In mathematics, a symmetric matrix with real entries is positivedefinite if the real number is positive for every nonzero real column vector where is the transpose of .^{[1]} More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positivedefinite if the real number is positive for every nonzero complex column vector where denotes the conjugate transpose of
Positive semidefinite matrices are defined similarly, except that the scalars and are required to be positive or zero (that is nonnegative). Negativedefinite and negative semidefinite matrices are defined analogously. A matrix that is not positive semidefinite and not negative semidefinite is sometimes called indefinite.
A matrix is thus positivedefinite if and only if it is the matrix of a positivedefinite quadratic form or Hermitian form. In other words, a matrix is positivedefinite if and only if it defines an inner product.
Positivedefinite and positivesemidefinite matrices can be characterized in many ways, which may explain the importance of the concept in various parts of mathematics. A matrix M is positivedefinite if and only if it satisfies any of the following equivalent conditions.
 M is congruent with a diagonal matrix with positive real entries.
 M is symmetric or Hermitian, and all its eigenvalues are real and positive .
 M is symmetric or Hermitian, and all its leading principal minors are positive.
 There exists a invertible matrix with conjugate transpose such that
A matrix is positive semidefinite if it satisfies similar equivalent conditions where "positive" is replaced by "nonnegative" and "invertible matrix" is replaced by "matrix".
Positivedefinite and positivesemidefinite 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 positivedefinite at a point p, then the function is convex near p, and, conversely, if the function is convex near p, then the Hessian matrix is positivesemidefinite at p.
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
An symmetric real matrix is said to be positivedefinite if for all nonzero in . Formally,
An symmetric real matrix is said to be positive semidefinite or nonnegativedefinite if for all in . Formally,
An symmetric real matrix is said to be negativedefinite if for all nonzero in . Formally,
An symmetric real matrix is said to be negativesemidefinite or nonpositivedefinite if for all in . Formally,
An 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 .
An Hermitian complex matrix is said to be positivedefinite if for all nonzero in . Formally,
An Hermitian complex matrix is said to be positive semidefinite or nonnegativedefinite if for all in . Formally,
An Hermitian complex matrix is said to be negativedefinite if for all nonzero in . Formally,
An Hermitian complex matrix is said to be negative semidefinite or nonpositivedefinite if for all in . Formally,
An 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 positivedefinite 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 positivedefinite 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 positivedefinite. 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 positivedefinite.
On the other hand, for a symmetric real matrix , the condition " for all nonzero real vectors " does imply that is positivedefinite in the complex sense.
Notation
If a Hermitian matrix is positive semidefinite, one sometimes writes and if is positivedefinite one writes . To denote that is negative semidefinite one writes and to denote that is negativedefinite 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 positivedefinite, negative semidefinite and negativedefinite matrices, respectively. This may be confusing, as sometimes nonnegative matrices (respectively, nonpositive matrices) are also denoted in this way.
Examples
 The identity matrix is positivedefinite (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
 For any real invertible matrix , the product is a positive definite matrix (if the means of the columns of A are 0, then this is also called the covariance 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 (this includes real symmetric matrices). All eigenvalues of are real, and their sign characterize its definiteness:
 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 columns 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 (eigenvectors) basis . Put differently, applying M to some vector z in our coordinates system (Mz), is the same as changing the basis of our z to the eigenvector coordinate system using P^{−1} (P^{−1}z), applying the stretching transformation D to it (DP^{−1}z), and then changing the basis back to our system using P (PDP^{−1}z).
With this in mind, 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.
Decomposition
Let be an Hermitian matrix. is positive semidefinite if and only if it can be decomposed as a product
of a matrix with its conjugate transpose.
When is real, can be real as well and the decomposition can be written as
is positive definite if and only if such a decomposition exists with invertible. More generally, is positive semidefinite with rank if and only if a decomposition exists with a matrix of full row rank (i.e. of rank ). Moreover, for any decomposition , .^{[2]}
Proof


If , then , so is positive semidefinite. If moreover is invertible then the inequality is strict for , so is positive definite. If is of rank , then . In the other direction, suppose is positive semidefinite. Since is Hermitian, it has an eigendecomposition where is unitary and is a diagonal matrix whose entries are the eigenvalues of Since is positive semidefinite, the eigenvalues are nonnegative real numbers, so one can define as the diagonal matrix whose entries are nonnegative square roots of eigenvalues. Then for . If moreover is positive definite, then the eigenvalues are (strictly) positive, so is invertible, and hence is invertible as well. If has rank , then it has exactly positive eigenvalues and the others are zero, hence in all but rows are all zeroed. Cutting the zero rows gives a matrix such that . 
The columns of can be seen as vectors in the complex or real vector space , respectively. Then the entries of are inner products (that is dot products, in the real case) of these vectors
In other words, a Hermitian matrix is positive semidefinite if and only if it is the Gram matrix of some vectors . It is positive definite if and only if it is the Gram matrix of some linearly independent vectors. In general, the rank of the Gram matrix of vectors equals the dimension of the space spanned by these vectors.^{[3]}
Uniqueness up to unitary transformations
The decomposition is not unique: if for some matrix and if is any unitary matrix (meaning ), then for .
However, this is the only way in which two decompositions can differ: the decomposition is unique up to unitary transformations. More formally, if is a matrix and is a matrix such that , then there is a matrix with orthonormal columns (meaning ) such that .^{[4]} When this means is unitary.
This statement has an intuitive geometric interpretation in the real case: let the columns of and be the vectors and in . A real unitary matrix is an orthogonal matrix, which describes a rigid transformation (an isometry of Euclidean space ) preserving the 0 point (i.e. rotations and reflections, without translations). Therefore, the dot products and are equal if and only if some rigid transformation of transforms the vectors to (and 0 to 0).
Square root
A matrix is positive semidefinite if and only if there is a positive semidefinite matrix (in particular is Hermitian, so ) satisfying . This matrix is unique,^{[5]} is called the nonnegative square root of , and is denoted with . When is positive definite, so is , hence it is also called the positive square root of .
The nonnegative square root should not be confused with other decompositions . Some authors use the name square root and for any such decomposition, or specifically for the Cholesky decomposition, or any decomposition of the form ; other only use it for the nonnegative square root.
If then .
Cholesky decomposition
A positive semidefinite matrix can be written as , where is lower triangular with nonnegative diagonal (equivalently where is upper triangular); this is the Cholesky decomposition. If is positive definite, then the diagonal of is positive and the Cholesky decomposition is unique. The Cholesky decomposition is especially useful for efficient numerical calculations. A closely related decomposition is the LDL decomposition, , where is diagonal and is lower unitriangular.
Other 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.)
 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.
A positive semidefinite matrix is positive definite if and only if it is invertible.^{[6]} A matrix is negative (semi)definite if and only if is positive (semi)definite.
Quadratic forms
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 .^{[7]}
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.
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.^{[8]} If then .^{[9]} 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.^{[10]}
Addition
 If and are positivedefinite, then the sum is also positivedefinite.^{[10]}
 If and are positivesemidefinite, then the sum is also positivesemidefinite.
 If is positivedefinite and is positivesemidefinite, then the sum is also positivedefinite.
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 for any (possibly rectangular) matrix . If is positive definite and has full column rank, then is positive definite.^{[11]}
Trace
The diagonal entries of a positivesemidefinite matrix are real and nonnegative. As a consequence the trace, . Furthermore,^{[12]} since every principal submatrix (in particular, 2by2) is positive semidefinite,
and thus, when ,
An Hermitian matrix is positive definite if it satisfies the following trace inequalities:^{[13]}
Another important result is that for any and positivesemidefinite matrices,
Hadamard product
If , although is not necessary positive semidefinite, the Hadamard product is, (this result is often called the Schur product theorem).^{[14]}
Regarding the Hadamard product of two positive semidefinite matrices , , there are two notable inequalities:
 Oppenheim's inequality: ^{[15]}
 .^{[16]}
Kronecker product
If , although is not necessary positive semidefinite, the Kronecker product .
Frobenius product
If , although is not necessary positive semidefinite, the Frobenius inner 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., ).^{[17]}
 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 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 and submatrices
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. The argument can be extended to show that any principal submatrix of is itself positive definite.
Converse results can be proved with stronger conditions on the blocks, for instance using the Schur complement.
Local extrema
A general quadratic form 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.
Covariance
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.
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 .^{[18]} 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
 Covariance matrix
 Mmatrix
 Positivedefinite function
 Positivedefinite kernel
 Schur complement
 Sylvester's criterion
 Numerical range
Notes
 ^ "Appendix C: Positive Semidefinite and Positive Definite Matrices". Parameter Estimation for Scientists and Engineers: 259–263. doi:10.1002/9780470173862.app3.
 ^ Horn & Johnson (2013), p. 440, Theorem 7.2.7
 ^ Horn & Johnson (2013), p. 441, Theorem 7.2.10
 ^ Horn & Johnson (2013), p. 452, Theorem 7.3.11
 ^ Horn & Johnson (2013), p. 439, Theorem 7.2.6 with
 ^ Horn & Johnson (2013), p. 431, Corollary 7.1.7
 ^ Horn & Johnson (2013), p. 485, Theorem 7.6.1
 ^ Horn & Johnson (2013), p. 438, Theorem 7.2.1
 ^ Horn & Johnson (2013), p. 495, Corollary 7.7.4(a)
 ^ ^{a} ^{b} Horn & Johnson (2013), p. 430, Observation 7.1.3
 ^ Horn & Johnson (2013), p. 431, Observation 7.1.8
 ^ Horn & Johnson (2013), p. 430
 ^ Wolkowicz, Henry; Styan, George P.H. (1980). "Bounds for Eigenvalues using Traces". Linear Algebra and its Applications. Elsevier (29): 471–506.
 ^ Horn & Johnson (2013), p. 479, Theorem 7.5.3
 ^ Horn & Johnson (2013), p. 509, Theorem 7.8.16
 ^ Styan, G. P. (1973). "Hadamard products and multivariate statistical analysis". Linear Algebra and Its Applications. 6: 217–240., Corollary 3.6, p. 227
 ^ 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. (2013). Matrix Analysis (2nd ed.). Cambridge University Press. ISBN 9780521548236.
 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.