Definiteness of a matrix
In linear algebra, a symmetric real matrix is said to be positive definite if the scalar is strictly positive for every nonzero column vector of real numbers. Here denotes the transpose of .[1] When interpreting as the output of an operator, , that is acting on an input, , the property of positive definiteness implies that the output always has a positive inner product with the input, as often observed in physical processes.
More generally, a complex Hermitian matrix is said to be positive definite if the scalar is strictly positive for every nonzero column vector of complex numbers. Here denotes the conjugate transpose of . Note that is automatically real since is Hermitian.
Positive semidefinite matrices are defined similarly, except that the above scalars or must be positive or zero (i.e. nonnegative). Negative definite and negative semidefinite matrices are defined analogously. A matrix that is not positive semidefinite and not negative semidefinite is called indefinite.
The matrix is positive definite if and only if the bilinear form is positive definite (and similarly for a positive definite sesquilinear form in the complex case). This is a coordinate realization of an inner product on a vector space.[2]
Some authors use more general definitions of definiteness, including some nonsymmetric real matrices, or nonHermitian complex ones.
Definitions
In the following definitions, is the transpose of , is the conjugate transpose of and denotes the ndimensional zerovector.
Definitions for real matrices
A symmetric real matrix is said to be positive definite if for all nonzero in . Formally,
A symmetric real matrix is said to be positive semidefinite or nonnegative definite if for all in . Formally,
A symmetric real matrix is said to be negative definite if for all nonzero in . Formally,
A symmetric real matrix is said to be negative semidefinite or nonpositive definite if for all in . Formally,
A symmetric real matrix which is neither positive semidefinite nor negative semidefinite is called indefinite.
Definitions for complex matrices
The following definitions all involve the term . Notice that this is always a real number for any Hermitian square matrix .
A Hermitian complex matrix is said to be positive definite if for all nonzero in . Formally,
A Hermitian complex matrix is said to be positive semidefinite or nonnegative definite if for all in . Formally,
A Hermitian complex matrix is said to be negative definite if for all nonzero in . Formally,
A Hermitian complex matrix is said to be negative semidefinite or nonpositive definite if for all in . Formally,
A Hermitian complex matrix which is neither positive semidefinite nor negative semidefinite is called indefinite.
Consistency between real and complex definitions
Since every real matrix is also a complex matrix, the definitions of "definiteness" for the two classes must agree.
For complex matrices, the most common definition says that " is positive definite if and only if is real and positive for all nonzero complex column vectors ". This condition implies that is Hermitian (i.e. its transpose is equal to its conjugate). To see this, consider the matrices and , so that and . The matrices and are Hermitian, therefore and are individually real. If is real, then must be zero for all . Then is the zero matrix and , proving that is Hermitian.
By this definition, a positive definite real matrix is Hermitian, hence symmetric; and is positive for all nonzero real column vectors . However the last condition alone is not sufficient for to be positive definite. For example, if
then for any real vector with entries and we have , which is always positive if is not zero. However, if is the complex vector with entries and , one gets
which is not real. Therefore, is not positive definite.
On the other hand, for a symmetric real matrix , the condition " for all nonzero real vectors " does imply that is positive definite in the complex sense.
Notation
If a Hermitian matrix is positive semidefinite, one sometimes writes and if is positive definite one writes . To denote that is negative semidefinite one writes and to denote that is negative definite one writes .
The notion comes from functional analysis where positive semidefinite matrices define positive operators.
A common alternative notation is , , and for positive semidefinite and positive definite, negative semidefinite and negative definite matrices, respectively. This may be confusing, as sometimes nonnegative matrices respectively nonpositive matrices are also denoted in this way.
Examples
 The identity matrix is positive definite (and as such also positive semidefinite). It is a real symmetric matrix, and, for any nonzero column vector z with real entries a and b, one has
 .
Seen as a complex matrix, for any nonzero column vector z with complex entries a and b one has
 .
 The real symmetric matrix
is positive definite since for any nonzero column vector z with entries a, b and c, we have
 For any real invertible matrix , the product is a positive definite matrix. A simple proof is that for any nonzero vector , the condition since the invertibility of matrix means that
 The example above shows that a matrix in which some elements are negative may still be positive definite. Conversely, a matrix whose entries are all positive is not necessarily positive definite, as for example
Eigenvalues
Let be an Hermitian matrix.
 is positive definite if and only if all of its eigenvalues are positive.
 is positive semidefinite if and only if all of its eigenvalues are nonnegative.
 is negative definite if and only if all of its eigenvalues are negative
 is negative semidefinite if and only if all of its eigenvalues are nonpositive.
 is indefinite if and only if it has both positive and negative eigenvalues.
Let be an eigendecomposition of , where is a unitary complex matrix whose rows comprise an orthonormal basis of eigenvectors of , and is a real diagonal matrix whose main diagonal contains the corresponding eigenvalues. The matrix may be regarded as a diagonal matrix that has been reexpressed in coordinates of the basis . In particular, the onetoone change of variable shows that is real and positive for any complex vector if and only if is real and positive for any ; in other words, if is positive definite. For a diagonal matrix, this is true only if each element of the main diagonal—that is, every eigenvalue of —is positive. Since the spectral theorem guarantees all eigenvalues of a Hermitian matrix to be real, the positivity of eigenvalues can be checked using Descartes' rule of alternating signs when the characteristic polynomial of a real, symmetric matrix is available.
Connections
A general purely quadratic real function on real variables can always be written as where is the column vector with those variables, and is a symmetric real matrix. Therefore, the matrix being positive definite means that has a unique minimum (zero) when is zero, and is strictly positive for any other .
More generally, a twicedifferentiable real function on real variables has local minimum at arguments if its gradient is zero and its Hessian (the matrix of all second derivatives) is positive semidefinite at that point. Similar statements can be made for negative definite and semidefinite matrices.
In statistics, the covariance matrix of a multivariate probability distribution is always positive semidefinite; and it is positive definite unless one variable is an exact linear function of the others. Conversely, every positive semidefinite matrix is the covariance matrix of some multivariate distribution.
Characterizations
Let be an Hermitian matrix. The following properties are equivalent to being positive definite:
 The associated sesquilinear form is an inner product
 The sesquilinear form defined by is the function from to such that for all and in , where is the conjugate transpose of . For any complex matrix , this form is linear in and semilinear in . Therefore, the form is an inner product on if and only if is real and positive for all nonzero ; that is if and only if is positive definite. (In fact, every inner product on arises in this fashion from a Hermitian positive definite matrix.)
 It is the Gram matrix of a set of linearly independent vectors
 Let be a list of linearly independent vectors of some complex vector space with an inner product . It can be verified that the Gram matrix of those vectors, defined by , is always positive definite. Conversely, if is positive definite, it has an eigendecomposition where is unitary, diagonal, and all diagonal elements of are real and positive. Let be the real diagonal matrix with entries so ; then . Now we let be the columns of . These vectors are linearly independent, and by the above is their Gram matrix, under the standard inner product of , namely .
 Its leading principal minors are all positive
 The kth leading principal minor of a matrix is the determinant of its upperleft submatrix. It turns out that a matrix is positive definite if and only if all these determinants are positive. This condition is known as Sylvester's criterion, and provides an efficient test of positive definiteness of a symmetric real matrix. Namely, the matrix is reduced to an upper triangular matrix by using elementary row operations, as in the first part of the Gaussian elimination method, taking care to preserve the sign of its determinant during pivoting process. Since the kth leading principal minor of a triangular matrix is the product of its diagonal elements up to row , Sylvester's criterion is equivalent to checking whether its diagonal elements are all positive. This condition can be checked each time a new row of the triangular matrix is obtained.
Quadratic forms, convexity, optimization
The (purely) quadratic form associated with a real matrix is the function such that for all . can be assumed symmetric by replacing it with .
A symmetric matrix is positive definite if and only if its quadratic form is a strictly convex function.
More generally, any quadratic function from to can be written as where is a symmetric matrix, is a real vector, and a real constant. This quadratic function is strictly convex, and hence has a unique finite global minimum, if and only if is positive definite. For this reason, positive definite matrices play an important role in optimization problems.
Simultaneous diagonalization
A symmetric matrix and another symmetric and positive definite matrix can be simultaneously diagonalized, although not necessarily via a similarity transformation. This result does not extend to the case of three or more matrices. In this section we write for the real case. Extension to the complex case is immediate.
Let be a symmetric and a symmetric and positive definite matrix. Write the generalized eigenvalue equation as where we impose that be normalized, i.e. . Now we use Cholesky decomposition to write the inverse of as . Multiplying by and letting , we get , which can be rewritten as where . Manipulation now yields where is a matrix having as columns the generalized eigenvectors and is a diagonal matrix of the generalized eigenvalues. Now premultiplication with gives the final result: and , but note that this is no longer an orthogonal diagonalization with respect to the inner product where . In fact, we diagonalized with respect to the inner product induced by .
Note that this result does not contradict what is said on simultaneous diagonalization in the article Diagonalizable matrix, which refers to simultaneous diagonalization by a similarity transformation. Our result here is more akin to a simultaneous diagonalization of two quadratic forms, and is useful for optimization of one form under conditions on the other.[3]
Properties
Induced partial ordering
For arbitrary square matrices , we write if i.e., is positive semidefinite. This defines a partial ordering on the set of all square matrices. One can similarly define a strict partial ordering . The ordering is called the Loewner order.
Inverse of positive definite matrix
Every positive definite matrix is invertible and its inverse is also positive definite.[4] If then .[5] Moreover, by the minmax theorem, the kth largest eigenvalue of is greater than the kth largest eigenvalue of .
Scaling
If is positive definite and is a real number, then is positive definite.[6]
Addition
If and are positive definite, then the sum is also positive definite.[6]
Multiplication
 If and are positive definite, then the products and are also positive definite. If , then is also positive definite.
 If is positive semidefinite, then is positive semidefinite. If is positive definite and has full rank, then is positive definite.[7]
Cholesky decomposition
For any matrix , the matrix is positive semidefinite, and . Conversely, any Hermitian positive semidefinite matrix can be written as , where is lower triangular; this is the Cholesky decomposition. If is not positive definite, then some of the diagonal elements of may be zero.
A hermitian matrix is positive definite if and only if it has a unique Cholesky decomposition, i.e. the matrix is positive definite if and only if there exists a unique lower triangular matrix , with real and strictly positive diagonal elements, such that .
Square root
A matrix is positive semidefinite if and only if there is a positive semidefinite matrix with . This matrix is unique,[8] is called the square root of , and is denoted with (the square root is not to be confused with the matrix in the Cholesky factorization , which is also sometimes called the square root of ).
If then .
Submatrices
Every principal submatrix of a positive definite matrix is positive definite.
Trace
The diagonal entries of a positive definite matrix are real and nonnegative. As a consequence the trace, . Furthermore,[9] since every principal submatrix (in particular, 2by2) is positive definite,
and thus
Hadamard product
If , although is not necessary positive semidefinite, the Hadamard product (this result is often called the Schur product theorem).[10]
Regarding the Hadamard product of two positive semidefinite matrices , , there are two notable inequalities:
Kronecker product
If , although is not necessary positive semidefinite, the Kronecker product .
Frobenius product
If , although is not necessary positive semidefinite, the Frobenius product (Lancaster–Tismenetsky, The Theory of Matrices, p. 218).
Convexity
The set of positive semidefinite symmetric matrices is convex. That is, if and are positive semidefinite, then for any between 0 and 1, is also positive semidefinite. For any vector :
This property guarantees that semidefinite programming problems converge to a globally optimal solution.
Relation with cosine
The positivedefiniteness of a matrix expresses that the angle between any vector and its image is always :
Further properties
 If is a symmetric Toeplitz matrix, i.e. the entries are given as a function of their absolute index differences: , and the strict inequality
holds, then is strictly positive definite.
 Let and Hermitian. If (resp., ) then (resp., ).[13]
 If is real, then there is a such that , where is the identity matrix.
 If denotes the leading minor, is the kth pivot during LU decomposition.
 A matrix is negative definite if its kth order leading principal minor is negative when is odd, and positive when is even.
 A matrix is positive semidefinite if and only if it arises as the Gram matrix of some set of vectors. In contrast to the positive definite case, these vectors need not be linearly independent.
A Hermitian matrix is positive semidefinite if and only if all of its principal minors are nonnegative. It is however not enough to consider the leading principal minors only, as is checked on the diagonal matrix with entries 0 and −1.
Block matrices
A positive matrix may also be defined by blocks:
where each block is . By applying the positivity condition, it immediately follows that and are hermitian, and .
We have that for all complex , and in particular for . Then
A similar argument can be applied to , and thus we conclude that both and must be positive definite matrices, as well.
Converse results can be proved with stronger conditions on the blocks, for instance using the Schur complement.
Extension for nonHermitian square matrices
The definition of positive definite can be generalized by designating any complex matrix (e.g. real nonsymmetric) as positive definite if for all nonzero complex vectors , where denotes the real part of a complex number .[14] Only the Hermitian part determines whether the matrix is positive definite, and is assessed in the narrower sense above. Similarly, If and are real, we have for all real nonzero vectors if and only if the symmetric part is positive definite in the narrower sense. It is immediately clear that is insensitive to transposition of M.
Consequently, a nonsymmetric real matrix with only positive eigenvalues does not need to be positive definite. For example, the matrix has positive eigenvalues yet is not positive definite; in particular a negative value of is obtained with the choice (which is the eigenvector associated with the negative eigenvalue of the symmetric part of ).
In summary, the distinguishing feature between the real and complex case is that, a bounded positive operator on a complex Hilbert space is necessarily Hermitian, or self adjoint. The general claim can be argued using the polarization identity. That is no longer true in the real case.
Applications
Heat conductivity matrix
Fourier's law of heat conduction, giving heat flux in terms of the temperature gradient is written for anisotropic media as , in which is the symmetric thermal conductivity matrix. The negative is inserted in Fourier's law to reflect the expectation that heat will always flow from hot to cold. In other words, since the temperature gradient always points from cold to hot, the heat flux is expected to have a negative inner product with so that . Substituting Fourier's law then gives this expectation as , implying that the conductivity matrix should be positive definite.
See also
Notes
 "Appendix C: Positive Semidefinite and Positive Definite Matrices". Parameter Estimation for Scientists and Engineers: 259–263. doi:10.1002/9780470173862.app3.
 Stewart, J. (1976). "Positive definite functions and generalizations, an historical survey". Rocky Mountain J. Math. 6 (3): 409–434. doi:10.1216/RMJ197663409.
 Horn & Johnson (1985), p. 218 ff.
 Horn & Johnson (1985), p. 397
 Horn & Johnson (1985), Corollary 7.7.4(a)
 Horn & Johnson (1985), Observation 7.1.3

Horn, Roger A.; Johnson, Charles R. (2013). "7.1 Definitions and Properties". Matrix Analysis (2nd ed.). Cambridge University Press. p. 431. ISBN 9780521839402. "Observation 7.1.8 Let be Hermitian and let :
 Suppose that A is positive semidefinite. Then is positive semidefinite, , and
 Suppose that A is positive definite. Then , and is positive definite if and only if rank(C) = m"
 Horn & Johnson (1985), Theorem 7.2.6 with
 Horn & Johnson (1985), p. 398
 Horn & Johnson (1985), Theorem 7.5.3
 Horn & Johnson (1985), Theorem 7.8.6
 Styan (1973)
 Bhatia, Rajendra (2007). Positive Definite Matrices. Princeton, New Jersey: Princeton University Press. p. 8. ISBN 9780691129181.
 Weisstein, Eric W. Positive Definite Matrix. From MathWorldA Wolfram Web Resource. Accessed on 20120726
References
 Horn, Roger A.; Johnson, Charles R. (1990). Matrix Analysis. Cambridge University Press. ISBN 9780521386326.
 Bhatia, Rajendra (2007). Positive definite matrices. Princeton Series in Applied Mathematics. ISBN 9780691129181.
 Bernstein, B.; Toupin, R. A. (1962). "Some Properties of the Hessian Matrix of a Strictly Convex Function". Journal für die reine und angewandte Mathematik. 210: 67–72. doi:10.1515/crll.1962.210.65.
External links
 Hazewinkel, Michiel, ed. (2001) [1994], "Positivedefinite form", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 9781556080104
 Wolfram MathWorld: Positive Definite Matrix