Implement study persistence and resumption capabilities for optimization workflows: Features: - Resume existing studies to add more trials - Create new studies when topology/config changes - Study metadata tracking (creation date, trials, config hash) - SQLite database persistence for Optuna studies - Configuration change detection with warnings - List all available studies Key Changes: - Enhanced OptimizationRunner.run() with resume parameter - Added _load_existing_study() for study resumption - Added _save_study_metadata() for tracking - Added _get_config_hash() to detect topology changes - Added list_studies() to view all studies - SQLite storage for study persistence Updated Files: - optimization_engine/runner.py: Core study management - examples/test_journal_optimization.py: Interactive study management - examples/study_management_example.py: Comprehensive examples Usage Examples: # New study runner.run(study_name="bracket_v1", n_trials=50) # Resume study (add 25 more trials) runner.run(study_name="bracket_v1", n_trials=25, resume=True) # New study after topology change runner.run(study_name="bracket_v2", n_trials=50) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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