A self-adjoint diagonally dominant square matrix $M$ with nonnegative diagonal is positive semi-definite. Does there exist a similar concept for integration kernels that define compact operators over, say, $L^2(\mathbb{R}^n)$?
Let me be more specific:
Suppose $M$ is a self-adjoint square matrix. $M$ is diagonally dominant if and only if, for all $i$, $$ |M_{ii}| \geq \sum_{j\neq i} |M_{ij}|. $$ $M$ is invertible. Suppose the diagonal satisfies $M_{ii}\geq 0$. Then $M$ is automatiaclly positive semidefinite. Thus, a simple criterion on the matrix elements determine whether $M$ is positive semidefinite -- a question which in general would involve computation of the whole spectrum of $M$.
Let us turn to integration kernels. Suppose $u:\mathbb{R}^n\times\mathbb{R}^n\rightarrow\mathbb{C}$ is (for simplicity assumed to be) a Schwartz function, such that we may define a compact operator $U$ over $L^2(\mathbb{R}^n)$ using $u$ as integration kernel, viz, $$ [U\phi](x) := \int_{\mathbb{R}^n} u(x,y)\phi(y) d^n y. $$ Assume $u(x,y) = \overline{u(y,x)}$ such that $U$ is self-adjoint. $u(x,y)$ is analogous to the matrix elements $M_{ij}$ of $M$.
Does there exist some sort of criterion for $u(x,y)$ analogous to $M$ being diagonally dominant, that guarantees $U\geq 0$? I.e., can we say something about whether $U$ is postive semidefinite by studying the behavior of $u(x,y)$ near the diagonal $x=y$? In general, positive semidefiniteness of $U$ is much harder to ascertain, i.e., we would have to study the spectrum of $U$.