User:IssaRice/Linear algebra/Equivalent statements for injectivity and surjectivity: Difference between revisions
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Let <math>A</math> be an <math>m \times n</math> matrix. | Let <math>A</math> be an <math>m \times n</math> matrix. That's a matrix with <math>m</math> rows and <math>n</math> columns, which you can also think of as a map <math>\mathbf R^n \to \mathbf R^m</math>. | ||
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Latest revision as of 19:42, 30 August 2023
Let be an matrix. That's a matrix with rows and columns, which you can also think of as a map .
| Injective | Surjective | Bijective |
|---|---|---|
| is injective | is surjective | is bijective |
| has a left inverse | has a right inverse | has both a left and right inverse (which turn out to be the same) |
| for each , the equation has at most one solution (in other words, a solution may not exist, but if it does, it is unique) | for each , the equation has at least one solution (in other words, a solution always exists, but it may not be unique) | for each , the equation has exactly one (in other words, a solution always exists, and it is unique) |
| the columns of are linearly independent | the columns of span | the columns of are a basis of |
| the rows of span | the rows of are linearly independent | the rows of are a basis of |
| has rank | has rank | has rank |
| in the row echelon form of , there is a pivot in every column | in the row echelon form of , there is a pivot in every row | in the row echelon form of , there is a pivot in every column and every row |
Characterizations of injectivity
left inverse
Ax=b has at most one solution
linearly independent columns
spanning rows
rank n
pivot in every column
null space = {0}
zero-dimensional null space
dimension of range = n
External links
- http://davidjekel.com/wp-content/uploads/2019/07/Linear_Algebra_Equivalences.pdf
- Hubbard and Hubbard's Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach also has a similar table in section 2.5 (kernels, images, and the dimension formula).