Is it possible to have a basis for a covariance matrix such that the greatest variance is greater than the variance of the first eigenvector?

Suppose we have a covariance matrix $C$. Define the eigendecomposition, $C = Q^{-1} \Lambda Q$ and some other arbitrary basis $C = B^{-1} D B$.

Define $V_\text{PCA} = \text{diag}(QCQ^{-1})$ and $V_B = diag(BCB^{-1})$, and assume they're both sorted by magnitude. Define $T_\text{PCA} = \text{sum}(V_\text{PCA})$ and $T_B = \text{sum}(V_B)$. Is it possible that $V_\text{PCA}[i] / T_\text{PCA} < V_B[i] / T_B$ for $i = 0$? If so, how is this possible? Doesn't it violate the idea that PCA initially finds the basis vector with maximum variance?

I can understand how the inequality holds for $i > 0$, due to the orthogonality constraint of PCA which may not result in the "optimal" vectors being found in the variance-explained sense.

I'm trying to figure out if there's an error in my code, so please forgive the convoluted notation, but I want to be as clear as possible about what I'm seeing.

This is equivalent to computing the trace: $T_{PCA}=\mbox{tr}(QCQ^{-1})$ and $T_B=\mbox{tr}(BCB^{-1})$. By the rules of cyclic invariance $\mbox{tr}(ABC)=\mbox{tr}(BCA)$ (a fact you can easily derive by writing out the coefficients and summing the diagonal). So that $T_{PCA}=T_{B}=\mbox{tr}(C)$.