Eigenvector Calculator With Work

Solve matrices with shown work and accuracy checks. Compare eigenvalues, vectors, traces, and determinants quickly. Download your clear results after each finished matrix calculation.

Calculator Input

Formula Used

The calculator solves the characteristic equation below.

det(A - lambda I) = 0

After each eigenvalue is found, the vector is taken from the null space below.

(A - lambda I)v = 0

The answer is checked with Av = lambda v. A small residual means the vector is accurate.

How To Use This Calculator

  1. Select a 2 by 2 or 3 by 3 matrix.
  2. Enter every matrix entry in the visible fields.
  3. Choose decimal places and a tolerance value.
  4. Select a vector normalization style.
  5. Press the calculate button to show work above the form.
  6. Use CSV or PDF downloads for saved results.

Example Data Table

Matrix Expected eigenvalues Sample eigenvectors Use case
[[4, 1], [2, 3]] 5, 2 [1, 1], [-0.5, 1] Basic real roots
[[2, 0, 0], [0, 3, 1], [0, 1, 3]] 2, 4, 2 [1, 0, 0], [0, 1, 1] Repeated value check
[[0, -1], [1, 0]] i, -i Complex directions Rotation matrix

Eigenvector Calculator With Work Guide

What This Tool Does

This calculator helps students inspect a square matrix. It finds eigenvalues. Then it builds the eigenvector equations. The output shows the characteristic polynomial, determinant parts, null space setup, and residual check. You can use a 2 by 2 matrix or a 3 by 3 matrix. Decimal control helps you match homework formatting. The tool supports row scaling through vector normalization.

Why Eigenvectors Matter

Eigenvectors show directions that do not rotate under a linear transformation. Only their length may change. The related scale factor is the eigenvalue. This idea appears in systems, graphics, statistics, vibration, ranking, and data reduction. A matrix may stretch one direction, shrink another direction, or reverse a direction. Eigenvectors expose those special directions. They make complicated transformations easier to understand.

How The Work Is Shown

The calculator starts with the matrix entries. It computes trace and determinant values. For a 3 by 3 matrix, it uses principal minor terms. These values create the characteristic equation. Solving that equation gives each eigenvalue. For every eigenvalue, the calculator forms A minus lambda I. It solves the homogeneous system. A nonzero vector from that null space becomes an eigenvector. The residual norm checks accuracy by comparing Av with lambda v.

Helpful Input Tips

Enter exact decimals when possible. Avoid rounded data until the final step. Use the tolerance field when repeated roots appear. A smaller tolerance keeps more digits. A larger tolerance may merge tiny numerical noise. Choose unit normalization for comparison. Choose first nonzero component normalization for hand written work. Leave normalization off when raw direction vectors are acceptable.

Reading The Results

The result area appears below the header and above the form. Each eigenvalue has its own work block. The matrix A minus lambda I is shown. The selected vector is listed in column form. The residual tells whether the vector fits the equation. A very small residual means the answer is consistent. Download the CSV for spreadsheet review. Use the PDF button for a printable record.

Best Uses

Use this page for checking algebra, preparing notes, and exploring examples. It is not a proof engine. Numerical roots may vary slightly. Always review rounded answers against your course rules.

FAQs

What is an eigenvector?

An eigenvector is a nonzero vector that keeps its direction after a matrix transformation. Its length may change. The change factor is the eigenvalue.

What matrix sizes are supported?

This page supports 2 by 2 and 3 by 3 square matrices. Those sizes cover many class problems and common quick checks.

Does it show the work?

Yes. It shows trace, determinant parts, the characteristic equation, A minus lambda I, the selected vector, normalization, and residual checking.

Can it handle complex eigenvalues?

Yes. Complex roots are displayed with i notation. The vector routine also works with complex entries for many practical examples.

Why do eigenvectors look different from my book?

Eigenvectors can be scaled many ways. If two vectors point in the same direction, both can be correct. Change normalization to match your expected format.

What does residual norm mean?

Residual norm measures the error in Av equals lambda v. A value near zero means the displayed vector fits the displayed eigenvalue.

When should I adjust tolerance?

Adjust tolerance when roots are repeated or nearly repeated. A practical value like 1e-9 works for most classroom matrices.

Can I save my results?

Yes. Use the CSV button for spreadsheet data. Use the PDF button to save a printable copy of the displayed work.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.