Expose option to switch between sparse matrix representations

src/jaxls/_factor_graph.py

@@ -60,10 +60,24 @@ def solve(
         | ConjugateGradientConfig = "conjugate_gradient",
         trust_region: TrustRegionConfig | None = TrustRegionConfig(),
         termination: TerminationConfig = TerminationConfig(),
+        sparse_mode: Literal["blockrow", "coo", "csr"] = "blockrow",
         verbose: bool = True,
     ) -> VarValues:
         """Solve the nonlinear least squares problem using either Gauss-Newton
-        or Levenberg-Marquardt."""
+        or Levenberg-Marquardt.
+
+        Args:
+            initial_vals: Initial values for the variables. If None, default values will be used.
+            linear_solver: The linear solver to use.
+            trust_region: Configuration for Levenberg-Marquardt trust region.
+            termination: Configuration for termination criteria.
+            sparse_mode: The representation to use for sparse matrix
+                multiplication. Can be "blockrow", "coo", or "csr".
+            verbose: Whether to print verbose output during optimization.
+
+        Returns:
+            Optimized variable values.
+        """
         if initial_vals is None:
             initial_vals = VarValues.make(
                 var_type(ids) for var_type, ids in self.sorted_ids_from_var_type.items()
@@ -79,7 +93,12 @@ def solve(
             linear_solver = "conjugate_gradient"
 
         solver = NonlinearSolver(
-            linear_solver, trust_region, termination, conjugate_gradient_config, verbose
+            linear_solver,
+            trust_region,
+            termination,
+            conjugate_gradient_config,
+            sparse_mode,
+            verbose,
         )
         return solver.solve(graph=self, initial_vals=initial_vals)
 

src/jaxls/_solvers.py

@@ -16,7 +16,7 @@
     make_point_jacobi_precoditioner,
 )
 
-from ._sparse_matrices import BlockRowSparseMatrix, SparseCsrMatrix
+from ._sparse_matrices import BlockRowSparseMatrix, SparseCooMatrix, SparseCsrMatrix
 from ._variables import VarTypeOrdering, VarValues
 from .utils import jax_log
 
@@ -191,6 +191,7 @@ class NonlinearSolver:
     trust_region: TrustRegionConfig | None
     termination: TerminationConfig
     conjugate_gradient_config: ConjugateGradientConfig | None
+    sparse_mode: jdc.Static[Literal["blockrow", "coo", "csr"]]
     verbose: jdc.Static[bool]
 
     @jdc.jit
@@ -254,11 +255,30 @@ def step(
         )
 
         # linear_transpose() will return a tuple, with one element per primal.
-        A_multiply = A_blocksparse.multiply
-        AT_multiply_ = jax.linear_transpose(
-            A_multiply, jnp.zeros((A_blocksparse.shape[1],))
-        )
-        AT_multiply = lambda vec: AT_multiply_(vec)[0]
+        if self.sparse_mode == "blockrow":
+            A_multiply = A_blocksparse.multiply
+            AT_multiply_ = jax.linear_transpose(
+                A_multiply, jnp.zeros((A_blocksparse.shape[1],))
+            )
+            AT_multiply = lambda vec: AT_multiply_(vec)[0]
+        elif self.sparse_mode == "coo":
+            A_coo = SparseCooMatrix(
+                values=jac_values, coords=graph.jac_coords_coo
+            ).as_jax_bcoo()
+            AT_coo = A_coo.transpose()
+            A_multiply = lambda vec: A_coo @ vec
+            AT_multiply = lambda vec: AT_coo @ vec
+        elif self.sparse_mode == "csr":
+            A_csr = SparseCsrMatrix(
+                values=jac_values, coords=graph.jac_coords_csr
+            ).as_jax_bcsr()
+            A_multiply = lambda vec: A_csr @ vec
+            AT_multiply_ = jax.linear_transpose(
+                A_multiply, jnp.zeros((A_blocksparse.shape[1],))
+            )
+            AT_multiply = lambda vec: AT_multiply_(vec)[0]
+        else:
+            assert_never(self.sparse_mode)
 
         # Compute right-hand side of normal equation.
         ATb = -AT_multiply(state.residual_vector)

src/jaxls/_sparse_matrices.py

@@ -145,6 +145,14 @@ class SparseCsrMatrix:
     coords: SparseCsrCoordinates
     """Indices describing non-zero entries."""
 
+    def as_jax_bcsr(self) -> jax.experimental.sparse.BCSR:
+        return jax.experimental.sparse.BCSR(
+            args=(self.values, self.coords.indices, self.coords.indptr),
+            shape=self.coords.shape,
+            indices_sorted=True,
+            unique_indices=True,
+        )
+
 
 @jdc.pytree_dataclass
 class SparseCooCoordinates: