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Neel Shah
NIFTy
Commits
24bf4f39
Commit
24bf4f39
authored
Jun 03, 2021
by
Philipp Arras
Browse files
Docs
parent
6be924b0
Changes
2
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Inline
Sidebyside
src/minimization/energy_adapter.py
View file @
24bf4f39
...
...
@@ 92,6 +92,20 @@ class EnergyAdapter(Energy):
class
StochasticEnergyAdapter
(
Energy
):
"""Provide the energy interface for an energy operator where parts of the
input are averaged instead of optimized.
Specifically, a set of standard normal distributed samples are drawn for
the input corresponding to `keys` and each sample is inserted partially
into `op`. The resulting operators are then averaged. The subdomain that
is not sampled is left a stochastic average of an energy with the remaining
subdomain being the DOFs that are considered to be optimization parameters.
Notes

`StochasticEnergyAdapter` should never be created using the constructor,
but rather via the factory function :attr:`make`.
"""
def
__init__
(
self
,
position
,
op
,
keys
,
local_ops
,
n_samples
,
comm
,
nanisinf
,
_callingfrommake
=
False
):
if
not
_callingfrommake
:
...
...
@@ 148,17 +162,9 @@ class StochasticEnergyAdapter(Energy):
@
staticmethod
def
make
(
position
,
op
,
sampling_keys
,
n_samples
,
mirror_samples
,
comm
=
None
,
nanisinf
=
False
):
"""A variant of `EnergyAdapter` that provides the energy interface for
an operator with a scalar target where parts of the imput are averaged
instead of optmized.
Specifically, a set of standart normal distributed
samples are drawn for the input corresponding to `keys` and each sample
gets partially inserted into `op`. The resulting operators are averaged
and represent a stochastic average of an energy with the remaining
subdomain being the DOFs that are considered to be optimization parameters.
comm
=
None
,
nanisinf
=
False
):
"""Factory function for StochasticEnergyAdapter.
Parameters

position : MultiField
...
...
@@ 181,10 +187,9 @@ class StochasticEnergyAdapter(Energy):
across this communicator. If `mirror_samples` is set, then a sample
and its mirror image will always reside on the same task.
nanisinf : bool
If true, nan energies which can happen due to overflows in the
forward model are interpreted as inf. Thereby, the code does not
crash on these occasions but rather the minimizer is told that the
position it has tried is not sensible.
If true, nan energies, which can occur due to overflows in the
forward model, are interpreted as inf which can be interpreted by
optimizers.
"""
myassert
(
op
.
target
==
DomainTuple
.
scalar_domain
())
samdom
=
{}
...
...
src/minimization/stochastic_minimizer.py
View file @
24bf4f39
...
...
@@ 18,6 +18,7 @@
from
.minimizer
import
Minimizer
from
.energy
import
Energy
class
ADVIOptimizer
(
Minimizer
):
"""Provide an implementation of an adaptive stepsize sequence optimizer,
following https://arxiv.org/abs/1603.00788.
...
...
@@ 48,11 +49,8 @@ class ADVIOptimizer(Minimizer):
def
_step
(
self
,
position
,
gradient
):
self
.
s
=
self
.
alpha
*
gradient
**
2
+
(
1

self
.
alpha
)
*
self
.
s
self
.
rho
=
(
self
.
eta
*
self
.
counter
**
(

0.5
+
self
.
epsilon
)
/
(
self
.
tau
+
(
self
.
s
).
sqrt
())
)
self
.
rho
=
self
.
eta
*
self
.
counter
**
(

0.5
+
self
.
epsilon
)
\
/
(
self
.
tau
+
(
self
.
s
).
sqrt
())
new_position
=
position

self
.
rho
*
gradient
self
.
counter
+=
1
return
new_position
...
...
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