forbes-0023 64b1e24467 feat(solver): compile symbolic Jacobian to flat Python for fast evaluation ...

Add a code generation pipeline that compiles Expr DAGs into flat Python
functions, eliminating recursive tree-walk dispatch in the Newton-Raphson
inner loop.

Key changes:
- Add to_code() method to all 11 Expr node types (expr.py)
- New codegen.py module with CSE (common subexpression elimination),
  sparsity detection, and compile()/exec() compilation pipeline
- Add ParamTable.env_ref() to avoid dict copies per iteration (params.py)
- Newton and BFGS solvers accept pre-built jac_exprs and compiled_eval
  to avoid redundant diff/simplify and enable compiled evaluation
- count_dof() and diagnostics accept pre-built jac_exprs
- solver.py builds symbolic Jacobian once, compiles once, passes to all
  consumers (_monolithic_solve, count_dof, diagnostics)
- Automatic fallback: if codegen fails, tree-walk eval is used

Expected performance impact:
- ~10-20x faster Jacobian evaluation (no recursive dispatch)
- ~2-5x additional from CSE on quaternion-heavy systems
- ~3x fewer entries evaluated via sparsity detection
- Eliminates redundant diff().simplify() in DOF/diagnostics

2026-02-21 11:22:36 -06:00

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