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name: top_eigen_trace_estimators description: Implements power iteration and Hutchinson trace estimation over an HVP oracle for fast curvature summaries.

Top Eigenvalue and Trace Estimators

Use this skill when a recovery or analysis task needs the PyHessian paper component described by this module. Do not use it to read or depend on the original PyHessian repository during recovery; the scripts are self-contained reduced implementations or contract checkers.

Inputs

• Hessian-vector product callable
• Parameter dimension
• Iteration limits, tolerance, and deterministic probe policy

Outputs

• Estimated top eigenvalue and vector
• Trace probe values and trace estimate

Workflow

1. Confirm that the requested experiment matches this module contract.
2. Use the script in scripts/estimators.py for deterministic checks or as reference logic.
3. Preserve the paper mechanism: Power iteration and randomized trace probing preserve the paper mechanism for scalable second-order summaries.
4. Write numeric evidence and avoid qualitative-only conclusions.
5. In recovery, record whether this skill was called, imported, or cross-checked.

Validation

Run python through the Distiller skill-tree validator with --run-tests, or run the test file in tests/ with the repository-independent Python path pointing at this skill's scripts/ directory.

Limitations

This generated skill captures reusable mechanism semantics. It is not a drop-in replacement for the original PyTorch package and does not authorize recovery to read the original repository.