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from tqdm_loggable.auto import tqdm
import jax
import jax.numpy as jnp
from jax import jit
from jax.flatten_util import ravel_pytree
from varipeps import varipeps_config
from varipeps.config import Line_Search_Methods, Wavevector_Type
from varipeps.ctmrg import CTMRGNotConvergedError, CTMRGGradientNotConvergedError
from varipeps.peps import PEPS_Unit_Cell
from varipeps.expectation import Expectation_Model
from varipeps.mapping import Map_To_PEPS_Model
from varipeps.utils.debug_print import debug_print
from .inner_function import (
calc_ctmrg_expectation,
calc_preconverged_ctmrg_value_and_grad,
calc_ctmrg_expectation_custom_value_and_grad,
)
from typing import Sequence, Tuple, List, Union, Optional, Dict
@jit
def _scalar_descent_grad(descent_dir, gradient):
descent_dir_real, _ = ravel_pytree(descent_dir)
gradient_real, _ = ravel_pytree(gradient)
if jnp.iscomplexobj(descent_dir_real):
descent_dir_real = jnp.concatenate(
(jnp.real(descent_dir_real), jnp.imag(descent_dir_real))
)
gradient_real = jnp.concatenate(
(jnp.real(gradient_real), jnp.imag(gradient_real))
)
return jnp.sum(descent_dir_real * gradient_real)
@jit
def _line_search_new_tensors(peps_tensors, descent_dir, alpha):
return [peps_tensors[i] + alpha * descent_dir[i] for i in range(len(peps_tensors))]
def _get_new_unitcell(
new_tensors,
unitcell,
spiral_indices,
convert_to_unitcell_func,
generate_unitcell,
reinitialize_env_as_identities,
):
if spiral_indices is not None:
for i in spiral_indices:
if (
varipeps_config.spiral_wavevector_type
is Wavevector_Type.TWO_PI_POSITIVE_ONLY
):
new_tensors[i] = new_tensors[i] % 2
elif (
varipeps_config.spiral_wavevector_type
is Wavevector_Type.TWO_PI_SYMMETRIC
):
new_tensors[i] = new_tensors[i] % 4 - 2
else:
raise ValueError("Unknown wavevector type!")
if convert_to_unitcell_func is None or generate_unitcell:
unitcell_tensors = unitcell.get_unique_tensors()
new_unitcell = unitcell.replace_unique_tensors(
[
unitcell_tensors[i].replace_tensor(
new_tensors[i],
reinitialize_env_as_identities=reinitialize_env_as_identities,
)
for i in range(unitcell.get_len_unique_tensors())
]
)
else:
new_unitcell = None
return new_tensors, new_unitcell
@jit
def _armijo_value(current_val, descent_dir, gradient, alpha, const_factor):
descent_dir_real, _ = ravel_pytree(descent_dir)
gradient_real, _ = ravel_pytree(gradient)
if jnp.iscomplexobj(descent_dir_real):
descent_dir_real = jnp.concatenate(
(jnp.real(descent_dir_real), jnp.imag(descent_dir_real))
)
gradient_real = jnp.concatenate(
(jnp.real(gradient_real), jnp.imag(gradient_real))
)
return jnp.fmin(
current_val,
current_val + const_factor * alpha * jnp.sum(descent_dir_real * gradient_real),
)
@jit
def _wolfe_value(
current_val,
descent_dir,
gradient,
new_gradient,
alpha,
armijo_const_factor,
wolfe_const_factor,
):
descent_dir_real, _ = ravel_pytree(descent_dir)
gradient_real, _ = ravel_pytree(gradient)
new_gradient_real, _ = ravel_pytree(new_gradient)
if jnp.iscomplexobj(descent_dir_real):
descent_dir_real = jnp.concatenate(
(jnp.real(descent_dir_real), jnp.imag(descent_dir_real))
)
gradient_real = jnp.concatenate(
(jnp.real(gradient_real), jnp.imag(gradient_real))
)
new_gradient_real = jnp.concatenate(
(jnp.real(new_gradient_real), jnp.imag(new_gradient_real))
)
scalar_descent_grad = jnp.sum(descent_dir_real * gradient_real)
cmp_value = current_val + armijo_const_factor * alpha * scalar_descent_grad
scalar_descent_new_grad = jnp.sum(descent_dir_real * new_gradient_real)
strong_wolfe_left_side = -scalar_descent_new_grad
strong_wolfe_right_side = -wolfe_const_factor * scalar_descent_grad
return (
cmp_value,
strong_wolfe_left_side,
strong_wolfe_right_side,
scalar_descent_new_grad,
)
@jit
def _wolfe_new_alpha(
alpha,
last_alpha,
value,
last_value,
descent_grad,
descent_last_grad,
lower_bound,
upper_bound,
):
d1 = (
descent_last_grad
+ descent_grad
- 3 * (last_value - value) / (last_alpha - alpha)
)
d2 = jnp.sign(alpha - last_alpha) * jnp.sqrt(
d1**2 - descent_last_grad * descent_grad
)
new_alpha = alpha - (alpha - last_alpha) * (descent_grad + d2 - d1) / (
descent_grad - descent_last_grad + 2 * d2
)
return jnp.where(
jnp.isinf(value)
| jnp.isinf(last_value)
| (new_alpha <= lower_bound)
| (new_alpha >= upper_bound)
| jnp.isnan(new_alpha),
lower_bound + (upper_bound - lower_bound) / 2,
new_alpha,
)
@jit
def _hager_zhang_initial_zero(input_tensors, gradient, config):
input_tensors_real, _ = ravel_pytree(input_tensors)
gradient_real, _ = ravel_pytree(gradient)
if jnp.iscomplexobj(input_tensors_real):
input_tensors_real = jnp.concatenate(
(jnp.real(input_tensors_real), jnp.imag(input_tensors_real))
)
gradient_real = jnp.concatenate(
(jnp.real(gradient_real), jnp.imag(gradient_real))
)
result = config.line_search_hager_zhang_psi_0
result *= jnp.linalg.norm(input_tensors_real, ord=jnp.inf)
result /= jnp.linalg.norm(gradient_real, ord=jnp.inf)
result = jnp.where(result == 0, config.line_search_initial_step_size, result)
return result
class _Hager_Zhang_Initial_State(enum.Enum):
NOT_FOUND = enum.auto()
FOUND = enum.auto()
SCALAR_LOWER_VALUE_GREATER = enum.auto()
class _Hager_Zhang_State(enum.Enum):
NONE = enum.auto()
UPDATE = enum.auto()
UPDATE_INNER = enum.auto()
@jit
def _hager_zhang_initial_quad_step_inner(
old_value, new_value, gradient, descent_direction, alpha, fallback_alpha
):
g_d_term = _scalar_descent_grad(descent_direction, gradient)
sum_term = old_value + alpha * g_d_term
sum_term -= new_value
alpha = jnp.where(
sum_term < 0, alpha**2 * g_d_term / (2 * sum_term), fallback_alpha
)
alpha = jnp.where(alpha > 0, alpha, fallback_alpha)
return alpha
def _hager_zhang_initial_quad_step(
input_tensors,
unitcell,
gradient,
descent_direction,
old_alpha,
old_value,
spiral_indices,
convert_to_unitcell_func,
generate_unitcell,
expectation_func,
additional_input,
reinitialize_env_as_identities,
enforce_elementwise_convergence,
):
alpha = varipeps_config.line_search_hager_zhang_psi_1 * old_alpha
new_tensors = _line_search_new_tensors(input_tensors, descent_direction, alpha)
new_tensors, new_unitcell = _get_new_unitcell(
new_tensors,
unitcell,
spiral_indices,
convert_to_unitcell_func,
generate_unitcell,
reinitialize_env_as_identities,
)
new_value, (new_unitcell, _) = calc_ctmrg_expectation(
new_tensors,
new_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
enforce_elementwise_convergence=enforce_elementwise_convergence,
)
fallback_alpha = varipeps_config.line_search_hager_zhang_psi_2 * old_alpha
return jnp.where(
new_value <= old_value,
_hager_zhang_initial_quad_step_inner(
old_value, new_value, gradient, descent_direction, alpha, fallback_alpha
),
fallback_alpha,
)
class NoSuitableStepSizeError(Exception):
pass
def line_search(
input_tensors: Sequence[jnp.ndarray],
unitcell: PEPS_Unit_Cell,
expectation_func: Expectation_Model,
gradient: jnp.ndarray,
descent_direction: jnp.ndarray,
current_value: Union[float, jnp.ndarray],
last_step_size: Optional[Union[float, jnp.ndarray]] = None,
convert_to_unitcell_func: Optional[Map_To_PEPS_Model] = None,
generate_unitcell: bool = False,
spiral_indices: Optional[Sequence[int]] = None,
additional_input: Dict[str, jnp.ndarray] = {},
reinitialize_env_as_identities: bool = True,
) -> Tuple[
List[jnp.ndarray],
PEPS_Unit_Cell,
Union[float, jnp.ndarray],
Union[float, jnp.ndarray],
]:
"""
Run two-way backtracing line search method for the CTMRG routine.
Args:
input_tensors (:term:`sequence` of :obj:`jax.numpy.ndarray`):
Sequence of the current tensors which should be optimized.
unitcell (:obj:`~varipeps.peps.PEPS_Unit_Cell`):
The PEPS unitcell to work on.
expectation_func (:obj:`~varipeps.expectation.Expectation_Model`):
Callable to calculate one expectation value which is used as loss
loss function of the model. Likely the function to calculate the energy.
gradient (:obj:`jax.numpy.ndarray`):
The gradient of the CTMRG method and expectation function for the
current step.
descent_direction (:obj:`jax.numpy.ndarray`):
The descent direction which should be used for the line search.
current_value (:obj:`float` or :obj:`jax.numpy.ndarray`):
The current value of the evaluation of the expectation function.
last_step_size (:obj:`float` or :obj:`jax.numpy.ndarray`):
The step size found in the last line search.
convert_to_unitcell_func (:obj:`~varipeps.mapping.Map_To_PEPS_Model`):
Function to convert the `input_tensors` to a PEPS unitcell. If ommited,
it is assumed that a PEPS unitcell is the input.
generate_unitcell (:obj:`bool`):
Force generation of unitcell from new tensors
spiral_indices (:term:`sequence` of :obj:`int`):
If spiral iPEPS ansatz is used, this argument contains the indices
of the wave vectors in the input tensor list.
additional_input (:obj:`dict` of :obj:`str` to :obj:`jax.numpy.ndarray` mapping):
Dict with additional inputs which should be considered in the
calculation of the expectation value.
reinitialize_env_as_identities (:obj:`bool`):
Flag if the env tensors should be reinitialized with identities.
Returns:
:obj:`tuple`\ (:obj:`list`\ (:obj:`jax.numpy.ndarray`), :obj:`~varipeps.peps.PEPS_Unit_Cell`, :obj:`float`, :obj:`float`):
Tuple with the optimized tensors, the new unitcell, the reduced
expectation value and the step size found in the line search.
Raises:
:obj:`ValueError`: The parameters mismatch the expected inputs.
:obj:`RuntimeError`: The line search does not converge.
"""
has_been_increased = False
incrementation_not_helped = False
enforce_elementwise_convergence = (
varipeps_config.ctmrg_enforce_elementwise_convergence
or varipeps_config.ad_use_custom_vjp
)
if varipeps_config.line_search_method is Line_Search_Methods.HAGERZHANG:
if last_step_size is None or last_step_size <= 0:
alpha = _hager_zhang_initial_zero(input_tensors, gradient, varipeps_config)
elif varipeps_config.line_search_hager_zhang_quad_step:
try:
alpha = _hager_zhang_initial_quad_step(
input_tensors,
unitcell,
gradient,
descent_direction,
last_step_size,
current_value,
spiral_indices,
convert_to_unitcell_func,
generate_unitcell,
expectation_func,
additional_input,
reinitialize_env_as_identities,
enforce_elementwise_convergence,
)
except CTMRGNotConvergedError:
alpha = varipeps_config.line_search_hager_zhang_psi_2 * last_step_size
else:
alpha = varipeps_config.line_search_hager_zhang_psi_2 * last_step_size
else:
alpha = (
last_step_size
if last_step_size is not None
and varipeps_config.line_search_use_last_step_size
and last_step_size > 0
else varipeps_config.line_search_initial_step_size
)
wolfe_upper_bound = None
wolfe_lower_bound = None
wolfe_alpha_last_step = 0
wolfe_descent_new_grad = _scalar_descent_grad(descent_direction, gradient)
hager_zhang_lower_bound = 0
hager_zhang_lower_bound_value = current_value
hager_zhang_lower_bound_grad = gradient
hager_zhang_lower_bound_des_grad = wolfe_descent_new_grad
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = None
hager_zhang_upper_bound_grad = None
hager_zhang_upper_bound_des_grad = None
hager_zhang_alpha_last_step = 0
hager_zhang_initial_found = _Hager_Zhang_Initial_State.NOT_FOUND
hager_zhang_descent_grad = wolfe_descent_new_grad
hager_zhang_state = _Hager_Zhang_State.NONE
hager_zhang_eps = (
jnp.linalg.norm(ravel_pytree(gradient)[0])
* varipeps_config.line_search_hager_zhang_eps_grad_norm_factor
if varipeps_config.line_search_hager_zhang_eps_use_grad_norm
else varipeps_config.line_search_hager_zhang_eps
)
new_value = current_value
tmp_value = None
tmp_unitcell = None
tmp_gradient = None
tmp_descent_direction = None
signal_reset_descent_dir = False
cache_original_unitcell = {
unitcell[0, 0][0][0].chi: (unitcell, gradient, descent_direction, current_value)
}
max_trunc_error = jnp.nan
count = 0
while count < varipeps_config.line_search_max_steps:
new_tensors = _line_search_new_tensors(input_tensors, descent_direction, alpha)
new_tensors, new_unitcell = _get_new_unitcell(
new_tensors,
unitcell,
spiral_indices,
convert_to_unitcell_func,
generate_unitcell,
reinitialize_env_as_identities,
)
if (
varipeps_config.line_search_method is Line_Search_Methods.SIMPLE
or varipeps_config.line_search_method is Line_Search_Methods.ARMIJO
):
try:
new_value, (new_unitcell, max_trunc_error) = calc_ctmrg_expectation(
new_tensors,
new_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
enforce_elementwise_convergence=enforce_elementwise_convergence,
)
if new_unitcell[0, 0][0][0].chi > unitcell[0, 0][0][0].chi:
tmp_value = current_value
tmp_unitcell = unitcell
tmp_gradient = gradient
tmp_descent_direction = descent_direction
if (
cache_original_unitcell.get(new_unitcell[0, 0][0][0].chi)
is not None
):
(
unitcell,
gradient,
descent_direction,
current_value,
) = cache_original_unitcell[new_unitcell[0, 0][0][0].chi]
else:
unitcell = unitcell.change_chi(new_unitcell[0, 0][0][0].chi)
debug_print(
"Line search: Recalculate original unitcell with higher chi {}.",
new_unitcell[0, 0][0][0].chi,
)
if varipeps_config.ad_use_custom_vjp:
(
current_value,
(unitcell, max_trunc_error),
), tmp_gradient_seq = calc_ctmrg_expectation_custom_value_and_grad(
input_tensors,
unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
)
else:
(
current_value,
(unitcell, max_trunc_error),
), tmp_gradient_seq = calc_preconverged_ctmrg_value_and_grad(
input_tensors,
unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
calc_preconverged=True,
)
gradient = [elem.conj() for elem in tmp_gradient_seq]
descent_direction = [-elem for elem in tmp_gradient]
cache_original_unitcell[new_unitcell[0, 0][0][0].chi] = (
unitcell,
gradient,
descent_direction,
current_value,
)
signal_reset_descent_dir = True
except CTMRGNotConvergedError:
new_value = jnp.inf
elif (
varipeps_config.line_search_method is Line_Search_Methods.WOLFE
or varipeps_config.line_search_method is Line_Search_Methods.HAGERZHANG
):
wolfe_value_last_step = new_value
try:
if varipeps_config.ad_use_custom_vjp:
(
new_value,
(new_unitcell, max_trunc_error),
), new_gradient_seq = calc_ctmrg_expectation_custom_value_and_grad(
new_tensors,
new_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
)
else:
(
new_value,
(new_unitcell, max_trunc_error),
), new_gradient_seq = calc_preconverged_ctmrg_value_and_grad(
new_tensors,
new_unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
calc_preconverged=True,
)
new_gradient = [elem.conj() for elem in new_gradient_seq]
if new_unitcell[0, 0][0][0].chi > unitcell[0, 0][0][0].chi:
tmp_value = current_value
tmp_unitcell = unitcell
tmp_gradient = gradient
tmp_descent_direction = descent_direction
if (
cache_original_unitcell.get(new_unitcell[0, 0][0][0].chi)
is not None
):
(
unitcell,
gradient,
descent_direction,
current_value,
) = cache_original_unitcell[new_unitcell[0, 0][0][0].chi]
else:
unitcell = unitcell.change_chi(new_unitcell[0, 0][0][0].chi)
debug_print(
"Line search: Recalculate original unitcell with higher chi {}.",
new_unitcell[0, 0][0][0].chi,
)
if varipeps_config.ad_use_custom_vjp:
(
current_value,
(unitcell, max_trunc_error),
), tmp_gradient_seq = calc_ctmrg_expectation_custom_value_and_grad(
input_tensors,
unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
)
else:
(
current_value,
(unitcell, max_trunc_error),
), tmp_gradient_seq = calc_preconverged_ctmrg_value_and_grad(
input_tensors,
unitcell,
expectation_func,
convert_to_unitcell_func,
additional_input,
calc_preconverged=True,
)
gradient = [elem.conj() for elem in tmp_gradient_seq]
descent_direction = [-elem for elem in tmp_gradient]
cache_original_unitcell[new_unitcell[0, 0][0][0].chi] = (
unitcell,
gradient,
descent_direction,
current_value,
)
signal_reset_descent_dir = True
except (CTMRGNotConvergedError, CTMRGGradientNotConvergedError):
new_value = jnp.inf
new_gradient = gradient
else:
raise ValueError("Unknown line search method.")
if varipeps_config.line_search_method is Line_Search_Methods.HAGERZHANG:
descent_new_grad = _scalar_descent_grad(descent_direction, new_gradient)
hz_wolfe_1_left = (
varipeps_config.line_search_hager_zhang_delta * hager_zhang_descent_grad
)
hz_wolfe_1_right = (new_value - current_value) / alpha
hz_wolfe_2_right = (
varipeps_config.line_search_hager_zhang_sigma * hager_zhang_descent_grad
)
if descent_new_grad >= hz_wolfe_2_right:
if hz_wolfe_1_left >= hz_wolfe_1_right and new_value <= (
current_value + hager_zhang_eps
):
break
hz_approx_wolfe_left = (
2 * varipeps_config.line_search_hager_zhang_delta - 1
) * hager_zhang_descent_grad
if hz_approx_wolfe_left >= hager_zhang_descent_grad and new_value <= (
current_value + hager_zhang_eps
):
break
if (
varipeps_config.line_search_method is Line_Search_Methods.HAGERZHANG
and hager_zhang_initial_found is not _Hager_Zhang_Initial_State.FOUND
):
if (
hager_zhang_initial_found
is _Hager_Zhang_Initial_State.SCALAR_LOWER_VALUE_GREATER
):
if descent_new_grad >= 0:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = descent_new_grad
hager_zhang_initial_found = _Hager_Zhang_Initial_State.FOUND
elif new_value <= (current_value + hager_zhang_eps):
hager_zhang_lower_bound = alpha
hager_zhang_lower_bound_value = new_value
hager_zhang_lower_bound_grad = new_gradient
hager_zhang_lower_bound_des_grad = descent_new_grad
alpha = (
(1 - varipeps_config.line_search_hager_zhang_theta)
* hager_zhang_lower_bound
+ varipeps_config.line_search_hager_zhang_theta
* hager_zhang_upper_bound
)
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
else:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = descent_new_grad
alpha = (
(1 - varipeps_config.line_search_hager_zhang_theta)
* hager_zhang_lower_bound
+ varipeps_config.line_search_hager_zhang_theta
* hager_zhang_upper_bound
)
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
elif descent_new_grad >= 0:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = descent_new_grad
hager_zhang_initial_found = _Hager_Zhang_Initial_State.FOUND
elif descent_new_grad < 0 and new_value > (current_value + hager_zhang_eps):
alpha = varipeps_config.line_search_hager_zhang_theta * alpha
hager_zhang_initial_found = (
_Hager_Zhang_Initial_State.SCALAR_LOWER_VALUE_GREATER
)
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
else:
if new_value <= (current_value + hager_zhang_eps):
hager_zhang_lower_bound = alpha
hager_zhang_lower_bound_value = new_value
hager_zhang_lower_bound_grad = new_gradient
hager_zhang_lower_bound_des_grad = descent_new_grad
alpha *= varipeps_config.line_search_hager_zhang_rho
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
if varipeps_config.line_search_method is Line_Search_Methods.SIMPLE:
smaller_value_found = (
new_value <= current_value or (new_value - current_value) <= 1e-13
)
elif varipeps_config.line_search_method is Line_Search_Methods.ARMIJO:
cmp_value = _armijo_value(
current_value,
descent_direction,
gradient,
alpha,
varipeps_config.line_search_armijo_const,
)
smaller_value_found = (
new_value <= cmp_value or (new_value - cmp_value) <= 1e-13
)
elif varipeps_config.line_search_method is Line_Search_Methods.WOLFE:
wolfe_descent_last_grad = wolfe_descent_new_grad
(
cmp_value,
wolfe_left_side,
wolfe_right_side,
wolfe_descent_new_grad,
) = _wolfe_value(
current_value,
descent_direction,
gradient,
new_gradient,
alpha,
varipeps_config.line_search_armijo_const,
varipeps_config.line_search_wolfe_const,
)
wolfe_cond_1 = new_value <= cmp_value or (new_value - cmp_value) <= 1e-13
wolfe_cond_2 = (
wolfe_left_side <= wolfe_right_side
or (wolfe_left_side - wolfe_right_side) <= 1e-13
)
if (
varipeps_config.line_search_method is Line_Search_Methods.SIMPLE
or varipeps_config.line_search_method is Line_Search_Methods.ARMIJO
):
if smaller_value_found:
if (
alpha >= varipeps_config.line_search_initial_step_size
or incrementation_not_helped
):
break
has_been_increased = True
alpha /= varipeps_config.line_search_reduction_factor
else:
if has_been_increased:
incrementation_not_helped = True
alpha = varipeps_config.line_search_reduction_factor * alpha
elif varipeps_config.line_search_method is Line_Search_Methods.WOLFE:
if wolfe_upper_bound is None and wolfe_lower_bound is None:
if jnp.isinf(new_value):
alpha /= varipeps_config.line_search_reduction_factor
elif not wolfe_cond_1 or (
count > 0 and new_value >= wolfe_value_last_step
):
wolfe_lower_bound = wolfe_alpha_last_step
wolfe_lower_bound_value = wolfe_value_last_step
wolfe_upper_bound = alpha
wolfe_upper_bound_value = new_value
tmp_alpha = alpha
alpha = _wolfe_new_alpha(
alpha,
wolfe_alpha_last_step,
new_value,
wolfe_value_last_step,
wolfe_descent_new_grad,
wolfe_descent_last_grad,
jnp.fmin(wolfe_lower_bound, wolfe_upper_bound),
jnp.fmax(wolfe_lower_bound, wolfe_upper_bound),
)
wolfe_alpha_last_step = tmp_alpha
elif wolfe_cond_2:
break
elif wolfe_descent_new_grad >= 0:
wolfe_lower_bound = alpha
wolfe_lower_bound_value = new_value
wolfe_upper_bound = wolfe_alpha_last_step
wolfe_upper_bound_value = wolfe_value_last_step
tmp_alpha = alpha
alpha = _wolfe_new_alpha(
alpha,
wolfe_alpha_last_step,
new_value,
wolfe_value_last_step,
wolfe_descent_new_grad,
wolfe_descent_last_grad,
jnp.fmin(wolfe_lower_bound, wolfe_upper_bound),
jnp.fmax(wolfe_lower_bound, wolfe_upper_bound),
)
wolfe_alpha_last_step = tmp_alpha
else:
wolfe_alpha_last_step = alpha
alpha /= varipeps_config.line_search_reduction_factor
elif jnp.isinf(new_value):
alpha = alpha + (wolfe_upper_bound - alpha) / 2
else:
if new_value > cmp_value or new_value >= wolfe_lower_bound_value:
wolfe_upper_bound = alpha
wolfe_upper_bound_value = new_value
else:
if wolfe_cond_2:
break
if (
wolfe_descent_new_grad * (wolfe_upper_bound - wolfe_lower_bound)
>= 0
):
wolfe_upper_bound = wolfe_lower_bound
wolfe_upper_bound_value = wolfe_lower_bound_value
wolfe_lower_bound = alpha
wolfe_lower_bound_value = new_value
tmp_alpha = alpha
alpha = _wolfe_new_alpha(
alpha,
wolfe_alpha_last_step,
new_value,
wolfe_value_last_step,
wolfe_descent_new_grad,
wolfe_descent_last_grad,
jnp.fmin(wolfe_lower_bound, wolfe_upper_bound),
jnp.fmax(wolfe_lower_bound, wolfe_upper_bound),
)
wolfe_alpha_last_step = tmp_alpha
elif varipeps_config.line_search_method is Line_Search_Methods.HAGERZHANG:
if hager_zhang_state is _Hager_Zhang_State.UPDATE:
if descent_new_grad >= 0:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = descent_new_grad
hager_zhang_state = _Hager_Zhang_State.NONE
elif new_value <= (current_value + hager_zhang_eps):
hager_zhang_lower_bound = alpha
hager_zhang_lower_bound_value = new_value
hager_zhang_lower_bound_grad = new_gradient
hager_zhang_lower_bound_des_grad = descent_new_grad
hager_zhang_state = _Hager_Zhang_State.NONE
else:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = _scalar_descent_grad(
descent_direction, new_gradient
)
alpha = (
(1 - varipeps_config.line_search_hager_zhang_theta)
* hager_zhang_lower_bound
+ varipeps_config.line_search_hager_zhang_theta
* hager_zhang_upper_bound
)
hager_zhang_state = _Hager_Zhang_State.UPDATE_INNER
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
elif hager_zhang_state is _Hager_Zhang_State.UPDATE_INNER:
if descent_new_grad >= 0:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = descent_new_grad
hager_zhang_state = _Hager_Zhang_State.NONE
elif new_value <= (current_value + hager_zhang_eps):
hager_zhang_lower_bound = alpha
hager_zhang_lower_bound_value = new_value
hager_zhang_lower_bound_grad = new_gradient
hager_zhang_lower_bound_des_grad = descent_new_grad
alpha = (
(1 - varipeps_config.line_search_hager_zhang_theta)
* hager_zhang_lower_bound
+ varipeps_config.line_search_hager_zhang_theta
* hager_zhang_upper_bound
)
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
else:
hager_zhang_upper_bound = alpha
hager_zhang_upper_bound_value = new_value
hager_zhang_upper_bound_grad = new_gradient
hager_zhang_upper_bound_des_grad = _scalar_descent_grad(
descent_direction, new_gradient
)
alpha = (
(1 - varipeps_config.line_search_hager_zhang_theta)
* hager_zhang_lower_bound
+ varipeps_config.line_search_hager_zhang_theta
* hager_zhang_upper_bound
)
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
else:
alpha = hager_zhang_lower_bound * hager_zhang_upper_bound_des_grad
alpha -= hager_zhang_upper_bound * hager_zhang_lower_bound_des_grad
alpha /= (
hager_zhang_upper_bound_des_grad - hager_zhang_lower_bound_des_grad
)
if alpha <= 0:
tqdm.write("Found negative alpha in secant operation!")
hz_secant_alpha = alpha
hz_secant_lower = hager_zhang_lower_bound
hz_secant_lower_value = hager_zhang_lower_bound_value
hz_secant_lower_grad = hager_zhang_lower_bound_grad
hz_secant_lower_des_grad = hager_zhang_lower_bound_des_grad
hz_secant_upper = hager_zhang_upper_bound
hz_secant_upper_value = hager_zhang_upper_bound_value
hz_secant_upper_grad = hager_zhang_upper_bound_grad
hz_secant_upper_des_grad = hager_zhang_upper_bound_des_grad
if hager_zhang_lower_bound < alpha < hager_zhang_upper_bound:
hager_zhang_state = _Hager_Zhang_State.UPDATE
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
if hz_secant_alpha is not None and (
hz_secant_alpha == hager_zhang_lower_bound
or hz_secant_alpha == hager_zhang_upper_bound
):
if hz_secant_alpha == hager_zhang_lower_bound:
alpha = hz_secant_lower * hager_zhang_lower_bound_des_grad
alpha -= hager_zhang_lower_bound * hz_secant_lower_des_grad
alpha /= hager_zhang_lower_bound_des_grad - hz_secant_lower_des_grad
else:
alpha = hz_secant_upper * hager_zhang_upper_bound_des_grad
alpha -= hager_zhang_upper_bound * hz_secant_upper_des_grad
alpha /= hager_zhang_upper_bound_des_grad - hz_secant_upper_des_grad
hz_secant_alpha = None
if hager_zhang_lower_bound < alpha < hager_zhang_upper_bound:
hager_zhang_state = _Hager_Zhang_State.UPDATE
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
hz_secant_alpha = None
if hz_secant_lower is not None and (
(hager_zhang_upper_bound - hager_zhang_lower_bound)
> varipeps_config.line_search_hager_zhang_gamma
* (hz_secant_upper - hz_secant_lower)
):
alpha = (hager_zhang_lower_bound + hager_zhang_upper_bound) / 2
hz_secant_lower = None
hager_zhang_state = _Hager_Zhang_State.UPDATE
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
continue
hz_secant_lower = None
if tmp_value is not None:
current_value, unitcell, gradient, descent_direction = (
tmp_value,
tmp_unitcell,
tmp_gradient,
tmp_descent_direction,
)
tmp_value, tmp_unitcell, tmp_gradient, tmp_descent_direction = (
None,
None,
None,
None,
)
signal_reset_descent_dir = False
count += 1
if (
new_unitcell is not None
and new_unitcell[0, 0][0][0].chi != unitcell[0, 0][0][0].chi
):
jax.clear_caches()
if count == varipeps_config.line_search_max_steps:
raise NoSuitableStepSizeError(f"Count {count}, Last alpha {alpha}")
return (
new_tensors,
new_unitcell,
new_value,
alpha,
signal_reset_descent_dir,
max_trunc_error,
)
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