Use more iterations for LR-GW

src/ott/solvers/quadratic/gromov_wasserstein_lr.py

@@ -278,6 +278,9 @@ class LRGromovWasserstein(sinkhorn.Sinkhorn):
     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.
+    min_iterations: The minimum number of low-rank Sinkhorn iterations carried
+      out before the error is computed and monitored.
+    max_iterations: The maximum number of low-rank Sinkhorn iterations.
     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
@@ -305,9 +308,11 @@ def __init__(
                                  "generalized-k-means"],
                          initializers_lr.LRInitializer] = "random",
       lse_mode: bool = True,
-      inner_iterations: int = 10,
       use_danskin: bool = True,
       implicit_diff: bool = False,
+      inner_iterations: int = 2_000,
+      min_iterations: int = 10_000,
+      max_iterations: int = 100_000,
       kwargs_dys: Optional[Mapping[str, Any]] = None,
       kwargs_init: Optional[Mapping[str, Any]] = None,
       progress_fn: Optional[ProgressCallbackFn_t] = None,
@@ -317,6 +322,8 @@ def __init__(
     super().__init__(
         lse_mode=lse_mode,
         inner_iterations=inner_iterations,
+        min_iterations=min_iterations,
+        max_iterations=max_iterations,
         use_danskin=use_danskin,
         implicit_diff=implicit_diff,
         **kwargs

tests/initializers/quadratic/gw_init_test.py

@@ -40,7 +40,11 @@ def test_explicit_initializer_lr(self):
     rank = 10
     q_init = initializers_lr.Rank2Initializer(rank)
     solver = gromov_wasserstein_lr.LRGromovWasserstein(
-        rank=rank, initializer=q_init
+        rank=rank,
+        initializer=q_init,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
     )
 
     assert solver.create_initializer("not used") is q_init
@@ -67,11 +71,17 @@ def test_gw_better_initialization_helps(self, rng: jax.Array, eps: float):
         rank=rank,
         initializer="random",
         epsilon=eps,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
     )
     solver_kmeans = gromov_wasserstein_lr.LRGromovWasserstein(
         rank=rank,
         initializer="k-means",
         epsilon=eps,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
     )
 
     out_random = solver_random(problem)

tests/solvers/quadratic/fgw_test.py

@@ -227,7 +227,9 @@ def test_fgw_lr_memory(self, rng: jax.Array, jit: bool):
     geom_xy = pointcloud.PointCloud(xx, yy)
     prob = quadratic_problem.QuadraticProblem(geom_x, geom_y, geom_xy)
 
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=2)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=2, min_iterations=0, inner_iterations=10, max_iterations=2000
+    )
     if jit:
       solver = jax.jit(solver)
 
@@ -262,7 +264,13 @@ def test_fgw_lr_generic_cost_matrix(
     lr_prob = prob.to_low_rank()
     assert lr_prob.is_low_rank
 
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=5, epsilon=10.0)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=5,
+        epsilon=10.0,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
+    )
     out = solver(prob)
 
     assert solver.rank == 5

tests/solvers/quadratic/gw_test.py

@@ -210,7 +210,9 @@ def test_gw_pointcloud(self, balanced: bool, rank: int):
     )
     if rank > 0:
       solver = gromov_wasserstein_lr.LRGromovWasserstein(
-          rank=rank, epsilon=0.0, max_iterations=10
+          rank=rank,
+          epsilon=0.0,
+          max_iterations=10,
       )
     else:
       solver = gromov_wasserstein.GromovWasserstein(
@@ -324,7 +326,13 @@ def test_gw_lr(self, rng: jax.Array):
     geom_yy = pointcloud.PointCloud(y)
     prob = quadratic_problem.QuadraticProblem(geom_xx, geom_yy, a=a, b=b)
 
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=5, epsilon=0.2)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=5,
+        epsilon=0.2,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
+    )
     ot_gwlr = solver(prob)
     solver = gromov_wasserstein.GromovWasserstein(epsilon=0.2)
     ot_gw = solver(prob)
@@ -352,9 +360,17 @@ def test_gw_lr_matches_fused(self, rng: jax.Array):
         geom_xx, geom_yy, geom_xy=geom_xy, fused_penalty=1.3, a=a, b=b
     )
 
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=6)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=6, min_iterations=0, inner_iterations=10, max_iterations=2000
+    )
     ot_gwlr = solver(prob)
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=6, epsilon=1e-1)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=6,
+        epsilon=1e-1,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
+    )
     ot_gwlreps = solver(prob)
     solver = gromov_wasserstein.GromovWasserstein(epsilon=5e-2)
     ot_gw = solver(prob)
@@ -372,7 +388,13 @@ def test_gw_lr_apply(self, axis: int):
     prob = quadratic_problem.QuadraticProblem(
         geom_x, geom_y, a=self.a, b=self.b
     )
-    solver = gromov_wasserstein_lr.LRGromovWasserstein(rank=2, epsilon=1e-1)
+    solver = gromov_wasserstein_lr.LRGromovWasserstein(
+        rank=2,
+        epsilon=1e-1,
+        min_iterations=0,
+        inner_iterations=10,
+        max_iterations=2000
+    )
     out = solver(prob)
 
     arr, matrix = (self.x, out.matrix) if axis == 0 else (self.y, out.matrix.T)
@@ -430,7 +452,12 @@ def test_gwlr_unbalanced(
     )
     solver = jax.jit(
         gromov_wasserstein_lr.LRGromovWasserstein(
-            rank=4, epsilon=eps, kwargs_dys={"translation_invariant": ti}
+            rank=4,
+            epsilon=eps,
+            kwargs_dys={"translation_invariant": ti},
+            min_iterations=0,
+            inner_iterations=10,
+            max_iterations=2000
         )
     )
 
@@ -468,8 +495,9 @@ def test_gwlr_unbalanced_matches_balanced(
             rank=rank,
             epsilon=eps,
             initializer="random",
+            inner_iterations=50,
             min_iterations=50,
-            max_iterations=50
+            max_iterations=50,
         )
     )