
}

impl LayerNormalization {
    /// Create new layer normalization
    pub fn new(size: usize) -> Self {
        Self {
            gamma: Array1::ones(size),
            beta: Array1::zeros(size),
            running_mean: Array1::zeros(size),
            running_var: Array1::ones(size),
            momentum: 0.9,
            epsilon: 1e-6,
        }
    }

    /// Normalize input
    pub fn normalize(&mut self, input: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        let mean = input.mean().unwrap_or(0.0);
        let var = input.variance();
        let std = (var + self.epsilon).sqrt();

        // Update running statistics
        self.running_mean = &self.running_mean * self.momentum
            + &(Array1::from_elem(input.len(), mean) * (1.0 - self.momentum));
        self.running_var = &self.running_var * self.momentum
            + &(Array1::from_elem(input.len(), var) * (1.0 - self.momentum));

        // Normalize
        let mut normalized = Array1::zeros(input.len());
        for i in 0..input.len().min(self.gamma.len()) {
            normalized[i] = self.gamma[i] * (input[i] - mean) / std + self.beta[i];
        }

        Ok(normalized)
    }
}

impl RecurrentConnections {
    /// Create new recurrent connections
    pub fn new(size: usize) -> Self {
        Self {
            hidden_state: Array1::zeros(size),
            cell_state: Array1::zeros(size),
            recurrent_weights: Array2::from_shape_fn((size, size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            input_gate_weights: Array2::from_shape_fn((size, size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            forget_gate_weights: Array2::from_shape_fn((size, size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            output_gate_weights: Array2::from_shape_fn((size, size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
        }
    }

    /// Create empty recurrent connections
    pub fn empty() -> Self {
        Self {
            hidden_state: Array1::zeros(0),
            cell_state: Array1::zeros(0),
            recurrent_weights: Array2::zeros((0, 0)),
            input_gate_weights: Array2::zeros((0, 0)),
            forget_gate_weights: Array2::zeros((0, 0)),
            output_gate_weights: Array2::zeros((0, 0)),
        }
    }

    /// Apply recurrent connections (LSTM-like)
    pub fn apply(&mut self, input: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        if self.hidden_state.is_empty() {
            return Ok(input.clone());
        }

        let size = self.hidden_state.len().min(input.len());
        let mut output = Array1::zeros(size);

        // Simplified LSTM computation
        for i in 0..size {
            // Input gate
            let mut input_gate = 0.0;
            for j in 0..size {
                input_gate += self.input_gate_weights[[i, j]] * input[j];
            }
            input_gate = (input_gate).tanh();

            // Forget gate
            let mut forget_gate = 0.0;
            for j in 0..size {
                forget_gate += self.forget_gate_weights[[i, j]] * self.hidden_state[j];
            }
            forget_gate = (forget_gate).tanh();

            // Update cell state
            self.cell_state[i] = forget_gate * self.cell_state[i] + input_gate * input[i];

            // Output gate
            let mut output_gate = 0.0;
            for j in 0..size {
                output_gate += self.output_gate_weights[[i, j]] * input[j];
            }
            output_gate = (output_gate).tanh();

            // Update hidden state and output
            self.hidden_state[i] = output_gate * self.cell_state[i].tanh();
            output[i] = self.hidden_state[i];
        }

        Ok(output)
    }
}

impl AdaptationController {
    /// Create new adaptation controller
    pub fn new(hidden_size: usize) -> Self {
        Self {
            strategy_selector: StrategySelector::new(hidden_size),
            adaptation_rate_controller: AdaptationRateController::new(),
            progress_monitor: ProgressMonitor::new(),
            strategy_history: BoundedHistory::new(100),
        }
    }

    /// Select optimization strategy
    pub fn select_strategy(
        &mut self,
        network_output: &Array1<f64>,
        performance_prediction: &f64,
    ) -> OptimizeResult<OptimizationStrategy> {
        let strategy = self
            .strategy_selector
            .select(network_output, *performance_prediction)?;
        self.strategy_history.push(strategy.clone());

        Ok(strategy)
    }

    /// Monitor progress and adapt
    pub fn monitor_and_adapt(&mut self, performance_prediction: &f64) -> OptimizeResult<()> {
        self.progress_monitor.update(*performance_prediction)?;

        match self.progress_monitor.current_state {
            ProgressState::Stagnating | ProgressState::Deteriorating => {
                self.adaptation_rate_controller.increase_rate()?;
            }
            ProgressState::Improving => {
                self.adaptation_rate_controller.maintain_rate()?;
            }
            _ => {}
        }

        Ok(())
    }

    /// Get adaptation signal
    pub fn get_adaptation_signal(&self) -> f64 {
        self.adaptation_rate_controller.current_rate
    }

    /// Reinforce current strategy
    pub fn reinforce_current_strategy(&mut self, strength: f64) -> OptimizeResult<()> {
        self.strategy_selector.reinforce_current(strength)
    }

    /// Encourage exploration
    pub fn encourage_exploration(&mut self, strength: f64) -> OptimizeResult<()> {
        self.strategy_selector.encourage_exploration(strength)
    }
}

impl StrategySelector {
    /// Create new strategy selector
    pub fn new(hidden_size: usize) -> Self {
        let num_strategies = 5;

        Self {
            selection_network: Array2::from_shape_fn((num_strategies, hidden_size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            strategy_embeddings: Array2::from_shape_fn((num_strategies, hidden_size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            strategy_weights: Array1::from_elem(num_strategies, 1.0 / num_strategies as f64),
            available_strategies: vec![
                OptimizationStrategy::gradient_descent(),
                OptimizationStrategy::momentum(),
                OptimizationStrategy::adaptive(),
                OptimizationStrategy::quasi_newton(),
                OptimizationStrategy::trust_region(),
            ],
        }
    }

    /// Select strategy based on network output
    pub fn select(
        &self,
        network_output: &Array1<f64>,
        performance_prediction: f64,
    ) -> OptimizeResult<OptimizationStrategy> {
        let mut strategy_scores = Array1::zeros(self.available_strategies.len());

        // Compute strategy scores
        for i in 0..strategy_scores.len() {
            for j in 0..network_output.len().min(self.selection_network.ncols()) {
                strategy_scores[i] += self.selection_network[[i, j]] * network_output[j];
            }

            // Add performance prediction influence
            strategy_scores[i] += performance_prediction * 0.1;

            // Add current weight
            strategy_scores[i] += self.strategy_weights[i];
        }

        // Apply softmax to get probabilities
        let max_score = strategy_scores.fold(-f64::INFINITY, |a, &b| a.max(b));
        strategy_scores.mapv_inplace(|x| (x - max_score).exp());
        let sum_scores = strategy_scores.sum();
        if sum_scores > 0.0 {
            strategy_scores /= sum_scores;
        }

        // Select strategy (argmax for deterministic, or sample for stochastic)
        let selected_idx = strategy_scores
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(i, _)| i)
            .unwrap_or(0);

        Ok(self.available_strategies[selected_idx].clone())
    }

    /// Reinforce current strategy
    pub fn reinforce_current(&mut self, strength: f64) -> OptimizeResult<()> {
        // Increase weight of current best strategy
        if let Some((best_idx, _)) = self
            .strategy_weights
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
        {
            self.strategy_weights[best_idx] += strength;
        }

        // Renormalize
        let sum = self.strategy_weights.sum();
        if sum > 0.0 {
            self.strategy_weights /= sum;
        }

        Ok(())
    }

    /// Encourage exploration
    pub fn encourage_exploration(&mut self, strength: f64) -> OptimizeResult<()> {
        // Add uniform noise to encourage exploration
        for weight in &mut self.strategy_weights {
            *weight += strength * rand::rng().random::<f64>();
        }

        // Renormalize
        let sum = self.strategy_weights.sum();
        if sum > 0.0 {
            self.strategy_weights /= sum;
        }

        Ok(())
    }
}

impl OptimizationStrategy {
    /// Create gradient descent strategy
    pub fn gradient_descent() -> Self {
        Self {
            id: "gradient_descent".to_string(),
            parameters: Array1::from(vec![0.01, 0.0, 0.0]), // [learning_rate, momentum, adaptivity]
            expected_performance: 0.7,
            computational_cost: 0.3,
            robustness: 0.8,
        }
    }

    /// Create momentum strategy
    pub fn momentum() -> Self {
        Self {
            id: "momentum".to_string(),
            parameters: Array1::from(vec![0.01, 0.9, 0.0]),
            expected_performance: 0.8,
            computational_cost: 0.4,
            robustness: 0.7,
        }
    }

    /// Create adaptive strategy
    pub fn adaptive() -> Self {
        Self {
            id: "adaptive".to_string(),
            parameters: Array1::from(vec![0.001, 0.0, 0.9]),
            expected_performance: 0.85,
            computational_cost: 0.6,
            robustness: 0.9,
        }
    }

    /// Create quasi-Newton strategy
    pub fn quasi_newton() -> Self {
        Self {
            id: "quasi_newton".to_string(),
            parameters: Array1::from(vec![0.1, 0.0, 0.5]),
            expected_performance: 0.9,
            computational_cost: 0.8,
            robustness: 0.6,
        }
    }

    /// Create trust region strategy
    pub fn trust_region() -> Self {
        Self {
            id: "trust_region".to_string(),
            parameters: Array1::from(vec![0.1, 0.0, 0.7]),
            expected_performance: 0.95,
            computational_cost: 0.9,
            robustness: 0.95,
        }
    }
}

impl AdaptationRateController {
    /// Create new adaptation rate controller
    pub fn new() -> Self {
        Self {
            controller_network: Array2::from_shape_fn((1, 10), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            current_rate: 0.1,
            rate_history: BoundedHistory::new(100),
            performance_correlation: 0.0,
        }
    }

    /// Increase adaptation rate
    pub fn increase_rate(&mut self) -> OptimizeResult<()> {
        self.current_rate = (self.current_rate * 1.2).min(1.0);
        self.rate_history.push(self.current_rate);

        Ok(())
    }

    /// Maintain current rate
    pub fn maintain_rate(&mut self) -> OptimizeResult<()> {
        self.rate_history.push(self.current_rate);

        Ok(())
    }
}

impl ProgressMonitor {
    /// Create new progress monitor
    pub fn new() -> Self {
        Self {
            progress_indicators: vec![
                ProgressIndicator::new("objective_improvement".to_string()),
                ProgressIndicator::new("gradient_norm".to_string()),
                ProgressIndicator::new("step_size".to_string()),
            ],
            monitoring_network: Array2::from_shape_fn((4, 10), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            alert_thresholds: HashMap::new(),
            current_state: ProgressState::Improving,
        }
    }

    /// Update progress monitoring
    pub fn update(&mut self, performance_value: f64) -> OptimizeResult<()> {
        // Update progress indicators
        for indicator in &mut self.progress_indicators {
            indicator.update(performance_value)?;
        }

        // Determine current state
        self.current_state = self.determine_progress_state()?;

        Ok(())
    }

    /// Determine progress state
    fn determine_progress_state(&self) -> OptimizeResult<ProgressState> {
        let mut improvement_count = 0;
        let mut stagnation_count = 0;

        for indicator in &self.progress_indicators {
            if indicator.trend > 0.1 {
                improvement_count += 1;
            } else if indicator.trend.abs() < 0.01 {
                stagnation_count += 1;
            }
        }

        if improvement_count >= 2 {
            Ok(ProgressState::Improving)
        } else if stagnation_count >= 2 {
            Ok(ProgressState::Stagnating)
        } else {
            Ok(ProgressState::Deteriorating)
        }
    }
}

impl ProgressIndicator {
    /// Create new progress indicator
    pub fn new(name: String) -> Self {
        Self {
            name,
            value: 0.0,
            history: BoundedHistory::new(50),
            trend: 0.0,
            importance: 1.0,
        }
    }

    /// Update indicator
    pub fn update(&mut self, new_value: f64) -> OptimizeResult<()> {
        self.value = new_value;
        self.history.push(new_value);

        // Compute trend using bounded history
        if self.history.len() > 2 {
            // Access the underlying data to compute trend
            let first = self.history.data.front().copied().unwrap_or(new_value);
            let last = self.history.data.back().copied().unwrap_or(new_value);
            self.trend = (last - first) / self.history.len() as f64;
        }

        Ok(())
    }
}

impl PerformancePredictor {
    /// Create new performance predictor
    pub fn new(hidden_size: usize) -> Self {
        Self {
            prediction_network: Array2::from_shape_fn((1, hidden_size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            feature_extractor: FeatureExtractor::new(hidden_size),
            prediction_horizon: 5,
            prediction_accuracy: 0.5,
            confidence_estimator: ConfidenceEstimator::new(hidden_size),
        }
    }

    /// Predict performance
    pub fn predict(&self, state_features: &Array1<f64>) -> OptimizeResult<f64> {
        // Extract features for prediction
        let prediction_features = self.feature_extractor.extract(state_features)?;

        // Forward pass through prediction network
        let mut prediction = 0.0;
        for j in 0..prediction_features
            .len()
            .min(self.prediction_network.ncols())
        {
            prediction += self.prediction_network[[0, j]] * prediction_features[j];
        }

        Ok(prediction.tanh()) // Normalize to [-1, 1]
    }
}

impl FeatureExtractor {
    /// Create new feature extractor
    pub fn new(feature_dim: usize) -> Self {
        Self {
            extraction_layers: vec![Array2::from_shape_fn((feature_dim, feature_dim), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            })],
            feature_dim,
            temporal_features: TemporalFeatures::new(feature_dim),
        }
    }

    /// Extract features for prediction
    pub fn extract(&self, input: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        let mut features = input.clone();

        // Apply extraction layers
        for layer in &self.extraction_layers {
            let output_dim = layer.nrows().min(features.len());
            let input_dim = layer.ncols().min(features.len());
            let mut new_features = Array1::zeros(output_dim);

            for i in 0..output_dim {
                for j in 0..input_dim {
                    new_features[i] += layer[[i, j]] * features[j];
                }
            }
            features = new_features;
        }

        Ok(features)
    }
}

impl TemporalFeatures {
    /// Create new temporal features
    pub fn new(dim: usize) -> Self {
        Self {
            time_embeddings: Array2::from_shape_fn((dim, 100), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            trend_analyzer: TrendAnalyzer::new(),
            seasonality_detector: SeasonalityDetector::new(dim),
        }
    }
}

impl TrendAnalyzer {
    /// Create new trend analyzer
    pub fn new() -> Self {
        Self {
            trend_coefficients: Array1::from(vec![1.0, 0.5, 0.1]),
            window_size: 10,
            trend_strength: 0.0,
        }
    }
}

impl SeasonalityDetector {
    /// Create new seasonality detector
    pub fn new(dim: usize) -> Self {
        Self {
            seasonal_patterns: Array2::zeros((dim, 12)),
            pattern_strength: Array1::zeros(12),
            detection_threshold: 0.1,
        }
    }
}

impl ConfidenceEstimator {
    /// Create new confidence estimator
    pub fn new(hidden_size: usize) -> Self {
        Self {
            confidence_network: Array2::from_shape_fn((1, hidden_size), |_| {
                (rand::rng().random::<f64>() - 0.5) * 0.1
            }),
            uncertainty_quantifier: UncertaintyQuantifier::new(),
            calibration_params: Array1::from(vec![1.0, 0.0, 0.1]),
        }
    }

    /// Estimate confidence in prediction
    pub fn estimate_confidence(&self, features: &Array1<f64>) -> OptimizeResult<f64> {
        let mut confidence = 0.0;
        for j in 0..features.len().min(self.confidence_network.ncols()) {
            confidence += self.confidence_network[[0, j]] * features[j];
        }

        // Apply sigmoid to get [0, 1] range
        Ok(1.0 / (1.0 + (-confidence).exp()))
    }
}

impl UncertaintyQuantifier {
    /// Create new uncertainty quantifier
    pub fn new() -> Self {
        Self {
            epistemic_uncertainty: 0.1,
            aleatoric_uncertainty: 0.1,
            method: UncertaintyMethod::Dropout,
        }
    }
}

impl Default for AdaptiveOptimizationStats {
    fn default() -> Self {
        Self {
            strategy_switches: 0,
            avg_adaptation_rate: 0.1,
            prediction_accuracy: 0.5,
            computational_efficiency: 0.5,
            robustness_score: 0.5,
        }
    }
}

impl LearnedOptimizer for NeuralAdaptiveOptimizer {
    fn meta_train(&mut self, training_tasks: &[TrainingTask]) -> OptimizeResult<()> {
        // Convert training tasks to trajectories
        let mut trajectories = Vec::new();

        for task in training_tasks {
            let trajectory = self.create_trajectory_from_task(task)?;
            trajectories.push(trajectory);
        }

        // Train networks
        self.train_networks(&trajectories)?;

        Ok(())
    }

    fn adapt_to_problem(
        &mut self,
        _problem: &OptimizationProblem,
        _params: &ArrayView1<f64>,
    ) -> OptimizeResult<()> {
        // Adaptation happens dynamically during optimization
        Ok(())
    }

    fn optimize<F>(
        &mut self,
        objective: F,
        initial_params: &ArrayView1<f64>,
    ) -> OptimizeResult<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut current_params = initial_params.to_owned();
        let mut best_value = objective(initial_params);
        let mut iterations = 0;

        for step_number in 0..1000 {
            iterations = step_number;

            // Get adaptive optimization step
            let adaptive_step =
                self.adaptive_optimization_step(&objective, &current_params.view(), step_number)?;

            // Apply the selected strategy
            let direction = self.compute_direction_for_strategy(
                &objective,
                &current_params,
                &adaptive_step.strategy,
            )?;
            let step_size = self.compute_step_size_for_strategy(&adaptive_step.strategy);

            // Update parameters
            for i in 0..current_params.len().min(direction.len()) {
                current_params[i] -= step_size * direction[i];
            }

            let current_value = objective(&current_params.view());

            if current_value < best_value {
                best_value = current_value;
            }

            // Record performance for adaptation
            self.meta_state.performance_history.push(current_value);

            // Check convergence
            if adaptive_step.confidence > 0.95 && step_size < 1e-8 {
                break;
            }
        }

        Ok(OptimizeResults::<f64> {
            x: current_params,
            fun: best_value,
            success: true,
            nit: iterations,
            message: "Neural adaptive optimization completed".to_string(),
            jac: None,
            hess: None,
            constr: None,
            nfev: iterations * 5, // Neural network evaluations
            njev: 0,
            nhev: 0,
            maxcv: 0,
            status: 0,
        })
    }

    fn get_state(&self) -> &MetaOptimizerState {
        &self.meta_state
    }

    fn reset(&mut self) {
        self.adaptive_stats = AdaptiveOptimizationStats::default();
        self.meta_state.performance_history.clear();
        self.adaptation_controller.strategy_history.clear();
        // Clear computation cache buffers
        self.computation_cache.gradient_buffer.fill(0.0);
        self.computation_cache.feature_buffer.fill(0.0);
        self.computation_cache.param_buffer.fill(0.0);
        self.computation_cache.network_output_buffer.fill(0.0);
        self.computation_cache.temp_buffer.fill(0.0);
    }
}

impl NeuralAdaptiveOptimizer {
    fn create_trajectory_from_task(
        &self,
        task: &TrainingTask,
    ) -> OptimizeResult<OptimizationTrajectory> {
        // Simplified trajectory creation
        let num_steps = 10;
        let mut states = Vec::new();
        let mut actions = Vec::new();
        let mut performance_values = Vec::new();
        let mut rewards = Vec::new();

        for i in 0..num_steps {
            states.push(Array1::from_shape_fn(
                self.optimization_network.architecture.input_size,
                |_| rand::rng().random::<f64>(),
            ));

            actions.push(Array1::from_shape_fn(
                self.optimization_network.architecture.output_size,
                |_| rand::rng().random::<f64>(),
            ));

            performance_values.push(1.0 - i as f64 / num_steps as f64);
            rewards.push(if i > 0 {
                performance_values[i - 1] - performance_values[i]
            } else {
                0.0
            });
        }

        Ok(OptimizationTrajectory {
            states,
            actions,
            performance_values,
            rewards,
        })
    }

    fn compute_direction_for_strategy<F>(
        &mut self,
        objective: &F,
        params: &Array1<f64>,
        strategy: &OptimizationStrategy,
    ) -> OptimizeResult<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        // Compute finite difference gradient using cached buffers
        let h = 1e-6;
        let f0 = objective(&params.view());
        let (gradient_buffer, param_buffer) = self
            .computation_cache
            .get_gradient_and_param_buffers(params.len(), params.len());

        // Copy parameters to buffer
        for (i, &val) in params.iter().enumerate() {
            if i < param_buffer.len() {
                param_buffer[i] = val;
            }
        }

        for i in 0..params.len().min(gradient_buffer.len()) {
            let original_val = param_buffer[i];
            param_buffer[i] = original_val + h;
            let f_plus = objective(&param_buffer.view());
            param_buffer[i] = original_val; // Restore
            gradient_buffer[i] = (f_plus - f0) / h;
        }

        // Create result gradient from buffer
        let mut gradient = Array1::zeros(params.len());
        for i in 0..params.len().min(gradient_buffer.len()) {
            gradient[i] = gradient_buffer[i];
        }

        // Apply strategy-specific transformations
        match strategy.id.as_str() {
            "momentum" => {
                // Apply momentum (simplified)
                gradient *= strategy.parameters[1]; // momentum factor
            }
            "adaptive" => {
                // Apply adaptive scaling
                let adaptivity = strategy.parameters[2];
                gradient.mapv_inplace(|g| g / (1.0 + adaptivity * g.abs()));
            }
            _ => {
                // Default gradient descent
            }
        }

        Ok(gradient)
    }

    fn compute_step_size_for_strategy(&self, strategy: &OptimizationStrategy) -> f64 {
        strategy.parameters[0] // Use first parameter as learning rate
    }
}

/// Convenience function for neural adaptive optimization
#[allow(dead_code)]
pub fn neural_adaptive_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    config: Option<LearnedOptimizationConfig>,
) -> OptimizeResult<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let config = config.unwrap_or_default();
    let mut optimizer = NeuralAdaptiveOptimizer::new(config);
    optimizer.optimize(objective, initial_params)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_neural_adaptive_optimizer_creation() {
        let config = LearnedOptimizationConfig::default();
        let optimizer = NeuralAdaptiveOptimizer::new(config);

        assert_eq!(optimizer.adaptive_stats.strategy_switches, 0);
    }

    #[test]
    fn test_optimization_network() {
        let architecture = NetworkArchitecture {
            input_size: 10,
            hidden_sizes: vec![16, 8],
            output_size: 4,
            activations: vec![
                ActivationType::ReLU,
                ActivationType::ReLU,
                ActivationType::Tanh,
            ],
            use_recurrent: false,
            use_attention: false,
        };

        let mut network = OptimizationNetwork::new(architecture);
        let input = Array1::from(vec![1.0; 10]);

        let output = network.forward(&input.view()).unwrap();

        assert_eq!(output.len(), 4);
        assert!(output.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_neural_layer() {
        let mut layer = NeuralLayer::new(5, 3, ActivationType::ReLU);
        let input = Array1::from(vec![1.0, 2.0, 3.0, 4.0, 5.0]);

        let output = layer.forward(&input.view()).unwrap();

        assert_eq!(output.len(), 3);
        assert!(output.iter().all(|&x| x.is_finite()));
    }

    #[test]
    fn test_strategy_selector() {
        let selector = StrategySelector::new(16);
        let network_output = Array1::from(vec![0.5; 16]);

        let strategy = selector.select(&network_output, 0.8).unwrap();

        assert!(!strategy.id.is_empty());
        assert!(strategy.expected_performance >= 0.0);
    }

    #[test]
    fn test_performance_predictor() {
        let predictor = PerformancePredictor::new(32);
        let features = Array1::from(vec![0.1; 32]);

        let prediction = predictor.predict(&features).unwrap();

        assert!(prediction >= -1.0 && prediction <= 1.0);
    }

    #[test]
    fn test_neural_adaptive_optimization() {
        let objective = |x: &ArrayView1<f64>| x[0].powi(2) + x[1].powi(2);
        let initial = Array1::from(vec![2.0, 2.0]);

        let config = LearnedOptimizationConfig {
            hidden_size: 32,
            max_parameters: 50,
            ..Default::default()
        };

        let result = neural_adaptive_optimize(objective, &initial.view(), Some(config)).unwrap();

        assert!(result.fun >= 0.0);
        assert_eq!(result.x.len(), 2);
        assert!(result.success);
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}