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Linear Independence Calculator – Check Vector Independence

Check if vectors are linearly independent or dependent.

Check Independence

Enter each vector on a new line. Separate components with commas or spaces.

How to Use

  1. Enter each vector on a separate line with components separated by commas or spaces
  2. For example: 1, 2, 3 on one line and 4, 5, 6 on the next
  3. Click calculate to determine if the vectors are linearly independent
  4. Review the rank, determinant (for square matrices), and RREF

What is Linear Independence?

A set of vectors is linearly independent if no vector in the set can be written as a linear combination of the others. Equivalently, the only solution to c₁v₁ + c₂v₂ + ... + cₙvₙ = 0 is when all coefficients c₁, c₂, ..., cₙ are zero.

If at least one non-trivial combination exists (some coefficients are non-zero), the vectors are linearly dependent.

How to Check Linear Independence

  • Form a matrix with the vectors as rows (or columns)
  • Apply Gaussian elimination to reduce to row echelon form
  • Count the number of non-zero rows (the rank)
  • If rank equals the number of vectors, they are linearly independent

For square matrices, you can also check the determinant: if det ≠ 0, the vectors are independent.

Applications of Linear Independence

  • Finding bases for vector spaces
  • Solving systems of linear equations
  • Determining if a transformation is invertible
  • Signal processing and data compression
  • Machine learning feature selection

Frequently Asked Questions

What does it mean if vectors are linearly dependent?
Linearly dependent vectors contain redundancy—at least one vector can be expressed as a combination of the others. This means they don't span as many dimensions as there are vectors.
Can more vectors than the dimension be independent?
No. In an n-dimensional space, at most n vectors can be linearly independent. Any set with more than n vectors must be dependent.
What is the relationship between rank and independence?
The rank of a matrix equals the maximum number of linearly independent rows (or columns). If you have k vectors and the rank is k, all vectors are independent.