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The Null Space and Solving Ax = 0

The null space collects every vector the matrix crushes to zero. Its size decides whether your equations have one solution or infinitely many — and it is tied to rank by a clean accounting law.

What gets sent to zero

The null space of A (also called its kernel) is the set of all vectors x with A*x = 0 — everything the matrix collapses onto the zero vector. The zero vector is always in there; the real question is whether anything else is. If only x = 0 solves A*x = 0, the columns are independent; if other x exist, the columns are dependent.

What it says about solutions

The null space controls the whole solution story for a system of equations A*x = b. If you find one solution p (a particular solution), then every solution looks like p plus something from the null space: x = p + n. So if the null space is just {0}, the solution is unique; if the null space is bigger, there are infinitely many solutions, all parallel shifts of one another.

  1. Reduce A to echelon form; pivot columns are independent, the rest are free.
  2. Each free column gives one free variable, and one independent null-space vector.
  3. No free variables -> null space is {0} -> A*x = b has at most one solution.

Rank + nullity = columns

Now the accounting law. Every column is either a pivot column (counted by the rank) or a free column (counted by the nullity). They cannot be both and nothing is left out, so they must add up. This is the rank-nullity theorem: rank + nullity = number of columns.

A is 3 x 4,  rank(A) = 2
columns = 4
nullity = columns - rank = 4 - 2 = 2

-> 2 pivot columns, 2 free columns
-> A*x = 0 has a 2-dimensional null space
-> A*x = b (if solvable) has infinitely many solutions
Four columns split into 2 pivot + 2 free, so the nullity is 2.