def _Underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=False)
# pylint: enable=protected-access
grad_b = linalg_ops.cholesky_solve(chol, math_ops.matmul(a, grad))
# Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
tmp = linalg_ops.cholesky_solve(chol, b)
a1 = math_ops.matmul(tmp, a, adjoint_a=True)
a1 = -math_ops.matmul(grad_b, a1)
a2 = grad - math_ops.matmul(a, grad_b, adjoint_a=True)
a2 = math_ops.matmul(tmp, a2, adjoint_b=True)
grad_a = a1 + a2
return (grad_a, grad_b, None)
def _Underdetermined(op, grad):
"""Gradients for the underdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the second
kind:
X = F(A, B) = A * (A*A^T + lambda*I)^{-1} * B
that (for lambda=0) solve the least squares problem
min ||X||_F subject to A*X = B.
"""
a = op.inputs[0]
b = op.inputs[1]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=False)
# pylint: enable=protected-access
grad_b = linalg_ops.cholesky_solve(chol, math_ops.matmul(a, grad))
# Temporary tmp = (A * A^T + lambda * I)^{-1} * B.
tmp = linalg_ops.cholesky_solve(chol, b)
a1 = math_ops.matmul(tmp, a, adjoint_a=True)
a1 = -math_ops.matmul(grad_b, a1)
a2 = grad - math_ops.matmul(a, grad_b, adjoint_a=True)
a2 = math_ops.matmul(tmp, a2, adjoint_b=True)
grad_a = a1 + a2
return (grad_a, grad_b, None)
def _Overdetermined(op, grad):
"""Gradients for the overdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the first
kind:
X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
which solve the least squares problem
min ||A * X - B||_F^2 + lambda ||X||_F^2.
"""
a = op.inputs[0]
b = op.inputs[1]
x = op.outputs[0]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=True)
# pylint: enable=protected-access
# Temporary z = (A^T * A + lambda * I)^{-1} * grad.
z = linalg_ops.cholesky_solve(chol, grad)
xzt = math_ops.matmul(x, z, adjoint_b=True)
zx_sym = xzt + array_ops.matrix_transpose(xzt)
grad_a = -math_ops.matmul(a, zx_sym) + math_ops.matmul(
b, z, adjoint_b=True)
grad_b = math_ops.matmul(a, z)
return (grad_a, grad_b, None)
def _Overdetermined(op, grad):
"""Gradients for the overdetermined case of MatrixSolveLs.
This is the backprop for the solution to the normal equations of the first
kind:
X = F(A, B) = (A^T * A + lambda * I)^{-1} * A^T * B
which solve the least squares problem
min ||A * X - B||_F^2 + lambda ||X||_F^2.
"""
a = op.inputs[0]
b = op.inputs[1]
x = op.outputs[0]
l2_regularizer = math_ops.cast(op.inputs[2], a.dtype.base_dtype)
# pylint: disable=protected-access
chol = linalg_ops._RegularizedGramianCholesky(
a, l2_regularizer=l2_regularizer, first_kind=True)
# pylint: enable=protected-access
# Temporary z = (A^T * A + lambda * I)^{-1} * grad.
z = linalg_ops.cholesky_solve(chol, grad)
xzt = math_ops.matmul(x, z, adjoint_b=True)
zx_sym = xzt + array_ops.matrix_transpose(xzt)
grad_a = -math_ops.matmul(a, zx_sym) + math_ops.matmul(b, z, adjoint_b=True)
grad_b = math_ops.matmul(a, z)
return (grad_a, grad_b, None)