A matrix is called positive semidefinite if it is symmetric and for any vector $$x$$, the quadratic form $$x^T A x \geq 0$$, where $$A$$ is the matrix. This property is significant because it ensures that the function associated with the matrix does not curve downward, which is a key characteristic of convex functions.
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