Remove rmatmul implementations

probabilistic-numerics · Nov 8, 2022 · 33d972c · 33d972c
1 parent 7b26800
commit 33d972c
Show file tree

Hide file tree

Showing 2 changed files with 0 additions and 60 deletions.
diff --git a/src/probnum/linops/_kronecker.py b/src/probnum/linops/_kronecker.py
@@ -136,7 +136,6 @@ def __init__(self, A: LinearOperatorLike, B: LinearOperatorLike):
                 self.A.shape[1] * self.B.shape[1],
             ),
             matmul=lambda x: _kronecker_matmul(self.A, self.B, x),
-            rmatmul=lambda x: _kronecker_rmatmul(self.A, self.B, x),
             todense=lambda: np.kron(
                 self.A.todense(cache=False), self.B.todense(cache=False)
             ),
@@ -260,28 +259,6 @@ def _kronecker_matmul(
     return y
 
 
-def _kronecker_rmatmul(
-    A: _linear_operator.LinearOperator,
-    B: _linear_operator.LinearOperator,
-    x: np.ndarray,
-) -> np.ndarray:
-    # Reshape into stack of matrices
-    y = x
-
-    if not y.flags.c_contiguous:
-        y = y.copy(order="C")
-
-    y = y.reshape(y.shape[:-1] + (A.shape[0], B.shape[0]))
-
-    # ((A.T) @ X) @ (B.T).T
-    y = (A.T @ y) @ B
-
-    # Revert to stack of vectorized matrices
-    y = y.reshape(y.shape[:-2] + (-1,))
-
-    return y
-
-
 class SymmetricKronecker(_linear_operator.LambdaLinearOperator):
     """Symmetric Kronecker product of two linear operators.
 
@@ -337,7 +314,6 @@ def __init__(
 
             dtype = self.A.dtype
             matmul = lambda x: _kronecker_matmul(self.A, self.A, x)
-            rmatmul = lambda x: _kronecker_rmatmul(self.A, self.A, x)
             todense = self._todense_identical_factors
             # (A (x)_s A)^T = A^T (x)_s A^T
             transpose = lambda: SymmetricKronecker(A=self.A.T)
@@ -357,7 +333,6 @@ def __init__(
 
             dtype = np.result_type(self.A.dtype, self.B.dtype, 0.5)
             matmul = self._matmul_different_factors
-            rmatmul = self._rmatmul_different_factors
             todense = self._todense_different_factors
             # (A (x)_s B)^T = A^T (x)_s B^T
             transpose = lambda: SymmetricKronecker(A=self.A.T, B=self.B.T)
@@ -371,7 +346,6 @@ def __init__(
             dtype=dtype,
             shape=2 * (self._n**2,),
             matmul=matmul,
-            rmatmul=rmatmul,
             todense=todense,
             transpose=transpose,
             inverse=inverse,
@@ -441,29 +415,6 @@ def _matmul_different_factors(self, x: np.ndarray) -> np.ndarray:
 
         return y
 
-    def _rmatmul_different_factors(self, x: np.ndarray) -> np.ndarray:
-        # Reshape into stack of matrices
-        y = x
-
-        if not y.flags.c_contiguous:
-            y = y.copy(order="C")
-
-        y = y.reshape(y.shape[:-1] + (self._n, self._n))
-
-        # (A.T) @ X @ (B.T).T
-        y1 = (self.A.T @ y) @ self.B
-
-        # (B.T) @ X @ (A.T).T
-        y2 = (self.B.T @ y) @ self.A
-
-        # 1/2 ((A^T)X(B^T)^T + (B^T)X(A^T)^T)
-        y = 0.5 * (y1 + y2)
-
-        # Revert to stack of vectorized matrices
-        y = y.reshape(y.shape[:-2] + (-1,))
-
-        return y
-
     def _todense_identical_factors(self) -> np.ndarray:
         """Dense representation of the symmetric Kronecker product."""
         # 1/2 (A (x) B + B (x) A)
@@ -533,9 +484,6 @@ def __init__(self, num_blocks: int, B: LinearOperatorLike):
             matmul=lambda x: _kronecker_matmul(
                 self.A, self.B, x
             ),  # TODO: can be implemented more efficiently
-            rmatmul=lambda x: _kronecker_rmatmul(
-                self.A, self.B, x
-            ),  # TODO: can be implemented more efficiently
             todense=lambda: np.kron(
                 self.A.todense(cache=False), self.B.todense(cache=False)
             ),

diff --git a/src/probnum/linops/_scaling.py b/src/probnum/linops/_scaling.py
@@ -76,9 +76,6 @@ def __init__(
                 matmul = lambda x: x.astype(
                     np.result_type(self.dtype, x.dtype), copy=False
                 )
-                rmatmul = lambda x: x.astype(
-                    np.result_type(self.dtype, x.dtype), copy=False
-                )
 
                 apply = lambda x, axis: x.astype(
                     np.result_type(self.dtype, x.dtype), copy=False
@@ -97,7 +94,6 @@ def __init__(
                 )
             else:
                 matmul = lambda x: self._scalar * x
-                rmatmul = lambda x: self._scalar * x
 
                 apply = lambda x, axis: self._scalar * x
 
@@ -129,7 +125,6 @@ def __init__(
             dtype = self._factors.dtype
 
             matmul = lambda x: self._factors[:, np.newaxis] * x
-            rmatmul = lambda x: self._factors * x
 
             apply = lambda x, axis: (
                 self._factors.reshape((-1,) + (x.ndim - (axis + 1)) * (1,)) * x
@@ -154,7 +149,6 @@ def __init__(
             shape,
             dtype,
             matmul=matmul,
-            rmatmul=rmatmul,
             apply=apply,
             todense=todense,
             transpose=lambda: self,
@@ -334,7 +328,6 @@ class Zero(_linear_operator.LambdaLinearOperator):
     def __init__(self, shape, dtype=np.float64):
 
         matmul = lambda x: np.zeros(x.shape, np.result_type(x, self.dtype))
-        rmatmul = lambda x: np.zeros(x.shape, np.result_type(x, self.dtype))
         apply = lambda x, axis: np.zeros(x.shape, np.result_type(x, self.dtype))
         todense = lambda: np.zeros(shape=shape, dtype=dtype)
         rank = lambda: np.intp(0)
@@ -347,7 +340,6 @@ def __init__(self, shape, dtype=np.float64):
             shape,
             dtype=dtype,
             matmul=matmul,
-            rmatmul=rmatmul,
             apply=apply,
             todense=todense,
             transpose=lambda: self,