Linear Algebra - Basis for a row space

In summary, to find a basis for the row space of A, we can transpose A and put it into reduced row echelon form. This will show that the rows of A form a basis for R4. Therefore, the basis consists of every row vector from A.
  • #1
jinksys
123
0
A =
Code:
 1  2 -1  3
 3  5  2  0
 0  1  2  1 
-1  0 -2  7

Problem: Find a basis for the row space of A consisting of vectors that are row vector of A.

My attempt:

I transpose the matrix A and put it into reduced row echelon form. It turns out that there are leading ones in every column. Therefore, the basis includes every row vector from A.

Is this the correct way to handle this problem?
 
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  • #2
Yes, that works.
 
  • #3
What you found means that the rows of A form a basis for R4, meaning that every vector in R4 can be written as a linear combination of the four vectors that are the rows of A.
 

Related to Linear Algebra - Basis for a row space

What is a row space in linear algebra?

A row space is the set of all linear combinations of the rows in a matrix. It represents the subspace spanned by the rows of the matrix and is a fundamental concept in linear algebra.

What is the basis for a row space?

The basis for a row space is a set of linearly independent vectors that span the row space. These vectors can be used to represent any vector in the row space, and there are an infinite number of possible bases for a given row space.

How do you find the basis for a row space?

To find the basis for a row space, you can use the Gaussian elimination method to reduce the matrix to its row-echelon form. The nonzero rows in the row-echelon form then form a basis for the row space.

What is the dimension of a row space?

The dimension of a row space is equal to the number of linearly independent rows in the matrix. This is also known as the rank of the matrix and is an important property in linear algebra.

Why is the row space important in linear algebra?

The row space is important because it helps us understand the relationships between the rows of a matrix and how they can be combined to create other vectors. It also has applications in solving systems of linear equations and in understanding the properties of matrix operations.

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