Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Rank (linear algebra)
Summary
In linear algebra, the rank of a matrix A is the dimension of the vector space generated (or spanned) by its columns. This corresponds to the maximal number of linearly independent columns of A. This, in turn, is identical to the dimension of the vector space spanned by its rows. Rank is thus a measure of the "nondegenerateness" of the system of linear equations and linear transformation encoded by A. There are multiple equivalent definitions of rank. A matrix's rank is one of its most fundamental characteristics.
The rank is commonly denoted by rank(A) or rk(A); sometimes the parentheses are not written, as in rank A.
In this section, we give some definitions of the rank of a matrix. Many definitions are possible; see Alternative definitions for several of these.
The column rank of A is the dimension of the column space of A, while the row rank of A is the dimension of the row space of A.
A fundamental result in linear algebra is that the column rank and the row rank are always equal. (Three proofs of this result are given in , below.) This number (i.e., the number of linearly independent rows or columns) is simply called the rank of A.
A matrix is said to have full rank if its rank equals the largest possible for a matrix of the same dimensions, which is the lesser of the number of rows and columns. A matrix is said to be rank-deficient if it does not have full rank. The rank deficiency of a matrix is the difference between the lesser of the number of rows and columns, and the rank.
The rank of a linear map or operator is defined as the dimension of its :where is the dimension of a vector space, and is the image of a map.
The matrix
has rank 2: the first two columns are linearly independent, so the rank is at least 2, but since the third is a linear combination of the first two (the first column minus the second), the three columns are linearly dependent so the rank must be less than 3.
The matrix
has rank 1: there are nonzero columns, so the rank is positive, but any pair of columns is linearly dependent.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood