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# Module: tfp.optimizer

TensorFlow Probability Optimizer python package.

## Modules

`convergence_criteria` module: TensorFlow Probability convergence criteria for optimizations.

`linesearch` module: Line-search optimizers package.

## Classes

`class StochasticGradientLangevinDynamics`: An optimizer module for stochastic gradient Langevin dynamics.

`class VariationalSGD`: An optimizer module for constant stochastic gradient descent.

## Functions

`bfgs_minimize(...)`: Applies the BFGS algorithm to minimize a differentiable function.

`converged_all(...)`: Condition to stop when all batch members have converged or failed.

`converged_any(...)`: Condition to stop when any batch member converges, or all have failed.

`differential_evolution_minimize(...)`: Applies the Differential evolution algorithm to minimize a function.

`differential_evolution_one_step(...)`: Performs one step of the differential evolution algorithm.

`lbfgs_minimize(...)`: Applies the L-BFGS algorithm to minimize a differentiable function.

`nelder_mead_minimize(...)`: Minimum of the objective function using the Nelder Mead simplex algorithm.

`nelder_mead_one_step(...)`: A single iteration of the Nelder Mead algorithm.

`proximal_hessian_sparse_minimize(...)`: Minimize using Hessian-informed proximal gradient descent.

`proximal_hessian_sparse_one_step(...)`: One step of (the outer loop of) the minimization algorithm.

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