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name: optimization-workflows description: "Route users who need flatten/unflatten helpers, structured optimizers, fixed-point differentiation helpers, and SciPy minimize integration using value_and_grad." disable-model-invocation: true metadata: disco-role: operating license: MIT

Optimization Workflows

Use this sub-skill for optimization tasks built from nested parameter trees, fixed-point recurrences, or SciPy minimization loops.

Route here when the user needs

• flatten or flatten_func to turn nested dict/list/tuple parameter trees into flat arrays and back.
• Structured optimizers such as sgd, rmsprop, or adam on nested containers.
• fixed_point for differentiable iterative updates.
• scipy.optimize.minimize combined with value_and_grad.

Route elsewhere when the question is really about

• value_and_grad semantics, scalar-output constraints, or Jacobian/Hessian selection: differentiation-core.
• General autograd.scipy wrapper behavior or SciPy wrapper coverage: numpy-scipy-primitives.
• Custom primitive or staged VJP/JVP mechanics: extend-primitives.

Bundled runtime helpers

• scripts/structured_optimizer_smoke.py
• scripts/rosenbrock_minimize.py
• scripts/fixed_point_smoke.py

These scripts use tiny synthetic fixtures only; they do not download data and there is no CLI entry point to discover.

What this sub-skill does not own

• Ordinary differentiation operators and higher-order autodiff semantics.
• Custom primitive registration or gradient-rule authoring.
• General NumPy/SciPy wrapper troubleshooting outside optimization workflows.