**Dear SymPy Community**, I'm a first-year dual-degree student in **Mathematics & AI** at Shanghai Jiao Tong University. I’m excited to contribute to SymPy’s codegen capabilities, specifically targeting the **"Efficient Jacobian/Hessian Evaluation for Optimization and ODE Integration"** project for GSoC 2025.
**Why This Project?** During my coursework on numerical optimization and ODE modeling, I encountered bottlenecks in generating efficient derivative functions for large-scale problems. SymPy’s symbolic differentiation paired with codegen could bridge this gap, and I aim to integrate recent advancements (e.g., #26773’s Jacobian optimizations) into a unified API. **Technical Vision**: 1. **CSE Optimization**: Eliminate redundant calculations via common subexpression elimination. 2. **Sparsity Detection**: Automatically identify zero elements in Jacobian matrices for ODE problems, enabling sparse storage formats. 3. **High-Performance Codegen**: Leverage `lambdify` and LLVM to generate C/Fortran code with near-handwritten performance. **Request for Guidance**: 1. Are there ongoing discussions or roadblocks around derivative codegen I should prioritize? 2. Would a prototype integrating Riccardo’s Jacobian method with `lambdify` be valuable as a starting point? 3. Is there interest in sparse Jacobian support for ODE problems, and are there preferred sparse formats (e.g., COO/CSC)? I’m eager to refine my proposal with community feedback. Thank you for your time! **GitHub**: voyager-jhk Best regards, Peiqi Li -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/sympy/CAPnDX9DixKS69%2B5mknBZuMy_HvL6fN2oM62obkV0EeSj97_utw%40mail.gmail.com.
