v1.10.0

Added

• New optimizer module (optimizers/) using the Template Method Pattern with explicit hook methods (_iterate_continuous_batch, _iterate_categorical_batch, _iterate_discrete_batch)
• Extended search space dimension types: continuous (min, max) tuples, categorical ["a", "b"] lists, and discrete numerical NumPy arrays
• DimensionType enum, DimensionInfo dataclass, and DimensionMasks for dimension-aware vectorized operations
• Automatic vectorization for search spaces with 1000+ dimensions via DimensionIteratorMixin
• resolution parameter for GridSearchOptimizer and DirectAlgorithm to handle continuous dimensions
• Mixed-type distance metric (Gower-like) for the DIRECT algorithm across heterogeneous dimensions
• Lazy search data construction in ResultsManager for reduced memory footprint during optimization
• State management via property setters with automatic history tracking in CoreOptimizer
• Extended search-space tests for all optimizers
• Examples for mixed and large search spaces
• Sphinx documentation site with landing page, logos, and navigation

Changed

• All optimizers reimplemented to comply with the new Template Method architecture
• Legacy optimizer implementations preserved in optimizers_legacy/ (not part of public API)
• SciPy restored as a core dependency
• Wall clipping algorithm reworked
• Optimizer initialization refactored (finish_initialization, _generate_position pattern)
• Converter enhanced with dimension type analysis (_analyze_dimension_types)
• Updated CI workflow configuration

Fixed

• finish_initialization in Downhill Simplex and other optimizers
• _move_random in sequential model-based optimizers
• Init position and evaluate_init override issues in optimizer subclasses
• Empty scores edge case in evaluation

Full Changelog: v1.9.0...v1.10.0