In mathematics, a symmetric matrix M {\displaystyle M} with real entries is positive-definite if the real number x ⊤ M x {\displaystyle \mathbf {x} ^{\top }M\mathbf {x} } is positive for every nonzero real column vector x , {\displaystyle \mathbf {x} ,} where x ⊤ {\displaystyle \mathbf {x} ^{\top }} is the row vector transpose of x . {\displaystyle \mathbf {x} .} More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is positive-definite if the real number z ∗ M z {\displaystyle \mathbf {z} ^{*}M\mathbf {z} } is positive for every nonzero complex column vector z , {\displaystyle \mathbf {z} ,} where z ∗ {\displaystyle \mathbf {z} ^{*}} denotes the conjugate transpose of z . {\displaystyle \mathbf {z} .}
Positive semi-definite matrices are defined similarly, except that the scalars x ⊤ M x {\displaystyle \mathbf {x} ^{\top }M\mathbf {x} } and z ∗ M z {\displaystyle \mathbf {z} ^{*}M\mathbf {z} } are required to be positive or zero (that is, nonnegative). Negative-definite and negative semi-definite matrices are defined analogously. A matrix that is not positive semi-definite and not negative semi-definite is sometimes called indefinite.
Some authors use more general definitions of definiteness, permitting the matrices to be non-symmetric or non-Hermitian. The properties of these generalized definite matrices are explored in § Extension for non-Hermitian square matrices, below, but are not the main focus of this article.