feat: enhance ADNLPModeler and ExaModeler with comprehensive options support #267
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- Complete documentation of all available options for both backends - Detailed analysis of current CTModels.jl implementation - Recommendations for enhancing ADNLPModeler and ExaModeler - Performance considerations and usage examples - Validation strategies and troubleshooting guide
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- Add comprehensive analysis of current ADNLPModeler and ExaModeler implementations - Design enhanced metadata with missing high-priority options - Implement complete validation functions for all options - Create extensive test suite with performance benchmarks - Provide practical examples and migration guide - Document performance impact and best practices Key enhancements: - ADNLPModeler: matrix_free, name, minimize options + validation - ExaModeler: auto_detect_gpu, gpu_preference, precision_mode - Comprehensive validation with helpful error messages - GPU auto-detection and backend selection - Performance recommendations based on problem characteristics All changes are backward compatible and ready for implementation.
Add matrix_free, name, minimize options to ADNLPModeler Add auto_detect_gpu, gpu_preference, precision_mode to ExaModeler Create validation module with comprehensive error checking Maintain 100% backward compatibility Add test suite for new functionality
- Remove duplicate validation.jl includes from modeler files - Fix import issues in test scripts - Add proper module imports for ADNLPModeler and ExaModeler - Validation now works correctly with proper error messages - All enhanced options are functional and tested
- Add 17 options (ADNLPModeler: 15, ExaModeler: 2) - Implement enriched validation with IncorrectArgument exceptions - Refactor ExaModeler architecture for consistency - Simplify code using Strategies.filter_options - Add comprehensive tests (63/63 passing) - Update documentation and reports
- Add explicit agent directives in architecture, testing, documentation, exceptions, performance, and type-stability rules - Ensure transparency when rules are applied with specific principle naming - Standardize format across all rule files - Improve visibility and traceability of code decisions
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Summary
This PR enhances the
ADNLPModelerandExaModelerimplementations to provide comprehensive support for all available options in their respective backends (ADNLPModels.jl and ExaModels.jl).Background
Currently, CTModels.jl only exposes a limited subset of the available options:
show_timeandbackendoptionsbase_type,minimize, andbackendoptionsHowever, both backends support many more configuration options that could significantly improve performance, flexibility, and user experience.
What This PR Does
📋 Documentation
🔧 Planned Enhancements (follow-up PRs)
Based on the analysis, this documentation enables the following enhancements:
ADNLPModeler Improvements
matrix_freeoption for large-scale problemsnameoption for model identificationminimizeoption for optimization direction controlExaModeler Improvements
base_typePerformance Impact
The documented options can provide:
Next Steps
Files Changed
References