# scirs2-optimize v0.1.0 Release Status
This module provides comprehensive optimization algorithms similar to SciPy's optimize module, implemented in Rust with full production support. Following the [SciRS2 POLICY](../SCIRS2_POLICY.md), this module features ecosystem consistency and platform testing.
## ✅ Production-Ready Features (v0.1.0)
### Core Optimization Methods
- **Unconstrained Optimization**: Nelder-Mead, BFGS, L-BFGS, Powell, Conjugate Gradient
- **Constrained Optimization**: SLSQP, Trust-region constrained, Augmented Lagrangian
- **Bounds Support**: Full bounds constraints for all unconstrained methods
- **Least Squares**: Levenberg-Marquardt, Trust Region Reflective, robust variants
- **Root Finding**: Hybrid methods, Broyden's methods, Anderson acceleration, Krylov
- **Scalar Optimization**: Brent's method, Golden section search, bounded optimization
### Advanced Optimization
- **Global Methods**: Differential Evolution, Basin-hopping, Dual Annealing, Particle Swarm, Simulated Annealing
- **Bayesian Optimization**: Gaussian Process surrogate models with multiple acquisition functions
- **Multi-objective**: NSGA-II, NSGA-III, scalarization methods
- **Stochastic Methods**: SGD variants, Adam, AdamW, RMSprop with momentum
- **Robust Least Squares**: Huber, Bisquare, Cauchy loss functions for outlier resistance
### Performance Features
- **Parallel Computing**: Multi-threaded evaluation, parallel global optimization
- **Memory Efficiency**: Large-scale sparse matrix handling, memory-efficient algorithms
- **JIT Compilation**: Just-in-time optimization for performance-critical functions
- **SIMD Operations**: Migrated to scirs2-core unified SIMD abstraction layer
- **Automatic Differentiation**: Forward and reverse mode AD support
### Specialized Capabilities
- **Sparse Numerical Differentiation**: Efficient Jacobian/Hessian computation
- **Async Optimization**: Asynchronous parallel evaluation for slow functions
- **Multi-start Strategies**: Clustering-based and systematic restart methods
- **Weighted/Bounded/Total Least Squares**: Extended least squares variants
## 🔄 Recent Changes
### SIMD Migration
- [x] Migrated all SIMD operations to use scirs2-core unified abstraction layer
- [x] Replaced custom platform detection with PlatformCapabilities from core
- [x] Removed direct x86_64 intrinsics usage
- [x] Added compatibility wrappers for smooth transition
- [x] All tests passing with new implementation
## 📋 More Enhancements
### Algorithm Improvements
- [x] SR1 and DFP quasi-Newton updates
- [x] Interior point methods for nonlinear programming
- [x] Hager-Zhang line search implementation
- [x] Enhanced convergence diagnostics
### Usability & Integration
- [x] Comprehensive benchmarking suite against SciPy
- [x] Advanced callback system for monitoring
- [x] Integration with scirs2-neural for ML optimization
- [x] Visualization tools for optimization trajectories
### Advanced Methods
- [x] GPU acceleration for suitable algorithms
- [x] Distributed optimization via MPI
- [x] Self-tuning parameter selection
- [x] Specialized ML optimizers (L1/group regularization)
## 🚀 Cutting-Edge Mode (v0.1.0)
### Core Cutting-Edge Features
- [x] **Unified Cutting-Edge Coordinator**: Intelligent coordination between optimization strategies
- [x] **Quantum-Neural Fusion**: Combining quantum superposition with neural adaptation
- [x] **Neuromorphic-Quantum Hybrid**: Spiking networks with quantum tunneling capabilities
- [x] **Meta-Learning Quantum**: Quantum-enhanced meta-learning optimization
- [x] **Adaptive Strategy Selection**: Dynamic strategy switching based on performance
- [x] **Full Cutting-Edge**: All strategies working in parallel with intelligent coordination
### Advanced Coordination Features
- [x] Cross-modal knowledge transfer between optimization approaches
- [x] Real-time performance tracking and strategy adaptation
- [x] Multi-strategy solution fusion with weighted combination
- [x] Problem characteristic learning and adaptation
- [x] Intelligent resource allocation and load balancing
- [x] Comprehensive optimization statistics and monitoring
### Cutting-Edge Capabilities
- [x] **Quantum Components**: Superposition, entanglement, tunneling, annealing
- [x] **Neuromorphic Components**: Spiking networks, STDP learning, event-driven optimization
- [x] **Meta-Learning Components**: Transformer-based optimization, few-shot adaptation
- [x] **Fusion Engine**: Cross-modal solution combination and knowledge transfer
- [x] **Adaptive Selector**: Performance-based strategy switching
- [x] **Performance Predictor**: Strategy effectiveness prediction
## 🔧 Technical Notes
- **API Stability**: Core API is stable and follows SciPy conventions
- **Error Handling**: Comprehensive error types with detailed diagnostics
- **Documentation**: Full API documentation with examples
- **Testing**: Extensive test suite covering all major algorithms
- **Performance**: Benchmarked against SciPy with comparable or better performance
- **Dependencies**: Minimal external dependencies, leverages workspace-managed versions
## 📦 Installation & Usage
This release is production-ready for scientific computing applications. All core optimization methods are fully implemented and tested. See README.md for detailed usage examples and API reference.