Finding Solutions to Systems of Linear Equations Using Reduced Echelon Form

In summary, the given problem presents four different reduced row echelon forms of augmented matrices representing systems of linear equations. The task at hand is to find all solutions to the original system in each part. Part a.) is already in reduced row echelon form, so the solutions can be easily determined. Parts b.), c.), and d.) also provide the row echelon forms, but the solutions must be written out based on the given equations.
  • #1
sheldonrocks97
Gold Member
66
2

Homework Statement



In each part, the reduced echelon form of the augmented matrix of a system of linear
equations is given. Find all solutions to the original system.
a.

[1 0] [2]
[0 1] [5]

b.

[1 0] [2]
[0 0] [1]

c.

[1 0] [2]
[0 0] [0]

d.

[1 2 0 1] [2]
[0 0 1 1] [5]

Homework Equations



None

The Attempt at a Solution



I know that a.) is already in reduced row echelon form. I just don't understand how to get the last four, because the second and third don't have ones other than in 1 corner.
 
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  • #2
sheldonrocks97 said:

Homework Statement



In each part, the reduced echelon form of the augmented matrix of a system of linear
equations is given. Find all solutions to the original system.
a.

[1 0] [2]
[0 1] [5]

b.

[1 0] [2]
[0 0] [1]

c.

[1 0] [2]
[0 0] [0]

d.

[1 2 0 1] [2]
[0 0 1 1] [5]

Homework Equations



None

The Attempt at a Solution



I know that a.) is already in reduced row echelon form. I just don't understand how to get the last four, because the second and third don't have ones other than in 1 corner.

Write out the actual equations corresponding to the echelon forms; after all, that is what we use such form for in the first place---to stand as shorthand for some equations.
 
  • #3
sheldonrocks97 said:

Homework Statement



In each part, the reduced echelon form of the augmented matrix of a system of linear
equations is given. Find all solutions to the original system.
a.

[1 0] [2]
[0 1] [5]

b.

[1 0] [2]
[0 0] [1]

c.

[1 0] [2]
[0 0] [0]

d.

[1 2 0 1] [2]
[0 0 1 1] [5]

Homework Equations



None

The Attempt at a Solution



I know that a.) is already in reduced row echelon form. I just don't understand how to get the last four, because the second and third don't have ones other than in 1 corner.

You don't "get" the last four into row echelon form. They are also given to already be in row echelon form. You are just asked to write out the solutions.
 

FAQ: Finding Solutions to Systems of Linear Equations Using Reduced Echelon Form

What is reduced echelon form?

Reduced echelon form is a type of matrix form in which a matrix is transformed into a specific form by using elementary row operations. It is useful for solving systems of linear equations and finding the inverse of a matrix.

How is reduced echelon form different from row echelon form?

Reduced echelon form is a more specific form of row echelon form. Unlike row echelon form, reduced echelon form requires that all leading coefficients (first non-zero entry in each row) must be equal to 1, and all entries above and below a leading coefficient must be zero.

Why is reduced echelon form important in linear algebra?

Reduced echelon form is important in linear algebra because it allows for efficient computation of solutions to systems of linear equations. It also helps in determining the rank and nullity of a matrix, and finding the inverse of a matrix.

How do you convert a matrix into reduced echelon form?

To convert a matrix into reduced echelon form, use elementary row operations such as swapping rows, multiplying a row by a non-zero constant, and adding a multiple of one row to another. These operations transform the matrix into row echelon form, and then the leading coefficients can be made equal to 1 and the rest of the entries above and below them can be made zero.

What are the applications of reduced echelon form in real life?

Reduced echelon form has various applications in fields such as engineering, physics, and computer science. It is used for solving systems of linear equations, finding the inverse of a matrix, and determining the rank and nullity of a matrix. It is also used in image and signal processing, and in solving optimization problems.

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