Given the two strictly convex (unique solution) optimization problems as:

$$Problem\:1:\min_{X} f(X)+\|X\|_{F}^2 \hspace{2cm}Problem \:2:\min_{X}f(X)+n\|X\|_2^2$$

where $X\in\mathbf{S}_{++}^{n}$ (being positive definite), $f(X)$ is strictly convex, $\|\cdot\|_{F}$ is the frobenius norm, and $\|\cdot\|_2$ is the spectral norm. Let $X_1$ and $X_2$ denote the solutions to problem 1 and 2, respectively.

Question: Prove that $f(X_1)\leq f(X_2)$ (or please provide a counterexample if you believe that this does not always hold). I highly appreciate any help or suggestions. Thanks.