# Definite matrix

## Property of a mathematical matrix / From Wikipedia, the free encyclopedia

#### Dear Wikiwand AI, let's keep it short by simply answering these key questions:

Can you list the top facts and stats about Positive-definite matrix?

Summarize this article for a 10 year old

In mathematics, a symmetric matrix $M$ with real entries is **positive-definite** if the real number $z^{\operatorname {T} }Mz$ is positive for every nonzero real column vector $z,$ where $z^{\operatorname {T} }$ is the transpose of $z$.[1] More generally, a Hermitian matrix (that is, a complex matrix equal to its conjugate transpose) is
**positive-definite** if the real number $z^{*}Mz$ is positive for every nonzero complex column vector $z,$ where $z^{*}$ denotes the conjugate transpose of $z.$

**Positive semi-definite** matrices are defined similarly, except that the scalars $z^{\operatorname {T} }Mz$ and $z^{*}Mz$ 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**.

A matrix is thus positive-definite if and only if it is the matrix of a positive-definite quadratic form or Hermitian form. In other words, a matrix is positive-definite if and only if it defines an inner product.

Positive-definite and positive-semidefinite matrices can be characterized in many ways, which may explain the importance of the concept in various parts of mathematics. A matrix M is positive-definite 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 an invertible matrix $B$ with conjugate transpose $B^{*}$ such that $M=B^{*}B.$

A matrix is positive semi-definite if it satisfies similar equivalent conditions where "positive" is replaced by "nonnegative", "invertible matrix" is replaced by "matrix", and the word "leading" is removed.

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 p, then the function is convex near p, and, conversely, if the function is convex near p, then the Hessian matrix is positive-semidefinite at p.

Some authors use more general definitions of definiteness, including some non-symmetric real matrices, or non-Hermitian complex ones.

Oops something went wrong: