Adding symbolic definition of Cholesky gradient.

theano/tensor/slinalg.py

@@ -62,8 +62,27 @@ class Cholesky(Op):
         z[0] = scipy.linalg.cholesky(x, lower=self.lower).astype(x.dtype)
 
     def grad(self, inputs, gradients):
-        return [CholeskyGrad(self.lower)(inputs[0], self(inputs[0]),
-                                         gradients[0])]
+
+        x = inputs[0]
+        dz = gradients[0]
+        chol_x = self(x)
+        chol_x = theano.printing.Print('Cholesky:')(chol_x)
+
+        def tril_and_halve_diagonal(mtx):
+            """Extracts lower triangle of square matrix and halves diagonal."""
+            return tensor.tril(mtx) - tensor.diag(tensor.diagonal(mtx) / 2.)
+
+        def conjugate_solve_triangular(outer, inner):
+            """Computes P^{-T} Q P^{-1} for lower-triangular P."""
+            return solve_upper_triangular(
+                outer.T, solve_upper_triangular(outer.T, inner.T).T)
+
+        s = conjugate_solve_triangular(
+            chol_x, tril_and_halve_diagonal(chol_x.T.dot(dz)))
+
+        s = theano.printing.Print('S:')(s)
+
+        return [tensor.tril(s + s.T) - tensor.diag(tensor.diagonal(s))]
 
 cholesky = Cholesky()
 
@@ -194,8 +213,9 @@ class Solve(Op):
             return [(rows, cols)]
 
 solve = Solve()  # general solve
-
-# TODO : SolveTriangular
+# lower and upper triangular solves
+solve_lower_triangular = Solve(A_structure='lower_triangular', lower=True)
+solve_upper_triangular = Solve(A_structure='upper_triangular', lower=False)
 
 # TODO: Optimizations to replace multiplication by matrix inverse
 #      with solve() Op (still unwritten)