OptiRS Learned

Learned optimizers and meta-learning for adaptive optimization in the OptiRS machine learning optimization library.

Overview

OptiRS-Learned implements state-of-the-art learned optimization techniques that use neural networks to learn better optimization strategies. This crate provides meta-learning algorithms, learned optimizers, and adaptive optimization techniques that can outperform traditional hand-designed optimizers on specific tasks and domains.

Features

• Learned Optimizers: Neural network-based optimizers that learn from experience
• Meta-Learning: Algorithms that learn to learn optimization strategies
• Transformer-Based Optimizers: Attention-based optimization with sequence modeling
• LSTM Optimizers: Recurrent neural network optimizers for sequential optimization
• Few-Shot Learning: Quick adaptation to new optimization tasks
• Domain-Specific Adaptation: Optimizers that specialize for specific problem domains
• Online Learning: Continuous adaptation during training
• Transfer Learning: Knowledge transfer between optimization tasks

Learned Optimizer Types

Neural Optimizers

• MLP Optimizers: Multi-layer perceptron-based optimization rules
• Transformer Optimizers: Self-attention mechanisms for parameter updates
• LSTM Optimizers: Long short-term memory networks for optimization
• Graph Neural Network Optimizers: Exploiting computational graph structure
• Hybrid Optimizers: Combining learned and traditional components

Meta-Learning Approaches

• MAML (Model-Agnostic Meta-Learning): Generic meta-learning for optimizers
• Learned Learning Rates: Neural networks that predict optimal learning rates
• Gradient-Based Meta-Learning: Learning optimization rules through gradients
• Memory-Augmented Optimizers: External memory for optimization history
• Few-Shot Optimizer Adaptation: Quick specialization for new domains

Installation

Add this to your Cargo.toml:

[dependencies]
optirs-learned = "0.1.0-beta.1"

Feature Selection

Enable specific learned optimizer types:

[dependencies]
optirs-learned = { version = "0.1.0-beta.1", features = ["transformer", "lstm", "meta_learning"] }

Available features:

• transformer: Transformer-based optimizers (enabled by default)
• lstm: LSTM-based optimizers
• meta_learning: Meta-learning algorithms
• autograd_integration: Automatic differentiation integration
• nlp: Natural language processing utilities for tokenization

Usage

Basic Learned Optimizer

use optirs_learned::{LearnedOptimizer, TransformerOptimizer};
use optirs_core::optimizers::OptimizerConfig;

// Create a transformer-based learned optimizer
let mut learned_optimizer = TransformerOptimizer::new()
    .with_hidden_size(256)
    .with_num_layers(4)
    .with_num_heads(8)
    .with_sequence_length(100)
    .build()?;

// Train the optimizer on a meta-training set
let meta_training_tasks = load_meta_training_tasks()?;
learned_optimizer.meta_train(&meta_training_tasks).await?;

// Use the learned optimizer for a new task
let mut params = create_model_parameters()?;
let grads = compute_gradients(&params, &training_data)?;

// The optimizer learns and adapts during training
learned_optimizer.step(&mut params, &grads).await?;

LSTM-Based Optimizer

use optirs_learned::{LstmOptimizer, OptimizerMemory};

// Create LSTM optimizer with memory
let mut lstm_optimizer = LstmOptimizer::new()
    .with_hidden_size(128)
    .with_num_layers(2)
    .with_memory(OptimizerMemory::new(1000))
    .with_forget_gate_bias(1.0)
    .build()?;

// The LSTM maintains state across optimization steps
for epoch in 0..100 {
    for batch in training_data.batches() {
        let grads = compute_batch_gradients(&params, &batch)?;
        lstm_optimizer.step(&mut params, &grads).await?;

        // LSTM state carries forward optimization knowledge
    }
}

Meta-Learning Example

use optirs_learned::{MetaLearner, MAMLOptimizer, TaskDistribution};

// Setup meta-learning for optimizer adaptation
let meta_learner = MAMLOptimizer::new()
    .with_inner_steps(5)
    .with_inner_lr(0.01)
    .with_outer_lr(0.001)
    .build()?;

// Define task distribution for meta-learning
let task_distribution = TaskDistribution::new()
    .add_domain("computer_vision", 0.4)
    .add_domain("nlp", 0.3)
    .add_domain("speech", 0.3)
    .build();

// Meta-train across multiple task domains
for meta_epoch in 0..1000 {
    let task_batch = task_distribution.sample_tasks(16)?;

    for task in task_batch {
        // Inner loop: adapt to specific task
        let adapted_optimizer = meta_learner.adapt_to_task(&task).await?;

        // Evaluate adapted optimizer
        let task_loss = adapted_optimizer.evaluate(&task.test_data).await?;

        // Outer loop: update meta-parameters
        meta_learner.meta_update(&task_loss).await?;
    }
}

Domain-Specific Specialization

use optirs_learned::{DomainSpecificOptimizer, OptimizationDomain};

// Create optimizers specialized for different domains
let vision_optimizer = DomainSpecificOptimizer::new()
    .for_domain(OptimizationDomain::ComputerVision)
    .with_architecture("resnet_specialized")
    .with_data_augmentation_aware(true)
    .build()?;

let nlp_optimizer = DomainSpecificOptimizer::new()
    .for_domain(OptimizationDomain::NaturalLanguageProcessing)
    .with_architecture("transformer_specialized")
    .with_attention_pattern_aware(true)
    .with_tokenizer_integration(true)
    .build()?;

// Optimizers automatically adapt to domain characteristics
vision_optimizer.optimize_cnn_model(&mut cnn_params, &image_data).await?;
nlp_optimizer.optimize_transformer_model(&mut transformer_params, &text_data).await?;

Online Learning and Adaptation

use optirs_learned::{OnlineLearner, AdaptiveOptimizer};

// Create optimizer that continuously learns during training
let mut adaptive_optimizer = OnlineLearner::new()
    .with_adaptation_rate(0.01)
    .with_memory_size(10000)
    .with_exploration_rate(0.1)
    .build()?;

// Optimizer adapts based on observed performance
for training_step in 0..100000 {
    let grads = compute_gradients(&params, &current_batch)?;

    // Optimizer learns from its own performance
    let step_result = adaptive_optimizer.step(&mut params, &grads).await?;

    // Provide feedback to improve future decisions
    let performance_feedback = evaluate_step_quality(&step_result)?;
    adaptive_optimizer.update_from_feedback(&performance_feedback).await?;
}

Architecture

Learned Optimizer Components

• Neural Architecture: Configurable neural network architectures for optimization
• Memory Systems: External memory for storing optimization history
• Attention Mechanisms: Self-attention for parameter importance weighting
• Adaptation Layers: Quick adaptation to new tasks and domains
• Meta-Learning Framework: Learning to learn optimization strategies

Training Infrastructure

• Meta-Training Pipeline: Distributed training across multiple tasks
• Task Sampling: Intelligent sampling of training tasks
• Gradient Computation: Efficient second-order gradient computation
• Checkpointing: Save and restore learned optimizer states
• Evaluation Framework: Comprehensive evaluation on diverse tasks

Advanced Features

Automatic Hyperparameter Tuning

use optirs_learned::{AutoHyperparameterTuner, HyperparameterSpace};

let hyperparameter_space = HyperparameterSpace::new()
    .add_learning_rate_range(1e-5, 1e-1)
    .add_batch_size_options(&[16, 32, 64, 128])
    .add_architecture_options(&["small", "medium", "large"])
    .build();

let tuner = AutoHyperparameterTuner::new()
    .with_search_space(hyperparameter_space)
    .with_budget(100)  // Number of trials
    .with_parallel_evaluations(4)
    .build();

let optimal_config = tuner.find_optimal_hyperparameters(&task).await?;

Neural Architecture Search for Optimizers

use optirs_learned::{OptimizerNAS, ArchitectureSearchSpace};

let search_space = ArchitectureSearchSpace::new()
    .add_layer_types(&["linear", "attention", "lstm", "gru"])
    .add_hidden_sizes(&[64, 128, 256, 512])
    .add_activation_functions(&["relu", "gelu", "swish"])
    .build();

let nas = OptimizerNAS::new()
    .with_search_space(search_space)
    .with_performance_predictor(PerformancePredictor::default())
    .build();

let optimal_architecture = nas.search_architecture(&meta_training_tasks).await?;

Integration with Traditional Optimizers

Hybrid Optimizers

use optirs_learned::{HybridOptimizer, TraditionalOptimizer};
use optirs_core::optimizers::Adam;

// Combine learned and traditional optimizers
let hybrid = HybridOptimizer::new()
    .with_learned_component(TransformerOptimizer::default())
    .with_traditional_component(Adam::new(0.001))
    .with_mixing_strategy(MixingStrategy::AdaptiveWeighting)
    .build()?;

// The optimizer learns when to use each component
hybrid.step(&mut params, &grads).await?;

Performance Monitoring

Learned Optimizer Analytics

use optirs_learned::{OptimizerAnalytics, LearningCurveTracker};

let analytics = OptimizerAnalytics::new()
    .with_metrics(&["convergence_speed", "final_performance", "stability"])
    .with_visualization(true)
    .build();

let tracker = LearningCurveTracker::new()
    .with_smoothing_window(100)
    .with_trend_detection(true)
    .build();

// Track optimizer learning progress
analytics.track_optimization_step(&step_result).await?;
tracker.update(&current_loss, &learning_metrics).await?;

Research and Experimental Features

Continual Learning Optimizers

• Optimizers that avoid catastrophic forgetting
• Elastic weight consolidation for optimization rules
• Progressive neural optimizer architectures
• Experience replay for optimization strategies

Multi-Task Optimizers

• Shared optimization knowledge across tasks
• Task-specific adaptation layers
• Cross-domain knowledge transfer
• Meta-learning for multi-task scenarios

Contributing

OptiRS follows the Cool Japan organization's development standards. See the main OptiRS repository for contribution guidelines.

Research Papers and References

This crate implements techniques from various research papers:

• "Learning to Learn by Gradient Descent by Gradient Descent" (Andrychowicz et al.)
• "Learned Optimizers that Scale and Generalize" (Metz et al.)
• "Tasks, stability, architecture, and compute: Training more effective learned optimizers" (Metz et al.)
• "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" (Finn et al.)

License

This project is licensed under either of:

• Apache License, Version 2.0
• MIT License

at your option.