A positive semidefinite matrix is a symmetric matrix where all eigenvalues are non-negative, ensuring that it does not invert the sign of any vector it multiplies in quadratic forms. This property makes it crucial in optimization problems, statistics, and quantum mechanics, where it often represents covariance matrices, Gram matrices, and density matrices, respectively.