Ridge regression with shrinkage towards nonzero matrix Suppose I want to perform ridge-regularized linear regression, except that we shrink the coefficients to a nonzero matrix:
$$
W^* = \arg\min_W \|Y - X W \|^2_2 + \lambda\|W-W_0\|^2_2.
$$
However, I want to use a solver that expects to solve the standard ridge regression problem
$$
\min \|Y - X W \|^2_2 + \lambda\|W\|^2_2.
$$
Is there a way to reduce my problem to the standard form?
One option would obviously be to solve the original problem directly. Another option would be to perform the change of variables $W_\Delta = W - W_0$, which leads  to
$$
W^* = W_0 + \arg\min_{W_\Delta} \|(Y-XW_0) - X W_\Delta\|^2_2 + \lambda \|W_\Delta\|^2_2.
$$
But suppose I'm (for no good reason, honestly) stubborn and don't want to have to add back in $W_0$ to the argmin of the ridge regression objective. Is there a way to do this?

In addition to @shimao's answer, here's another approach (also assuming univariate $y \in R^{n}, X \in R^{n \times d}$ for simplicity). Using the identity derived by @shimao,
\begin{align}
\quad \|y - X w \|^2_2 + \lambda\|w-\mu\|^2_2
= w^T (X^TX + \lambda I)w - 2(y^TX + \lambda\mu^T)w + c,
\end{align}
we can also construct $\tilde{X}$ via Cholesky factorization, rather than concatenation. You find lower Cholesky factor $L$ of the following
$$
L L^T = X^T X + \lambda I_d. 
$$
Then $\tilde{X} = L^T$, a $d \times d$ upper triangular matrix. We want $\tilde{X}^T \tilde{y} = X^T y + \lambda \mu$, so we solve
$$
\tilde{y} = L^{-1} \Big(X^T y + \lambda \mu\Big)
$$
Since $L$ is lower-triangular, this can be obtained via forward substitution.
@shimao's solution is better for general usage, since it requires no Cholesky factorization or forward substitution.
