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[ENH] Improve CG solver and add weights caching for better performance #17
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Updated `_compute_conditional_number` to return the computed condition number and made plotting optional with a new `plot` argument. Adjusted kernel options to store condition number during numpy backend operations.
Introduced a `use_gpu` parameter to enable GPU acceleration in the `pykeops_torch_cg` function. Adjusted solver backend logic to toggle between CPU and GPU based on this parameter.
Enhanced the `ConjugateGradientSolver` function with
Replaced helper function-based preconditioning with the adaptive Nyström preconditioner in the PyKeOps solver. This includes configurable strategies for pivot selection (`aggressive`, `conservative`, `minimal`) and fallback handling for robust kernel matrix approximation. Removed unused diagonal and Jacobi preconditioners. Cleaned up redundant code.
Introduced a `verbose` parameter to control diagnostic print statements during the iteration process.
Introduced a `clear_cache` method in the `WeightCache` class for better memory management. Enhanced the numerical stability of the conjugate gradient solver with improved initialization and adaptive tolerances. Refactored interpolation logic to handle weights more efficiently, and adjusted benchmarking to test expanded solver configurations.
This was referenced Aug 5, 2025
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Enhanced Conjugate Gradient Solver with Improved Stability and GPU Support
This PR significantly improves the robustness and performance of our solver system with several key enhancements:
Solver Improvements
ConjugateGradientSolver
with enhanced stability features for ill-conditioned matricesGPU Acceleration
Caching and Weights Management
weights_x0
in solver input for warm startsclear_cache
method toWeightCache
for better memory managementNumerical Stability
Experimental Features
These changes significantly improve the solver's ability to handle challenging numerical problems while maintaining performance.