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# Licensed under the Apache License, Version 2.0 (the "License");
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# https://www.apache.org/licenses/LICENSE-2.0
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"""Implementation of gradient descent in JAX."""
from typing import Any
from typing import Callable
from typing import NamedTuple
from typing import Union
from dataclasses import dataclass
from jaxopt._src import base
from jaxopt._src.proximal_gradient import ProximalGradient, ProxGradState
[docs]@dataclass(eq=False)
class GradientDescent(ProximalGradient):
"""Gradient Descent solver.
Attributes:
fun: a smooth function of the form ``fun(parameters, *args, **kwargs)``,
where ``parameters`` are the model parameters w.r.t. which we minimize
the function and the rest are fixed auxiliary parameters.
value_and_grad: whether ``fun`` just returns the value (False) or both
the value and gradient (True).
has_aux: whether ``fun`` outputs auxiliary data or not.
If ``has_aux`` is False, ``fun`` is expected to be
scalar-valued.
If ``has_aux`` is True, then we have one of the following
two cases.
If ``value_and_grad`` is False, the output should be
``value, aux = fun(...)``.
If ``value_and_grad == True``, the output should be
``(value, aux), grad = fun(...)``.
At each iteration of the algorithm, the auxiliary outputs are stored
in ``state.aux``.
stepsize: a stepsize to use (if <= 0, use backtracking line search), or a
callable specifying the **positive** stepsize to use at each iteration.
maxiter: maximum number of proximal gradient descent iterations.
maxls: maximum number of iterations to use in the line search.
tol: tolerance to use.
acceleration: whether to use acceleration (also known as FISTA) or not.
verbose: whether to print error on every iteration or not.
Warning: verbose=True will automatically disable jit.
implicit_diff: whether to enable implicit diff or autodiff of unrolled
iterations.
implicit_diff_solve: the linear system solver to use.
"""
[docs] def init_state(self,
init_params: Any,
*args,
**kwargs) -> ProxGradState:
"""Initialize the solver state.
Args:
init_params: pytree containing the initial parameters.
*args: additional positional arguments to be passed to ``fun``.
**kwargs: additional keyword arguments to be passed to ``fun``.
Returns:
state
"""
return super().init_state(init_params, None, *args, **kwargs)
[docs] def update(self,
params: Any,
state: NamedTuple,
*args,
**kwargs) -> base.OptStep:
"""Performs one iteration of gradient descent.
Args:
params: pytree containing the parameters.
state: named tuple containing the solver state.
*args: additional positional arguments to be passed to ``fun``.
**kwargs: additional keyword arguments to be passed to ``fun``.
Returns:
(params, state)
"""
return super().update(params, state, None, *args, **kwargs)
[docs] def optimality_fun(self, params, *args, **kwargs):
"""Optimality function mapping compatible with ``@custom_root``."""
return self._grad_fun(params, *args, **kwargs)
def __post_init__(self):
super().__post_init__()
self.reference_signature = self.fun