A matrix is considered positive definite if it is symmetric and all its eigenvalues are positive. This property ensures that the quadratic form associated with the matrix is always greater than zero for all non-zero vectors, which is crucial in optimization as it implies a local minimum at critical points. The positive definiteness of a matrix provides essential conditions for determining optimal solutions and helps formulate certain types of optimization problems effectively.
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