By Kedlaya K.S.

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The bad news about this criterion is that determining whether a matrix is positive definite is not a priori an easy task: one cannot check M x · x ≥ 0 for every vector, so it seems one must compute all of the eigenvalues of M , which can be quite a headache. The good news is that there is a very nice criterion for positive definiteness of a symmetric matrix, due to Sylvester, that saves a lot of work. Theorem 28 (Sylvester’s criterion). An n × n symmetric matrix of real numbers is positive definite if and only if the determinant of the upper left k × k submatrix is positive for k = 1, .

We say f is continuous at x if limt→0 f (x + t) = f (x). If y is any vector and x is in the domain of f , we say the directional derivative of f along x in the direction y exists and equals fy (x) if f (x + ty) − f (x) fy (x) = lim . t→0 t If f is written as a function of variables x1 , . . , xn , we call the directional derivative along the i-th standard basis vector the partial derivative of f with respect to i and denote it by ∂f . In other words, the partial derivative is the derivative of f as a function of x i along, ∂xi regarding the other variables as constants.

We say f is continuous at x if limt→0 f (x + t) = f (x). If y is any vector and x is in the domain of f , we say the directional derivative of f along x in the direction y exists and equals fy (x) if f (x + ty) − f (x) fy (x) = lim . t→0 t If f is written as a function of variables x1 , . . , xn , we call the directional derivative along the i-th standard basis vector the partial derivative of f with respect to i and denote it by ∂f . In other words, the partial derivative is the derivative of f as a function of x i along, ∂xi regarding the other variables as constants.