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If the Hessian has both positive and negative eigenvalues, then is a saddle point for . Otherwise the test is inconclusive. This implies that at a local minimum the Hessian is positive-semidefinite, and at a local maximum the Hessian is negative-semidefinite.
Positive-definite and positive-semidefinite real matrices are at the basis of convex optimization, since, given a function of several real variables that is twice differentiable, then if its Hessian matrix (matrix of its second partial derivatives) is positive-definite at a point , then the function is convex near p, and, conversely, if the ...
The following test can be applied at any critical point a for which the Hessian matrix is invertible: If the Hessian is positive definite (equivalently, has all eigenvalues positive) at a, then f attains a local minimum at a. If the Hessian is negative definite (equivalently, has all eigenvalues negative) at a, then f attains a local maximum at a.
In mathematics, positive semidefinite may refer to: Positive semidefinite function; Positive semidefinite matrix; Positive semidefinite quadratic form;
In mathematics, Sylvester’s criterion is a necessary and sufficient criterion to determine whether a Hermitian matrix is positive-definite. Sylvester's criterion states that a n × n Hermitian matrix M is positive-definite if and only if all the following matrices have a positive determinant: the upper left 1-by-1 corner of M,
One can, for example, modify the Hessian by adding a correction matrix so as to make ″ + positive definite. One approach is to diagonalize the Hessian and choose B k {\displaystyle B_{k}} so that f ″ ( x k ) + B k {\displaystyle f''(x_{k})+B_{k}} has the same eigenvectors as the Hessian, but with each negative eigenvalue replaced by ϵ > 0 ...
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If = then this means the Hessian is positive semidefinite (or if the domain is the real line, it means that ″ ()), which implies the function is convex, and perhaps strictly convex, but not strongly convex.