# Proof that sum of k-eigenvalues is convex

I saw a post https://mathoverflow.net/questions/98367/a-sum-of-eigenvalues that said

It is well-known that $$\sum^{r}_{i = 1} \lambda_{i}(X)$$ is convex.

and I saw an explanation in Boyd and Vandenberghe - "Convex Optimization", question 3.26(a) that said

$$\sum^{k}_{i = 1} \lambda_{i}(X) = \sup\{\operatorname{tr}(V^{T}XV) \mid V^{T}V = I\}.$$

The variational characterization shows that $$f$$ is the pointwise supremum of a family of linear functions $$\operatorname{tr}(V^{T}XV )$$

is an explanation for why it is convex.

But I don't really understand how the sup is the sum of the first $$k$$ eigenvalues and how that makes it convex.

• What is, perhaps, missing is that $V$ is a $n\times k$ matrix.
– lcv
May 18 at 5:35

The supremum of any family of convex functions is convex. (proof)

Every linear function is convex.

Hence the supremum of a family of linear functions is convex.

Edit: To prove the supremum identity first note that $$V \in \mathbb R^{n \times k}$$ with $$V^TV=I$$ \begin{align} \textrm{tr}(V^TXV) &= \textrm{tr}(XVV^T)\\ &\le \lambda(X)^T \lambda(V V^T) \end{align} Now since $$(VV^T)^2 = VV^T$$ the only eigenvalues are $$0$$ and $$1$$. Also note that $$\textrm{tr}(VV^T) = \textrm{tr}(V^TV) = k$$ Hence $$VV^T$$ has $$k$$ eigenvalues that are 1 and the rest are zero. Combining this with the previous inequality yields, and noting that any sum of $$k$$ eigenvalues is less than the sum of the $$k$$ largest eigenvalues yields $$\textrm{tr}(V^TXV) \le \sum_{i=1}^k \lambda_i(X)$$.

Taking $$V = [v_1, \ldots v_k]$$ where $$v_j$$ is the normalised eigenvector corresponding to $$\lambda_j(X)$$ achieves the supremum.

• Yes, I understand that the sup of any family of convex functions is convex, I just don't understand by the sup is the first k eigenvalues May 18 at 3:39
• Does this also mean that $f(x) = \sum^{k}_{i} \lambda_{i}(g(x))$ is also convex May 18 at 14:35
• No, take $g(x) = -x^2$ and $n=1$. The sup identity still holds but the family of functions need not be convex. May 18 at 21:22