Lbfgs box

src/mrpro/algorithms/__init__.py

@@ -1,2 +1,4 @@
 from mrpro.algorithms._prewhiten_kspace import prewhiten_kspace
 from mrpro.algorithms._remove_readout_os import remove_readout_os
+from mrpro.algorithms._lbfgs import lbfgs
+from mrpro.algorithms._adam import adam

src/mrpro/algorithms/_adam.py

@@ -0,0 +1,103 @@
+"""ADAM for solving non-linear minimization problems."""
+
+# Copyright 2024 Physikalisch-Technische Bundesanstalt
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#       http://www.apache.org/licenses/LICENSE-2.0
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import torch
+from torch.optim import Adam
+
+from mrpro.operators import Operator
+
+
+def adam(
+    f: Operator[*tuple[torch.Tensor, ...], tuple[torch.Tensor]],
+    params: list,
+    max_iter: int,
+    lr: float = 1e-3,
+    betas: tuple[float, float] = (0.9, 0.999),
+    eps: float = 1e-8,
+    weight_decay: float = 0,
+    amsgrad: bool = False,
+    foreach: bool | None = None,
+    maximize: bool = False,
+    capturable: bool = False,
+    differentiable: bool = False,
+    fused: bool | None = None,
+) -> list[torch.Tensor]:
+    """Adam for non-linear minimization problems.
+
+    Parameters
+    ----------
+    f
+        scalar-valued function to be optimized
+    params
+        list of parameters to be optimized.
+        Note that these parameters will not be changed. Instead, we create a copy and
+        leave the initial values untouched.
+    lr, optional
+        learning rate, by default 1e-3
+    betas, optional
+        coefficients used for computing running averages of gradient and its square,
+        by default (0.9, 0.999)
+    eps, optional
+        term added to the denominator to improve numerical stability, by default 1e-8
+    weight_decay, optional
+        weight decay (L2 penalty), by default 0
+    amsgrad, optional
+        whether to use the AMSGrad variant of this algorithm from the paper
+        `On the Convergence of Adam and Beyond`, by default False
+    foreach, optional
+        whether `foreach` implementation of optimizer is used, by default None
+    maximize, optional
+        maximize the objective with respect to the params, instead of minimizing, by default False
+    capturable, optional
+        whether this instance is safe to capture in a CUDA graph. Passing True can impair ungraphed
+        performance, so if you don’t intend to graph capture this instance, leave it False, by default False
+    differentiable, optional
+        whether autograd should occur through the optimizer step in training. Otherwise, the step() function
+        runs in a torch.no_grad() context. Setting to True can impair performance, so leave it False if you
+        don’t intend to run autograd through this instance, by default False
+    fused, optional
+        whether the fused implementation (CUDA only) is used. Currently, torch.float64, torch.float32,
+        torch.float16, and torch.bfloat16 are supported., by default None
+
+    Returns
+    -------
+        list of optimized parameters
+    """
+
+    # define Adam routine
+    optim = Adam(
+        params=[p.detach().clone().requires_grad_(True) for p in params],
+        lr=lr,
+        betas=betas,
+        eps=eps,
+        weight_decay=weight_decay,
+        amsgrad=amsgrad,
+        foreach=foreach,
+        maximize=maximize,
+        capturable=capturable,
+        differentiable=differentiable,
+        fused=fused,
+    )
+
+    def closure():
+        optim.zero_grad()
+        (objective,) = f(*params)
+        objective.backward()
+        return objective
+
+    # run adam
+    for _ in range(max_iter):
+        optim.step(closure)
+
+    return params

src/mrpro/algorithms/_lbfgs.py

@@ -0,0 +1,94 @@
+"""LBFGS for solving non-linear minimization problems."""
+
+# Copyright 2024 Physikalisch-Technische Bundesanstalt
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#       http://www.apache.org/licenses/LICENSE-2.0
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+
+import torch
+from torch.optim import LBFGS
+
+from mrpro.operators import Operator
+
+
+def lbfgs(
+    f: Operator[*tuple[torch.Tensor, ...], tuple[torch.Tensor]],
+    params: list,
+    lr: float = 1.0,
+    max_iter: int = 100,
+    max_eval: int | None = 100,
+    tolerance_grad: float = 1e-07,
+    tolerance_change: float = 1e-09,
+    history_size: int = 10,
+    line_search_fn: str | None = 'strong_wolfe',
+) -> list[torch.Tensor]:
+    """LBFGS for non-linear minimization problems.
+
+    Parameters
+    ----------
+    f
+        scalar function to be minimized
+    params
+        list with parameters to be optimized.
+        Note that these parameters will not be changed. Instead, we create a copy and
+        leave the initial values untouched.
+    lr, optional
+        learning rate
+    max_iter, optional
+        maximal number of iterations, by default 100
+    max_eval, optional
+        maximal number of evaluations of f per optimization step,
+        by default 100
+    tolerance_grad, optional
+        termination tolerance on first order optimality,
+        by default 1e-07
+    tolerance_change, optional
+        termination tolerance on function value/parameter changes, by default 1e-09
+    history_size, optional
+        update history size, by default 10
+    line_search_fn, optional
+        line search algorithm, either ‘strong_wolfe’ or None,
+        by default "strong_wolfe"
+
+    Returns
+    -------
+        list of optimized parameters
+    """
+
+    # TODO: remove after new pytorch release;
+    if torch.tensor([torch.is_complex(p) for p in params]).any():
+        raise ValueError(
+            "at least one tensor in 'params' is complex-valued; \
+            \ncomplex-valued tensors will be allowed for lbfgs in future torch versions"
+        )
+
+    # define lbfgs routine
+    optim = LBFGS(
+        params=[p.detach().clone().requires_grad_(True) for p in params],
+        lr=lr,
+        history_size=history_size,
+        max_iter=max_iter,
+        max_eval=max_eval,
+        tolerance_grad=tolerance_grad,
+        tolerance_change=tolerance_change,
+        line_search_fn=line_search_fn,
+    )
+
+    def closure():
+        optim.zero_grad()
+        (objective,) = f(*params)
+        objective.backward()
+        return objective
+
+    # run lbfgs
+    optim.step(closure)
+
+    return params

src/mrpro/operators/_ConstraintsOp.py

@@ -0,0 +1,154 @@
+"""Operator enforcing constraints by variable transformations."""
+
+# Copyright 2024 Physikalisch-Technische Bundesanstalt
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#       http://www.apache.org/licenses/LICENSE-2.0
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+
+import torch
+import torch.nn.functional as F
+
+from mrpro.operators import Operator
+
+
+class ConstraintsOp(Operator[*tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]):
+    """Transformation to map real-valued tensors to certain ranges."""
+
+    def __init__(
+        self,
+        bounds: tuple[tuple[float | None, float | None], ...],
+        beta_sigmoid: float = 1.0,
+        beta_softplus: float = 1.0,
+    ) -> None:
+        super().__init__()
+
+        if beta_sigmoid <= 0:
+            raise ValueError(f'parameter beta_sigmoid must be greater than zero; given {beta_sigmoid}')
+        if beta_softplus <= 0:
+            raise ValueError(f'parameter beta_softplus must be greater than zero; given {beta_softplus}')
+
+        self.beta_sigmoid = beta_sigmoid
+        self.beta_softplus = beta_softplus
+
+        self.lower_bounds = [bound[0] for bound in bounds]
+        self.upper_bounds = [bound[1] for bound in bounds]
+
+        for lb, ub in bounds:
+            if lb is not None and ub is not None:
+                if torch.isnan(torch.tensor(lb)) or torch.isnan(torch.tensor(ub)):
+                    raise ValueError(' "nan" is not a valid lower or upper bound;' f'\nbound tuple {lb, ub} is invalid')
+
+                if lb >= ub:
+                    raise ValueError(
+                        'bounds should be ( (a1,b1), (a2,b2), ...) with ai < bi if neither ai or bi is None;'
+                        f'\nbound tuple {lb, ub} is invalid'
+                    )
+
+    @staticmethod
+    def sigmoid(x: torch.Tensor, beta: float = 1.0) -> torch.Tensor:
+        """Constraint x to be in the range given by 'bounds'."""
+
+        return F.sigmoid(beta * x)
+
+    @staticmethod
+    def sigmoid_inverse(x: torch.Tensor, beta: float = 1.0) -> torch.Tensor:
+        """Constraint x to be in the range given by 'bounds'."""
+
+        return torch.logit(x) / beta
+
+    @staticmethod
+    def softplus(x: torch.Tensor, beta: float = 1.0) -> torch.Tensor:
+        """Constrain x to be in (bound,infty)."""
+
+        return -(1 / beta) * torch.nn.functional.logsigmoid(-beta * x)
+
+    @staticmethod
+    def softplus_inverse(x: torch.Tensor, beta: float = 1.0) -> torch.Tensor:
+        """Inverse of 'softplus_transformation."""
+
+        return beta * x + torch.log(-torch.expm1(-beta * x))
+
+    def forward(self, *x: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        """Transform tensors to chosen range.
+
+        Parameters
+        ----------
+        x
+            tensors to be transformed
+
+        Returns
+        -------
+            tensors transformed to the range defined by the chosen bounds
+        """
+        # iterate over the tensors and constrain them if necessary according to the
+        # chosen bounds
+        xc = []
+        for i in range(len(self.lower_bounds)):
+            lb, ub = self.lower_bounds[i], self.upper_bounds[i]
+
+            # distiguish cases
+            if (lb is not None and not torch.isneginf(torch.tensor(lb))) and (
+                ub is not None and not torch.isposinf(torch.tensor(ub))
+            ):
+                # case (a,b) with a<b and a,b \in R
+                xc.append(lb + (ub - lb) * self.sigmoid(x[i], beta=self.beta_sigmoid))
+
+            elif lb is not None and (ub is None or torch.isposinf(torch.tensor(ub))):
+                # case (a,None); corresponds to (a, \infty)
+                xc.append(lb + self.softplus(x[i], beta=self.beta_softplus))
+
+            elif (lb is None or torch.isneginf(torch.tensor(lb))) and ub is not None:
+                # case (None,b); corresponds to (-\infty, b)
+                xc.append(ub - self.softplus(-x[i], beta=self.beta_softplus))
+            elif (lb is None or torch.isneginf(torch.tensor(lb))) and (ub is None or torch.isposinf(torch.tensor(ub))):
+                # case (None,None); corresponds to (-\infty, \infty), i.e. no transformation
+                xc.append(x[i])
+
+        return tuple(xc)
+
+    def inverse(self, *xc: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        """Reverses the variable transformation.
+
+        Parameters
+        ----------
+        xc
+            transformed tensors with values in the range defined by the bounds
+
+        Returns
+        -------
+            tensors in the domain with no bounds
+        """
+        # iterate over the tensors and constrain them if necessary according to the
+        # chosen bounds
+        x = []
+        for i in range(len(self.lower_bounds)):
+            lb, ub = self.lower_bounds[i], self.upper_bounds[i]
+
+            # distiguish cases
+            if (lb is not None and not torch.isneginf(torch.tensor(lb))) and (
+                ub is not None and not torch.isposinf(torch.tensor(ub))
+            ):
+
+                # case (a,b) with a<b and a,b \in R
+                x.append(self.sigmoid_inverse((xc[i] - lb) / (ub - lb), beta=self.beta_sigmoid))
+
+            elif lb is not None and (ub is None or torch.isposinf(torch.tensor(ub))):
+                # case (a,None); corresponds to (a, \infty)
+                x.append(self.softplus_inverse(xc[i] - lb, beta=self.beta_softplus))
+
+            elif (lb is None or torch.isneginf(torch.tensor(lb))) and ub is not None:
+                # case (None,b); corresponds to (-\infty, b)
+                x.append(-self.softplus_inverse(-(xc[i] - ub), beta=self.beta_softplus))
+            elif (lb is None or torch.isneginf(torch.tensor(lb))) and (ub is None or torch.isposinf(torch.tensor(ub))):
+                # case (None,None); corresponds to (-\infty, \infty), i.e. no transformation
+                x.append(xc[i])
+
+        return tuple(x)

src/mrpro/operators/__init__.py

@@ -1,4 +1,5 @@
 from mrpro.operators._Operator import Operator
+from mrpro.operators._ConstraintsOp import ConstraintsOp
 from mrpro.operators._SignalModel import SignalModel
 from mrpro.operators._LinearOperator import LinearOperator
 from mrpro.operators._SensitivityOp import SensitivityOp

src/mrpro/operators/functionals/__init__.py

@@ -0,0 +1 @@
+from mrpro.operators.functionals._mse_data_discrepancy import mse_data_discrepancy