Welcome to MedLibrary.org. For best results, we recommend beginning with the navigation links at the top of the page, which can guide you through our collection of over 14,000 medication labels and package inserts. For additional information on other topics which are not covered by our database of medications, just enter your topic in the search box below:
In mathematics, the Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a function. It describes the local curvature of a function of many variables. The Hessian matrix was developed in the 19th century by the German mathematician Ludwig Otto Hesse and later named after him. Hesse originally used the term "functional determinants".
Given the real-valued function
if all second partial derivatives of f exist and are continuous over the domain of the function, then the Hessian matrix of f is
where x = (x1, x2, ..., xn) and Di is the differentiation operator with respect to the ith argument. Thus
Because f is often clear from context, is frequently abbreviated to .
The Hessian matrix is related to the Jacobian matrix by = .
where J is the Jacobian matrix, which is a vector (the gradient) for scalar-valued functions. The full Hessian matrix can be difficult to compute in practice; in such situations, quasi-Newton algorithms have been developed that use approximations to the Hessian. The best-known quasi-Newton algorithm is the BFGS algorithm.
Mixed derivatives and symmetry of the Hessian
This can also be written
Critical points and discriminant
If the gradient of f (i.e. its derivative in the vector sense) is zero at some point x, then f has a critical point (or stationary point) at x. The determinant of the Hessian at x is then called the discriminant. If this determinant is zero then x is called a degenerate critical point of f, this is also called a non-Morse critical point of f. Otherwise it is non-degenerate, this is called a Morse critical point of f.
Second derivative test
The following test can be applied at a non-degenerate critical point x. If the Hessian is positive definite at x, then f attains a local minimum at x. If the Hessian is negative definite at x, then f attains a local maximum at x. If the Hessian has both positive and negative eigenvalues then x is a saddle point for f (this is true even if x is degenerate). Otherwise the test is inconclusive.
Note that for positive semidefinite and negative semidefinite Hessians the test is inconclusive (yet a conclusion can be made that f is locally convex or concave respectively). However, more can be said from the point of view of Morse theory.
The second derivative test for functions of one and two variables is simple. In one variable, the Hessian contains just one second derivative; if it is positive then x is a local minimum, and if it is negative then x is a local maximum; if it is zero then the test is inconclusive. In two variables, the determinant can be used, because the determinant is the product of the eigenvalues. If it is positive then the eigenvalues are both positive, or both negative. If it is negative then the two eigenvalues have different signs. If it is zero, then the second derivative test is inconclusive.
More generally, the second-order conditions that are sufficient for a local minimum or maximum can be expressed in terms of the sequence of principal (upper-leftmost) minors (determinants of sub-matrices) of the Hessian; these conditions are a special case of those given in the next section for bordered Hessians for constrained optimization—the case in which the number of constraints is zero.
A bordered Hessian is used for the second-derivative test in certain constrained optimization problems. Given the function as before:
but adding a constraint function such that:
the bordered Hessian appears as
If there are, say, m constraints then the zero in the north-west corner is an m × m block of zeroes, and there are m border rows at the top and m border columns at the left.
The above rules stating that extrema are characterized by a positive definite or negative definite Hessian cannot apply here since a bordered Hessian cannot be definite: we have z'Hz = 0 if vector z has a non-zero as its first element, followed by zeroes.
The second derivative test consists here of sign restrictions of the determinants of a certain set of n - m submatrices of the bordered Hessian. Intuitively, one can think of the m constraints as reducing the problem to one with n - m free variables. (For example, the maximization of subject to the constraint can be reduced to the maximization of without constraint.)
Specifically, sign conditions are imposed on the sequence of principal minors (determinants of upper-left-justified sub-matrices) of the bordered Hessian, the smallest minor consisting of the truncated first m+1 rows and columns, the next consisting of the truncated first m+2 rows and columns, and so on, with the last being the entire bordered Hessian. There are thus n–m minors to consider. A sufficient condition for a local maximum is that these minors alternate in sign with the largest one having the sign of (–1)m+1. A sufficient condition for a local minimum is that all of these minors have the sign of (–1)m. (In the unconstrained case of m=0 these conditions coincide with the conditions for the unbordered Hessian to be negative definite or positive definite respectively.)
If f is instead a function from , i.e.
then the array of second partial derivatives is not a two-dimensional matrix of size , but rather a tensor of order 3. This can be thought of as a multi-dimensional array with dimensions , which degenerates to the usual Hessian matrix for .
Generalizations to Riemannian manifolds
- by ,
where we have taken advantage of the first covariant derivative of a function being the same as its ordinary derivative. Choosing local coordinates we obtain the local expression for the Hessian as
where are the Christoffel symbols of the connection. Other equivalent forms for the Hessian are given by
- and .
- The determinant of the Hessian matrix is a covariant; see Invariant of a binary form
- Polarization identity, useful for rapid calculations involving Hessians.
- Jacobian matrix
- The Hessian matrix is commonly used for expressing image processing operators in image processing and computer vision (see the Laplacian of Gaussian (LoG) blob detector, the determinant of Hessian (DoH) blob detector and scale space).
- Binmore; Davies (2007). Calculus Concepts and Methods. Cambridge University Press. p. 190.
- Neudecker, Heinz; Magnus, Jan R. (1988), Matrix differential calculus with applications in statistics and econometrics, New York: John Wiley & Sons, ISBN 978-0-471-91516-4 [Amazon-US | Amazon-UK], page 136
- Chiang, Alpha C., Fundamental Methods of Mathematical Economics, McGraw-Hill, third edition, 1984: p. 386.