# OptiRS Core TODO (v0.3.0)
## Module Status: Production Ready
**Release Date**: 2026-03-17
**Tests**: 647 unit tests + doc tests passing (3 ignored)
**Optimizers**: 21 fully implemented
**SciRS2 Compliance**: 100%
---
## Vision & Goals
Build a state-of-the-art, production-ready optimization library for Rust that rivals PyTorch and TensorFlow optimizers in performance and exceeds them in memory safety and ergonomics.
---
## Completed: Core Optimizers
### First-Order Optimizers (16 total)
- [x] **SGD** - Stochastic Gradient Descent
- [x] Basic SGD with learning rate
- [x] Classical momentum (Polyak)
- [x] Nesterov accelerated gradient (NAG)
- [x] Weight decay integration
- [x] Learning rate scheduling support
- [x] Gradient centralization option
- [x] **SIMD SGD** - SIMD-accelerated SGD
- [x] 2-4x speedup on large arrays
- [x] Automatic SIMD threshold detection
- [x] **Adam** - Adaptive Moment Estimation
- [x] Basic Adam algorithm (beta1=0.9, beta2=0.999, epsilon=1e-8)
- [x] Bias correction for first and second moments
- [x] Numerical stability with epsilon clipping
- [x] Memory-efficient implementation
- [x] **AdamW** - Adam with Decoupled Weight Decay
- [x] Decouple weight decay from gradient-based updates
- [x] Performance optimization with vectorized operations
- [x] Support for different weight decay schedules
- [x] **RMSprop** - Root Mean Square Propagation
- [x] Basic RMSprop implementation
- [x] Centered variant with mean centering
- [x] Momentum integration
- [x] **AdaGrad** - Adaptive Gradient Algorithm
- [x] Basic AdaGrad with accumulator
- [x] Diagonal approximation for memory efficiency
- [x] **AdaDelta** - Adaptive learning rate without manual tuning
- [x] Automatic step size adaptation using RMS
- [x] 10-step warmup boost for cold-start
- [x] Full convergence validation (7 tests)
- [x] **AdaBound** - Dynamic bounds converging to SGD
- [x] Dynamic learning rate bounds
- [x] Smooth transition from adaptive to SGD
- [x] AMSBound variant support
- [x] Final learning rate convergence
- [x] **LAMB** - Layer-wise Adaptive Moments
- [x] Layer-wise adaptation mechanism
- [x] Trust ratio computation
- [x] Large batch optimization (batch size > 16K)
- [x] Mixed precision support
- [x] **LARS** - Layer-wise Adaptive Rate Scaling
- [x] Layer-wise learning rate adaptation
- [x] Trust ratio computation
- [x] **RAdam** - Rectified Adam
- [x] Variance rectification term
- [x] Automated warmup scheduling
- [x] Convergence guarantees
- [x] **Lookahead** - Slow/Fast Weight Updates
- [x] Dual optimizer state management
- [x] Interpolation mechanism
- [x] Compatibility wrapper for any base optimizer
- [x] **Ranger** - RAdam + Lookahead combination
- [x] RAdam + Lookahead combination
- [x] Variance rectification + trajectory smoothing
- [x] Proper slow/fast weight synchronization
- [x] 7 comprehensive tests
- [x] **Lion** - Evolved Sign Momentum
- [x] Sign-based updates
- [x] Memory-efficient (no second moment)
- [x] **SAM** - Sharpness Aware Minimization
- [x] Sharpness-aware perturbation
- [x] Better generalization characteristics
- [x] **SparseAdam** - Sparse Gradient Support
- [x] Efficient sparse tensor handling
- [x] High-dimensional problem optimization
- [x] **GroupedAdam** - Parameter Group Support
- [x] Different hyperparameters per group
- [x] Layer-wise configuration
### Second-Order Methods (3 total)
- [x] **L-BFGS** - Limited-memory BFGS
- [x] Two-loop recursion algorithm
- [x] Line search with Wolfe conditions
- [x] Memory-efficient history management
- [x] Configurable memory size
- [x] **L-BFGS Simple** - Simplified L-BFGS
- [x] Easier configuration
- [x] Good default parameters
- [x] **Newton-CG** - Newton Conjugate Gradient
- [x] Conjugate gradient solver for Newton system
- [x] O(n) memory using only Hessian-vector products
- [x] Trust region control
- [x] Negative curvature detection
- [x] 7 comprehensive tests
---
## Completed: SciRS2 Integration
- [x] **Full SciRS2-Core Integration** - 100% complete
- [x] **Array Operations** - All ndarray imports via scirs2_core::ndarray
- [x] **Random Generation** - All rand imports via scirs2_core::random
- [x] **Error Handling** - Integrated scirs2_core::error types
- [x] **Namespace Correction** - Fixed all scirs2_optim references
- [x] **Compilation Fixes** - Resolved all SciRS2 policy violations
---
## Completed: Advanced Features
### Mathematical Utilities
- [x] **Gradient Processing**
- [x] Gradient clipping (by norm: L2, L-inf, and by value)
- [x] Gradient normalization (layer-wise and global)
- [x] Gradient accumulation with overflow prevention
- [x] Gradient centralization (zero-mean gradients)
- [x] **Numerical Stability**
- [x] Overflow/underflow prevention
- [x] NaN/Inf detection
- [x] Mixed precision training support (FP16/BF16/FP32)
### Learning Rate Scheduling
- [x] Exponential decay
- [x] Step decay
- [x] Multi-step decay
- [x] Cosine annealing with warm restarts
- [x] Linear warmup strategies
- [x] Polynomial decay
- [x] Cyclical learning rates
- [x] OneCycle scheduling
- [x] ReduceLROnPlateau
### Performance Optimization
- [x] **SIMD Acceleration**
- [x] SIMD-optimized mathematical operations
- [x] Platform-specific optimizations
- [x] 2-4x speedup achieved
- [x] **Parallel Processing**
- [x] Parallel gradient updates
- [x] Thread-safe optimizer state management
- [x] 4-8x speedup achieved
### Memory Efficiency
- [x] In-place operations with mutation tracking
- [x] Memory pool for temporary calculations
- [x] Gradient accumulation for large models
- [x] Chunked processing for billion-parameter models
---
## Future Work (v0.3.0+)
### Meta-Learning Optimizers
- [ ] MAML (Model-Agnostic Meta-Learning) support
- [x] Reptile optimizer
- [x] Meta-SGD with learnable learning rates
- [ ] Neural optimizer implementations
### Additional Regularization
- [x] Spectral normalization
- [x] Weight standardization
- [x] Group Lasso and structured sparsity
### Distributed & Federated Learning
- [x] FedAvg implementation (FedProx with mu=0 degenerates to FedAvg)
- [x] FedProx with proximal term (distributed/fedprox.rs)
- [ ] Differential privacy integration
- [ ] Secure aggregation protocols
### Developer Experience
- [x] Gradient flow visualization (GradientFlowAnalyzer with SVG output, vanishing/exploding detection)
- [x] Loss landscape visualization (LossLandscapeAnalyzer: 2D perturbation, sharpness, saddle point detection)
- [ ] Hyperparameter sensitivity analysis
### Domain-Specific Optimizers
- [x] Vision-specific (ViTLayerDecay scheduler: per-layer exponential LR decay for Vision Transformers)
- [x] NLP-specific (AttentionAwareScheduler: component-specific LR scaling for Transformer models)
- [ ] RL-specific (PPO, TRPO variants)
---
## Testing Status
### Coverage
- [x] Unit tests for all 19 optimizers
- [x] Convergence tests (Rosenbrock, Himmelblau)
- [x] Numerical stability tests
- [x] Edge case handling tests
- [x] Performance regression tests
### Test Count
```
647 unit tests passing
3 intentionally ignored (hardware-specific)
Doc tests: All passing
```
---
## Performance Metrics Achieved
- SGD: < 10ns per parameter update
- Adam: < 50ns per parameter update
- Memory overhead: < 1.5x parameter size
- Parallel efficiency: > 85% on multi-core
- SIMD speedup: 2-4x on large arrays
---
## Design Principles Followed
- **Zero-cost abstractions**: No runtime overhead for unused features
- **Memory safety**: Leveraging Rust's ownership system
- **Ergonomics**: PyTorch/TensorFlow-like API
- **Performance**: Optimized for both throughput and latency
- **Correctness**: Extensive testing and validation
---
**Status**: ✅ Production Ready
**Version**: v0.3.0
**Release Date**: 2026-03-17