Feature/quadratic solve

docs/initializers/linear.rst

@@ -20,6 +20,7 @@ Low-rank Sinkhorn Initializers
 .. autosummary::
     :toctree: _autosummary
 
+    initializers_lr.LRInitializer
     initializers_lr.RandomInitializer
     initializers_lr.Rank2Initializer
     initializers_lr.KMeansInitializer

docs/problems/quadratic.rst

@@ -20,5 +20,6 @@ Costs
 .. autosummary::
     :toctree: _autosummary
 
+    quadratic_costs.GWLoss
     quadratic_costs.make_square_loss
     quadratic_costs.make_kl_loss

docs/solvers/quadratic.rst

@@ -12,7 +12,7 @@ Gromov-Wasserstein Solvers
 .. autosummary::
     :toctree: _autosummary
 
-    gromov_wasserstein.solve
+    solve
     gromov_wasserstein.GromovWasserstein
     gromov_wasserstein.GWOutput
     gromov_wasserstein_lr.LRGromovWasserstein

docs/tutorials/notebooks/gromov_wasserstein_multiomics.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "BB8VjJrVsuuG"
@@ -21,7 +20,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "BYAvtCwhsuuJ"
@@ -31,7 +29,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "pJXAdeCCsuuJ"
@@ -134,7 +131,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "m9uzbXpZsuuT"
@@ -187,7 +183,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "brfhPBHWsuuV"
@@ -214,7 +209,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "KtvRj6RosuuY"
@@ -224,7 +218,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "gmS0MXbwsuuZ"
@@ -234,7 +227,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "ZbiuslH0suua"
@@ -298,7 +290,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "LNp6t08Ysuuc"
@@ -347,7 +338,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "Lo-CPjPWsuue"
@@ -359,11 +349,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We provide the Average FOSCTTM to align `X` (chromatinaccessibility domain) to `Y` (gene expression domain) for each implementation:"
+    "We provide the Average FOSCTTM to align `X` (chromatin accessibility domain) to `Y` (gene expression domain) for each implementation:"
    ]
   },
   {
@@ -415,7 +404,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "UtcqjKXC26uG"
@@ -425,7 +413,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "svoMyh1dwPvg"
@@ -539,7 +526,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "ZruK0o4Gsuul"
@@ -4879,7 +4865,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "7y-DPRLCsuuo"
@@ -16909,7 +16894,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

src/ott/problems/quadratic/quadratic_costs.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 from typing import Callable, NamedTuple
 
-import jax
 import jax.numpy as jnp
+import jax.scipy as jsp
 
 __all__ = ["make_square_loss", "make_kl_loss"]
 
@@ -24,23 +24,54 @@ class Loss(NamedTuple):  # noqa: D101
   is_linear: bool
 
 
-class GWLoss(NamedTuple):  # noqa: D101
+class GWLoss(NamedTuple):
+  r"""Efficient decomposition of the Gromov-Wasserstein loss function.
+
+  The loss function :math:`L` is assumed to match the form given in eq. 5. of
+  :cite:`peyre:16`:
+
+  .. math::
+    L(x, y) = f_1(x) + f_2(y) - h_1(x) h_2(y)
+
+  Args:
+    f1: First linear term.
+    f2: Second linear term.
+    h1: First quadratic term.
+    h2: Second quadratic term.
+  """
   f1: Loss
   f2: Loss
   h1: Loss
   h2: Loss
 
 
-def make_square_loss() -> GWLoss:  # noqa: D103
+def make_square_loss() -> GWLoss:
+  """Squared Euclidean loss for Gromov-Wasserstein.
+
+  See Prop. 1 and Remark 1 of :cite:`peyre:16` for more information.
+
+  Returns:
+    The squared Euclidean loss.
+  """
   f1 = Loss(lambda x: x ** 2, is_linear=False)
   f2 = Loss(lambda y: y ** 2, is_linear=False)
   h1 = Loss(lambda x: x, is_linear=True)
   h2 = Loss(lambda y: 2.0 * y, is_linear=True)
   return GWLoss(f1, f2, h1, h2)
 
 
-def make_kl_loss(clipping_value: float = 1e-8) -> GWLoss:  # noqa: D103
-  f1 = Loss(lambda x: -jax.scipy.special.entr(x) - x, is_linear=False)
+def make_kl_loss(clipping_value: float = 1e-8) -> GWLoss:
+  r"""Kullback-Leibler loss for Gromov-Wasserstein.
+
+  See Prop. 1 and Remark 1 of :cite:`peyre:16` for more information.
+
+  Args:
+    clipping_value: Value used to avoid :math:`\log(0)`.
+
+  Returns:
+    The KL loss.
+  """
+  f1 = Loss(lambda x: -jsp.special.entr(x) - x, is_linear=False)
   f2 = Loss(lambda y: y, is_linear=True)
   h1 = Loss(lambda x: x, is_linear=True)
   h2 = Loss(lambda y: jnp.log(jnp.clip(y, clipping_value)), is_linear=False)

src/ott/problems/quadratic/quadratic_problem.py

@@ -41,18 +41,16 @@ class QuadraticProblem:
   is assumed to match the form given in eq. 5., with our notations:
 
   .. math::
-
-    L(x, y) = lin1(x) + lin2(y) - quad1(x) * quad2(y)
+    L(x, y) = f_1(x) + f_2(y) - h_1(x) h_2(y)
 
   Args:
     geom_xx: Ground geometry of the first space.
     geom_yy: Ground geometry of the second space.
     geom_xy: Geometry defining the linear penalty term for
-      Fused Gromov-Wasserstein. If `None`, the problem reduces to a plain
-      Gromov-Wasserstein problem.
-    fused_penalty: multiplier of the linear term in Fused Gromov-Wasserstein,
-      i.e. problem = purely quadratic + fused_penalty * linear problem.
-      Ignored if ``geom_xy`` is not specified.
+      fused Gromov-Wasserstein :cite:`vayer:19`. If :obj:`None`, the problem
+      reduces to a plain Gromov-Wasserstein problem :cite:`peyre:16`.
+    fused_penalty: Multiplier of the linear term in fused Gromov-Wasserstein,
+      i.e. ``problem = purely quadratic + fused_penalty * linear problem``.
     scale_cost: option to rescale the cost matrices:
 
       - if :obj:`True`, use the default for each geometry.
@@ -62,19 +60,14 @@ class QuadraticProblem:
         :class:`~ott.geometry.pointcloud.PointCloud`.
       - if :obj:`None`, do not scale the cost matrices.
 
-    a: array representing the probability weights of the samples
-      from ``geom_xx``. If `None`, it will be uniform.
-    b: array representing the probability weights of the samples
-      from ``geom_yy``. If `None`, it will be uniform.
-    loss: a 2-tuple of 2-tuples of Callable. The first tuple is the linear
-      part of the loss. The second one is the quadratic part (quad1, quad2).
-      By default, the loss is set as the 4 functions representing the squared
-      Euclidean loss, and this property is taken advantage of in subsequent
-      computations. Alternatively, KL loss can be specified in no less optimized
-      way.
-    tau_a: if `< 1.0`, defines how much unbalanced the problem is on
+    a: The first marginal. If :obj:`None`, it will be uniform.
+    b: The second marginal. If :obj:`None`, it will be uniform.
+    loss: Gromov-Wasserstein loss function, see
+      :class:`~ott.problems.quadratic.quadratic_costs.GWLoss` for more
+      information.
+    tau_a: If :math:`< 1.0`, defines how much unbalanced the problem is on
       the first marginal.
-    tau_b: if `< 1.0`, defines how much unbalanced the problem is on
+    tau_b: If :math:`< 1.0`, defines how much unbalanced the problem is on
       the second marginal.
     gw_unbalanced_correction: Whether the unbalanced version of
       :cite:`sejourne:21` is used. Otherwise, ``tau_a`` and ``tau_b``
@@ -101,8 +94,8 @@ def __init__(
       a: Optional[jnp.ndarray] = None,
       b: Optional[jnp.ndarray] = None,
       loss: Union[Literal["sqeucl", "kl"], quadratic_costs.GWLoss] = "sqeucl",
-      tau_a: Optional[float] = 1.0,
-      tau_b: Optional[float] = 1.0,
+      tau_a: float = 1.0,
+      tau_b: float = 1.0,
       gw_unbalanced_correction: bool = True,
       ranks: Union[int, Tuple[int, ...]] = -1,
       tolerances: Union[float, Tuple[float, ...]] = 1e-2,

src/ott/solvers/linear/_solve.py

@@ -1,11 +1,22 @@
+# Copyright OTT-JAX
+#
+# 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.
 from typing import Any, Optional, Union
 
 import jax.numpy as jnp
 
 from ott.geometry import geometry
 from ott.problems.linear import linear_problem
-
-#if TYPE_CHECKING:
 from ott.solvers.linear import sinkhorn, sinkhorn_lr
 
 __all__ = ["solve"]
@@ -23,7 +34,7 @@ def solve(
   """Solve linear regularized OT problem using Sinkhorn iterations.
 
   Args:
-    geom: The ground geometry cost of the linear problem.
+    geom: The ground geometry of the linear problem.
     a: The first marginal. If :obj:`None`, it will be uniform.
     b: The second marginal. If :obj:`None`, it will be uniform.
     tau_a: If :math:`< 1`, defines how much unbalanced the problem is
@@ -36,7 +47,7 @@ def solve(
     kwargs: Keyword arguments for
       :class:`~ott.solvers.linear.sinkhorn.Sinkhorn` or
       :class:`~ott.solvers.linear.sinkhorn_lr.LRSinkhorn`,
-      depending on ``rank``.
+      depending on the ``rank``.
 
   Returns:
     The Sinkhorn output.

src/ott/solvers/linear/sinkhorn_lr.py

@@ -11,7 +11,6 @@
 # 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.
-"""A Jax implementation of the Low-Rank Sinkhorn algorithm."""
 from typing import (
     Any,
     Callable,
@@ -272,7 +271,7 @@ def _inv_g(self) -> jnp.ndarray:
 
 @jax.tree_util.register_pytree_node_class
 class LRSinkhorn(sinkhorn.Sinkhorn):
-  r"""A Low-Rank Sinkhorn solver for linear reg-OT problems.
+  r"""Low-Rank Sinkhorn solver for linear reg-OT problems.
 
   The algorithm is described in :cite:`scetbon:21` and the implementation
   contained here is adapted from `LOT <https://github.com/meyerscetbon/LOT>`_.
@@ -288,15 +287,12 @@ class LRSinkhorn(sinkhorn.Sinkhorn):
       described in :cite:`scetbon:22b`.
     epsilon: Entropic regularization added on top of low-rank problem.
     initializer: How to initialize the :math:`Q`, :math:`R` and :math:`g`
-      factors. Valid options are `'random'`, `'rank2'`, `'k-means'`, and
-      `'generalized-k-means'`.
+      factors.
     lse_mode: Whether to run computations in LSE or kernel mode.
     inner_iterations: Number of inner iterations used by the algorithm before
       re-evaluating progress.
     use_danskin: Use Danskin theorem to evaluate gradient of objective w.r.t.
       input parameters. Only `True` handled at this moment.
-    implicit_diff: Whether to use implicit differentiation. Currently, only
-      ``implicit_diff = False`` is implemented.
     progress_fn: callback function which gets called during the Sinkhorn
       iterations, so the user can display the error at each iteration,
       e.g., using a progress bar. See :func:`~ott.utils.default_progress_fn`
@@ -316,25 +312,23 @@ def __init__(
       rank: int,
       gamma: float = 10.,
       gamma_rescale: bool = True,
-      epsilon: float = 0.,
+      epsilon: float = 0.0,
       initializer: Union[Literal["random", "rank2", "k-means",
                                  "generalized-k-means"],
                          initializers_lr.LRInitializer] = "random",
       lse_mode: bool = True,
       inner_iterations: int = 10,
       use_danskin: bool = True,
-      implicit_diff: bool = False,
       kwargs_dys: Optional[Mapping[str, Any]] = None,
       kwargs_init: Optional[Mapping[str, Any]] = None,
       progress_fn: Optional[ProgressCallbackFn_t] = None,
       **kwargs: Any,
   ):
-    assert not implicit_diff, "Implicit diff. not yet implemented."
+    kwargs["implicit_diff"] = None  # not yet implemented
     super().__init__(
         lse_mode=lse_mode,
         inner_iterations=inner_iterations,
         use_danskin=use_danskin,
-        implicit_diff=implicit_diff,
         **kwargs
     )
     self.rank = rank

src/ott/solvers/quadratic/__init__.py

@@ -12,3 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from . import gromov_wasserstein, gromov_wasserstein_lr, gw_barycenter
+from ._solve import solve