# How to Calculate Eigenvalues and Eigenvectors in Excel

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In the world of matrix problems, Eigenvalue and Eigenvector are one of the most important terms. They are widely used to solve linear equations reducing dimension space. In this article, let us learn how to calculate eigenvalues and eigenvectors in Excel with 2 useful methods.

## What Are Eigenvalues?

The Eigenvalues are a set of scalar values associated with a set of linear equations that has a non-zero absolute solution. People also call it Characteristic Roots. The process results in a set of Determinants that has this equation:

(A â€“ Î»I)=0

Here, the scalar Lambda (Î») is the required eigenvalue that is associated with square k x k matrix A and Identity matrix I.

## What Are Eigenvectors?

In a linear equation, along with calculating Eigenvalues, we also calculate the Eigenvector of a matrix. It is a sort of vector that is generated through the scalar values. It is also called proper vectors, characteristic vectors, or latent vectors. We can symbolize itÂ with an X in the Determinant equation which results in this:

(A â€“ Î»I)X=0

## How to Calculate Eigenvalues and Eigenvectors in Excel: 2 Useful Methods

So far, we learned about eigenvalues and eigenvectors in brief. Now let us calculate them with the following 3X3 MatrixÂ A.

### 1. Calculate Eigenvalues and Eigenvectors with Goal Seek in Excel

In this first method, we will process the calculation with the Goal Seek feature in Excel. Letâ€™s see how it works.

• First, insert a general Identity Matrix in the Cell range F5:H7 where we have 1 in the diagonal cells.

• Second, create a new column to find the Determinant where the initial scalar Lambda (Î») is 0.

• Then, insert this formula in Cell B11 to find the 1st value of the k x k matrix based on the determinant equation.
`=B5-\$C\$10*F5`

• Similarly, apply the same formula based on each cell of the A matrix.
• Therefore, the output looks like this.

• Now, insert this formula into Cell C15 to find the Determinant value.
`=MDETERM(B11:D13)`

Here, the MDETERM function returns the value of the determinant from the B11:D13 array.
• Next, go to the Data tab and select Goal Seek from the What-If Analysis option.

• Then, refer Determinant value as the Set cell and the Lambda (Î») value as By changing cell.
• Along with this, determine the To value as 0 as we want the Determinant value equal to zero.

• Following this, press OK and after some time you will get the new Eigenvalue along with the Determinant value in Cells C10 and C15 respectively.

• Next, change the Lambda (Î») value to 1.5 and run the same process in Goal Seek to get another set of values as follows.

• Similarly, insert 3 as Lambda (Î») value and run the Goal Seek operation to get the following output.

• So far, we got 3 sets of eigenvalues as the Lambda (Î») value.
• Lastly, insert this formula for each set of eigenvalues and get the final eigenvectors like this.
`=-MMULT(MINVERSE(C12:D13),B6:B7)`

In this formula, the MMULT function calculates the product of the k x k matrix both the initial and the new one. Along with it, the MINVERSE function provides the inverse of the matrix.

### 2. Apply Power Method to Get Eigenvalues and Eigenvectors

We can apply the Power Method in Excel to find the eigenvalues and eigenvectors along with the dominant one. To do this, follow the simple steps below.

• In the beginning, create an initial vector column with the following values in the Cell range F5:F7.

• Then, insert the values as a row vector in the Cell range C10:E10 for ease of the process.

• Next, insert this formula in Cell F10 to transpose and return the absolute vector value.
`=TRANSPOSE(ROUND(MMULT(\$B\$5:\$D\$7,TRANSPOSE(C10:E10)),4))`

Here, the TRANSPOSE function converts any given vertical range to a horizontal range and vice-versa. Then, the ROUND function returns the output as a rounded number. Lastly, the MMULT function calculates the product of the matrix \$B\$5:\$D\$7, where we set it as an absolute cell reference. Lastly, we provide 4 in the num-digits argument to get up to 4 decimal places.
• Afterward, press Enter and you will get the following output where k=0.

• Next, insert this formula in Cell I10 to find the Maximum Absolute Eigenvalue.
`=MAX(ABS(F10:H10))`

Here, the MAX function returns the maximum value from the Cell range F10:H10. Also, the ABS function returns an absolute value of this omitting negative values.

• Next, select the cell range C11:E11 and insert this formula to get the next horizontal vector value where k=1.

`=F10:H10/I10`

• Following this, select the Cell range F10:I10 and drag its bottom corner downward as shown below.

• After this, insert this formula in Cell J11 to get the absolute value of Epsilon (e) in the power method.

`=ABS(I11-I10)`

• Lastly, select the Cell range B11:J11 and drag the bottom corner of the last cell downward until it shows repetitive values like this.

• So far, we got the eigenvalues (mk+1) and eigenvectors (V[k]T) for each value of k.
• Therefore, analyzing the dataset above, delete the rows that have repetitive values.
• Accordingly, comparing the last value of Epsilon (e) which is 0.0001 with the standard value 0.0005 we can get that 0.0001 < 0.0005.
• As a result, we have got our dominant eigenvalue and eigenvectors based on the adjacent cell values as shown below.

Get this practice file and try the methods by yourself.

## Conclusion

Finally, we are at the end of our article. Here we tried to explain how to calculate eigenvalues and eigenvectors in Excel with 2 useful methods. Let us know your feedback in the comment box.

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1. Hai, how do we know the standard value of Epsilon (e) = 0.0005? does the epsilon value is decided by us or does 0.0005 is the standard value?

• Hi HANI,
Thanks for your query. The choice of epsilon depends on the desired accuracy of the eigenvalue estimate. It is typically a small positive value, and the algorithm terminates when the absolute difference between consecutive eigenvalue estimates falls below epsilon.

The specific value of epsilon will vary depending on the application and the desired level of accuracy. Using epsilon = 0.0005 indicates that the power method iterations will continue until the absolute difference between consecutive eigenvalue estimates falls below 0.0005. This level of tolerance can be suitable for many applications, especially when the eigenvalues of the matrix are relatively close in magnitude.

However, it’s important to note that the appropriate value of epsilon may vary depending on the specific problem and matrix being analyzed. If you find that the algorithm converges too quickly or does not reach the desired accuracy with epsilon = 0.0005, you may need to adjust the value accordingly.

Consider the scale and conditioning of the problem as well. If the matrix is ill-conditioned or has eigenvalues with large differences in magnitude, a smaller epsilon may be necessary to obtain accurate results.

So, it’s always important to assess the specific requirements of your problem and adjust the value of epsilon accordingly to achieve the desired accuracy.

Regards
Rafiul Hasan
Team ExcelDemy

2. Hi.Thx for your doc, I can try SVD in excel without programed library.
But I wonder, why do we must use “Round” function?
I tried to use without “round”, AV^kt was not calculated collectly.

Can you explain for me please?

3. Hi agian,
I solve the problem about “round” function, which is my just mistake…..
Now I wondering about ‘how can we find other Eigen Vector and Value.
In my case, I mus find eigen vectors of 10×10 matrix….
so, I practice with 3×3 matrix and try to find normalized method.
within 3×3 matrix, [5,4,-1; 4,5,1; -1,1,2],
starting with V = 0,1,0 give the eigen value = 9 but
starting with V = 0,0,1 give the eigen value = 3. how can we choose V?

Furthermore, in hand writing, eigen value = 0 is the another solution.
and I cannot find ‘V’ such as give back eigen value = 0

• Dear MATHMAN,

We are glad that you have already got your answer related to the ROUND function.

Now let’s jump to the problem related to finding other EigenValue and EigenVector using the power method.

In the power method, it is a must to choose an initial vector V0. So, the tips for what to keep in mind while randomly choosing an initial vector V0:

1. Ensure that the initial vector has non-zero elements.
2. The initial vector must be non-orthogonal. To check you can use this example:
If initial vector V0 =[0,1,1] and W is the given dominant Eigenvector, then V0 X W =0Â
3. Avoid extremely small or large values in the initial vector.
4. If possible, ensure that the magnitude of the initial vector is consistent.

The dominant Eigenvalue for the 3×3 matrix [5,4,-1; 4,5,1; -1,1,2] is approximately Î» â‰ˆ 9.

If you are getting Eignevalue Î»â‰ˆ0 for the initial EigenVector V0 = [0,0,1] most probably because the vector has no magnitude along the x and y axes and a magnitude of 1 along the z-axis. Therefore, the initial vector V0 does not converge to the dominant Eigenvalue for the given matrix. It is expected that different EigenVectors can lead to different EigenValues.

The dominant eigenvalue is typically the one with the largest magnitude. It is recommended to use multiple initial vectors in the power method so that you know which eigenvalue converges to the dominant eigenvalue.

I hope this solution resolves your issue. Feel free to email us at [email protected]Â if you have any further problems or inquiries.

Regards,

Qayem Ishrak Khan

Team ExcelDemy.

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