EigenVector Calculator

Discover the power of our eigenvector calculator, simplifying the eigenvectors for square matrices in linear algebra.

Table of Contents:

Introduction to Eigenvector Calculator:

The eigenvector calculator with steps is an online tool that helps you to determine the eigenvector from the given system of linear equations. It used the linear transformation method in which it finds the eigenvalue from the given equation and then finds the eigenvectors from that scalar value.

Eigenvector Calculator with Steps

The eigenspace calculator is a useful tool specially designed to compute the eigenvectors for the square matrix for everyone because it is used in various fields such as physics, computer science, and economics.

What is an Eigenvector?

An eigenvector is a special vector that is related to a linear transformation of a vector space because it involves the system of linear equations.

An eigenvector is defined as a square matrix A, an eigenvector v, and a corresponding eigenvalue λ that satisfies the given system of linear equations. It can be expressed as:

$$ det( A - λI) \;=\; 0 $$

$$ Av \;=\; λv $$

where:

  • A is a n×n square matrix.
  • v is a non-zero vector in R (for complex numbers).
  • λ is a scalar known as the eigenvalue associated with the eigenvector v

How to Calculate Eigenvectors?

To calculate the eigenvectors of a matrix, the eigenvector calculator with steps follows some important steps to get the exact solution of your given system. These steps are:

Suppose the eigenvector of the given system is,

$$ A \;=\; \biggr[\begin{matrix}-5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] $$

Step-by-Step Process to Find Eigenvectors

First, the eigenspace basis calculator converts the given system of equations into an augmented matrix, then finds the determinant with the help of the value equation that used in the determinant as

$$ det( A - λI) \;=\; 0 $$

$$ det \biggr(λ \biggr[\begin{matrix} 1 & 0 \\ 0 & 1 \\ \end{matrix} \biggr] - \biggr[\begin{matrix} -5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] \biggr)I) \;=\; 0 $$

$$ det \biggr[\begin{matrix} λ + 5 & -2 \\ 7 & λ - 4 \\ \end{matrix} \biggr] \;=\; 0 $$

Then the eigenbasis calculator solves the determinant and you get the quadratic equation,

$$ λ^2 + λ - 6 \;=\; 0 $$

$$ λ^2 + 3λ - 2λ - 6 \;=\; 0 $$

$$ λ(\lambda + 3) - 2(\lambda + 3) \;=\; 0 $$

$$ (\lambda + 3) (\lambda - 2) \;=\; 0 $$

$$ (\lambda + 3) \;=\; 0 \;,\; (\lambda - 2) \;=\; 0 $$

$$ λ \;=\; -3 \;,\; λ \;=\; 2 $$

Now the eigenvector finder adds these scalar values into the above equation as:

For λ=2 as det(2I-λ)=0

$$ \biggr(2 \biggr[\begin{matrix} 1 & 0 \\ 0 & 1 \\ \end{matrix} \biggr] - \biggr[\begin{matrix} -5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] \biggr) \biggr[\begin{matrix} x \\ y \\ \end{matrix} \biggr] \;=\; \biggr[\begin{matrix} 0 \\ 0 \\ \end{matrix} \biggr] $$

$$ \biggr[\begin{matrix} 7 & -2 \\ 7 & -2 \\ \end{matrix} \biggr] \biggr[\begin{matrix} x \\ y \\ \end{matrix} \biggr] \;=\; \biggr[\begin{matrix} 0 \\ 0 \\ \end{matrix} \biggr] $$

Now the eigenvector solver solves the square matrix A and transforms it into the identity matrix.

$$ \biggr[\begin{array}{rr|rr} 7 & -2 & 0 \\ 7 & -2 & 0 \\ \end{array} \biggr] \rightarrow … \rightarrow \biggr[\begin{array}{rr|rr} 1 & -2/7 & 0 \\ 0 & 0 & 0 \\ \end{array} \biggr] $$

$$ \biggr[\begin{matrix} 2/7 s \\ s \\ \end{matrix} \biggr] \;=\; s \biggr[\begin{matrix} 2/7 \\ 1 \\ \end{matrix} \biggr] $$

Further, the eigenvector calculator with steps simplifies the matrix and gets the value of s by multiplying 7.

$$ t \biggr[\begin{matrix} 2 \\ 7 \\ \end{matrix} \biggr] $$

For λ =2 the eigenvector is,

$$ \begin{matrix} 2 \\ 7 \\ \end{matrix} $$

For λ= -3 as det(-3I-λ)=0

$$ \biggr((-3) \biggr[\begin{matrix} 1 & 0 \\ 0 & 1 \\ \end{matrix} \biggr] - \biggr[\begin{matrix} -5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] \biggr) \biggr[\begin{matrix} x \\ y \\ \end{matrix} \biggr] \;=\; \biggr[\begin{matrix} 0 \\ 0 \\ \end{matrix} \biggr] $$

$$ \biggr[\begin{matrix} 2 & -2 \\ 7 & -7 \\ \end{matrix} \biggr] \biggr[\begin{matrix} x \\ y \\ \end{matrix} \;=\; \biggr[\begin{matrix} 0 \\ 0 \\ \end{matrix} \biggr] $$

The eigenspace calculator repeats the above method that is used for λ= 2 to get the eigenvector of the given equation,

$$ \biggr[\begin{matrix} s \\ s \\ \end{matrix} \;=\; s \biggr[\begin{matrix} 1 \\ 1 \\ \end{matrix} \biggr] $$

For λ =-3 the eigenvector is

$$ \biggr[\begin{matrix} 1 \\ 1 \\ \end{matrix} \biggr] $$

For verification of whether the eigenvector is correct or not, it verifies AX = 2X,

$$ \biggr[\begin{matrix} -5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] \biggr[\begin{matrix} 2 \\ 7 \\ \end{matrix} \biggr] \;=\; \biggr[\begin{matrix} 4 \\ 14 \\ \end{matrix} \biggr] 2 \biggr[\begin{matrix} 2 \\ 7 \\ \end{matrix} \biggr] $$

Verify that AX = -3X for the basic eigenvector,

$$ \biggr[\begin{matrix} -5 & 2 \\ -7 & 4 \\ \end{matrix} \biggr] \biggr[\begin{matrix} 1 \\ 1 \\ \end{matrix} \biggr] \;=\; \biggr[\begin{matrix} -3 \\ -3 \\ \end{matrix} \biggr] -3 \biggr[\begin{matrix} 1 \\ 1 \\ \end{matrix} \biggr] $$

After verification, we know our eigenvectors are correct for the given system of linear equations.

How to Use Eigenvector Calculator?

The eigenspace basis calculator has a simple interface, that enables you to use it to evaluate the eigenvector from the given system of equation questions. Before adding the input for the solutions of the eigenvector problem, you must follow some simple steps. These steps are:

  1. Select the order of the matrix from the list for the eigenvector solution.
  2. Enter the matrix element as per your matrix order in the input box.
  3. Review your input value for the eigenvector solution before hitting the calculate button to start the calculation process
  4. Click on the “Calculate” button of the eigenbasis calculator to get the desired result of your given eigenvector problem.
  5. If you want to try out our eigenvector finder to check its accuracy in solution then must try the load example.
  6. Click on the “Recalculate” button to get a new page for solving more linear system questions.

Outcome of Eigenspace Calculator:

Eigenvector Calculator gives you the solution to a given problem when you add the input to it. It provides you with solutions of linear systems. It may contain as:

  • Result Option

You can click on the result option and it provides you with a solution for the eigenvector.

  • Possible Step

When you click on the possible steps option it provides you with the solution of the eigenvector from the given linear equation.

Advantages of Eigenspace Basis Calculator

The eigenvector solver gives you tons of advantages whenever you use it to calculate a system of linear equations and to get its solution immediately. These advantages are:

  • Our eigenspace calculator saves the time and effort that you consume in solving complex eigenvector questions in a few seconds
  • It is a free-of-cost tool that provides you solution of eigenvectors from the given system without paying a single penny.
  • You can use this eigenbasis calculator for practice so that you get a strong hold on this concept.
  • It is an adaptive tool that allows you to find the correct solution from the given system of equations for eigenvectors.
  • The eigenvector calculator with steps is a trustworthy tool that provides you with accurate solutions as per your input system to calculate the eigenvector problem.
Related References
Frequently Ask Questions

What if the eigenvector is zero?

If the eigenvector corresponding to an eigenvalue is zero, it is not a valid eigenvector for the system of linear equations. If no eigenvector is associated with that particular eigenvalue, or the matrix has fewer linearly independent eigenvectors than its order then the vector becomes zero

In both cases, it explains that the transformation represented by the matrix does not have a unique direction that corresponds to that eigenvalue.

What if an eigenvector is the diagonal?

If an eigenvector is diagonal, it means that it aligns with one of the axes coordinates of the vector space. In a diagonal matrix, the eigenvectors are aligned with the coordinate axes.

This is because when a diagonal matrix is multiplied by its eigenvector, the result is an eigenvector itself, as it is the corresponding eigenvalue.

So, if you find that one of your eigenvectors is diagonal, it simply means that the transformation represented by the matrix has the direction of that axis by the corresponding eigenvalue. This is a characteristic property of diagonal matrices.

What is a nontrivial eigenvector?

A non-trivial eigenvector is an eigenvector that is not a zero vector. In other words, these vectors have some magnitude and direction, rather than being a vector of all zeros.

In linear algebra, when you're solving for eigenvectors and eigenvalues of a matrix, a non-trivial eigenvector is necessary because it represents direction within the vector space that has the linear transformation to represent the matrix.

How to find unit eigenvector?

To find a unit eigenvector corresponding to a specific eigenvalue, you would typically follow these steps:

  1. Find the Eigenvalues: Compute the eigenvalues of the matrix by solving the characteristic equation det ⁡(A − λI) = 0
  2. Find Eigenvectors: For each eigenvalue, solve the system of equations (A−λI) v = 0 to find the corresponding eigenvectors.
  3. Normalize Eigenvectors: Once you have found an eigenvector, normalize it by dividing each component by its magnitude u = ∥v∥/v, thus obtaining a unit vector.

What is eigenvalue and eigenvector?

Eigenvalues and eigenvectors are important concepts in linear algebra that have a crucial role in understanding linear transformations in matrices.

Eigenvalue:

An eigenvalue of a square matrix A is a scalar λ such that there exists a non-zero vector v. To calculate eigenvalue given condition should be followed:

$$ Av \;=\; λv $$

When a matrix is multiplied by one of its eigenvectors, the result is the eigenvector, with the scaling factor being the eigenvalue.

Eigenvector:

An eigenvector corresponding to an eigenvalue λ of a matrix A is a non-zero vector v such that satisfies the equation:

$$ Av \;=\; λv $$

Eigenvectors are vectors when transformed by the matrix, have a scalar factor, which is the corresponding eigenvalue. They represent directions in the vector space that remain unchanged under the linear transformation in the matrix.

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