# OptiRS Core TODO (v0.1.0-rc.2)

## Module Status: Production Ready

**Tests**: 581 unit tests + doc tests passing (3 ignored)
**Optimizers**: 19 fully implemented
**SciRS2 Compliance**: 100%

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## 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.

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## 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** (NEW in RC1)
  - [x] Automatic step size adaptation using RMS
  - [x] 10-step warmup boost for cold-start
  - [x] Full convergence validation (7 tests)

- [x] **AdaBound** (NEW in RC1)
  - [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** (NEW in RC1)
  - [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** (NEW in RC1)
  - [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

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## 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

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## 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

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## Future Work (v0.2.0+)

### Meta-Learning Optimizers
- [ ] MAML (Model-Agnostic Meta-Learning) support
- [ ] Reptile optimizer
- [ ] Meta-SGD with learnable learning rates
- [ ] Neural optimizer implementations

### Additional Regularization
- [ ] Spectral normalization
- [ ] Weight standardization
- [ ] Group Lasso and structured sparsity

### Distributed & Federated Learning
- [ ] FedAvg implementation
- [ ] FedProx with proximal term
- [ ] Differential privacy integration
- [ ] Secure aggregation protocols

### Developer Experience
- [ ] Gradient flow visualization
- [ ] Loss landscape visualization
- [ ] Hyperparameter sensitivity analysis

### Domain-Specific Optimizers
- [ ] Vision-specific (ViT layer decay)
- [ ] NLP-specific (attention-aware scheduling)
- [ ] RL-specific (PPO, TRPO variants)

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## 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
```
581 unit tests passing
3 intentionally ignored (hardware-specific)
Doc tests: All passing
```

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## 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

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## 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

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**Status**: Production Ready
**Version**: v0.1.0-rc.2
