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name: bbvi_stochastic_optimizer description: Apply stochastic ascent and AdaGrad updates to black-box variational inference gradient estimates while recording parameter-change evidence.

BBVI Stochastic Optimizer

When To Use

Use this skill to update variational parameters with BBVI gradient estimates. It supports fixed Robbins-Monro-style scalar ascent and diagonal AdaGrad updates from the paper.

Do not use it to compute BBVI gradients or to evaluate predictive likelihood.

Inputs

• params: current variational parameters as a numeric list.
• gradient: gradient estimate with the same shape.
• method: scalar or adagrad.
• learning_rate: scalar base learning rate.
• state: optional AdaGrad accumulator.

Outputs

• params_before and params_after.
• gradient and step.
• state_before and state_after.
• optimizer_step_executed boolean.

Workflow

1. Validate finite parameters and gradients.
2. For scalar, apply params + learning_rate * gradient.
3. For adagrad, update the squared-gradient accumulator and apply learning_rate / sqrt(accumulator + epsilon) per parameter.
4. Return a trace suitable for recovery validation.

Validation

Run:

python tests/test_optimizer.py

Limitations

This skill performs ascent. If minimizing a loss, negate the gradient before calling it.