Solve System of Equations via Gaussian Elimination

In summary: After reducing the matrix to echelon form, there will always be a solution, even if the row has 0s in it.
  • #1
EvLer
458
0
I am not sure how to solve this:
Given an augmented matrix, find conditions on a, b, c for which the system has solutions:
Code:
-1   -2   3    b
-1   -6   23   c
-3   2    4    a
so by Gaussian elimination, the matrix I ended up with is
Code:
1   2   -3   -b
0   4   -20  b-c
0   0   -35  (3b-a) + 2(b-c)
And now I am stuck, because there is still a variable left in the last row. Does that mean there are no such a, b, c for which the system has a solution?
Thanks in advance.
 
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  • #2
EvLer said:
I am not sure how to solve this:

And now I am stuck, because there is still a variable left in the last row. Does that mean there are no such a, b, c for which the system has a solution?
Thanks in advance.

In general, when do you know there is a solution, and when do you know there is no solution?

Suppose you were given a particular a,b and c at random... would you be able to solve for x, y and z?
 
  • #3
So, are you implying that the conditions I need on a, b, and c are such that
z = (3b - a + 2b - 2c)/ (-35) and so on?
The problems we have done in class were basically of the form such that the last row of coefficient part of the augm. matrix is all zeros without variables. So the conditions are only in terms of a, b, and c themselves.
 
  • #4
EvLer said:
So, are you implying that the conditions I need on a, b, and c are such that
z = (3b - a + 2b - 2c)/ (-35) and so on?
The problems we have done in class were basically of the form such that the last row of coefficient part of the augm. matrix is all zeros without variables. So the conditions are only in terms of a, b, and c themselves.

The idea is that no matter what a,b,c are you can always get a solution. Like you showed above you can always solve for z, then for y then for x.

a, b and c can be any real number.

Another way to look at it is, after you reduce an augmented matrix to echelon form.. the only time you have no solution is if you have a row that is

0 0 0 | t

where t is non-zero. Any other time there is always a solution.

Since your matrix (reduced to echelon form) does not have a row like this (no matter what values a, b or c take) there is a solution for any a,b,c.

So a,b,c can be any real numbers.
 

1. What is Gaussian Elimination?

Gaussian elimination is a method for solving a system of linear equations by systematically eliminating variables until a solution is found. It involves transforming the equations into a triangular form through a series of row operations, making it easier to solve for the variables.

2. How does Gaussian Elimination work?

Gaussian elimination works by using three types of row operations: multiplying a row by a constant, swapping two rows, and adding a multiple of one row to another row. These operations are used to transform the equations into an upper triangular form, where the bottom row contains only one variable, the second-to-last row contains two variables, and so on. Once the equations are in this form, the variables can be solved for one at a time, starting from the bottom and working up.

3. When is Gaussian Elimination used?

Gaussian elimination is used when solving a system of linear equations, particularly when there are more than two variables. It is a useful tool in many areas of science, such as physics, engineering, and economics. It can also be used in computer programming to solve systems of equations in simulations and optimization problems.

4. What are the advantages of using Gaussian Elimination?

Gaussian elimination is a powerful and efficient method for solving systems of equations, as it can handle large and complex systems of equations. It also provides a systematic way to solve for the variables, making it less prone to errors compared to other methods. Additionally, it can be easily programmed into computers, making it useful for solving equations in simulations and computer programs.

5. Are there any limitations to Gaussian Elimination?

Gaussian elimination may not work for systems of equations that have no solution or infinite solutions. Additionally, it can be time-consuming and labor-intensive for very large systems of equations. In these cases, alternative methods such as matrix inversion or Cramer's rule may be more suitable.

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