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What does a matrix do, and which outputs can disappear?

Read a column as an arrow, then a matrix as an action on that arrow. Solving an equation becomes a question about reachable targets, giving collapse, eigenvectors and rotations a geometric interpretation.

Questions in this reading · 8
  1. What do the numbers in a vector describe?
  2. What does multiplying a vector mean?
  3. What is the same in a diagram and a column calculation?
  4. How can columns explain a matrix equation?
  5. Which equations survive a collapse in dimension?
  6. Which outputs disappear when a transformation collapses space?
  7. Why shift a matrix before looking for an eigenvector?
  8. How do rotations change in four dimensions?

What do the numbers in a vector describe?

Translate between an arrow and a list of coordinates. Each representation makes a different aspect of the same object easier to inspect.

Compare original passages and timestamps · 2
the geometric understanding is what lets you judge what tools to use
Essence of linear algebra preview · ▶ 1:08–1:24
a language to describe space and the manipulation of space using numbers that can be crunched and run through a computer.
Vectors | Chapter 1, Essence of linear algebra · ▶ 9:00–9:11

What does multiplying a vector mean?

Scaling one vector and combining two vectors are different operations. Check the operands and the type of result before interpreting the notation.

Compare original passages and timestamps · 2
these two fundamental operations, vector addition and scalar multiplication.
Vectors | Chapter 1, Essence of linear algebra · ▶ 8:14–8:19
multiplying two vectors together is the dot product plus the wedge product
Why can't you multiply vectors? · ▶ 38:28–38:37

What is the same in a diagram and a column calculation?

Switch between geometric motion and coordinate arithmetic to see one linear operation from two angles. Strang’s column combination is a worked example; neither representation is best for every task.

Compare original passages and timestamps · 3
the ability to translate back and forth between them
Vectors | Chapter 1, Essence of linear algebra · ▶ 8:45–8:49
the geometric understanding is what lets you judge what tools to use
Essence of linear algebra preview · ▶ 1:08–1:24
A times x is a combination of the columns of A.
Lec 1 | MIT 18.06 Linear Algebra, Spring 2005 · ▶ 38:24–38:36

How can columns explain a matrix equation?

Translate coordinates into a geometric picture. The columns of a matrix then show which combinations of vectors can reach the desired output.

Compare original passages and timestamps · 2
the ability to translate back and forth between them
Vectors | Chapter 1, Essence of linear algebra · ▶ 8:45–8:49
linear combinations of the columns fill three dimensional space
Lec 1 | MIT 18.06 Linear Algebra, Spring 2005 · ▶ 28:43–29:03

Which equations survive a collapse in dimension?

Once a transformation flattens space, reachable outputs lie in its image. A target inside that image and one outside it lead to different answers.

Compare original passages and timestamps · 2
A determinant of 0 would mean that all of space is squished onto something with 0 volume
The determinant | Chapter 6, Essence of linear algebra · ▶ 6:32–6:42
If these three columns all lie in the same plane, then their combinations will lie in that same plane.
Lec 1 | MIT 18.06 Linear Algebra, Spring 2005 · ▶ 31:06–31:18

Which outputs disappear when a transformation collapses space?

Some target vectors fall outside the image after a collapse. In the eigenvalue example it is A minus lambda times the identity that becomes singular; this does not make every system unsolvable.

Compare original passages and timestamps · 3
A determinant of 0 would mean that all of space is squished onto something with 0 volume
The determinant | Chapter 6, Essence of linear algebra · ▶ 6:32–6:42
If these three columns all lie in the same plane, then their combinations will lie in that same plane.
Lec 1 | MIT 18.06 Linear Algebra, Spring 2005 · ▶ 31:06–31:18
make this determinant zero, meaning the tweaked transformation squishes space into a lower dimension
Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra · ▶ 7:58–8:07

Why shift a matrix before looking for an eigenvector?

A zero determinant signals collapse into a lower dimension. Applying that test to the shifted matrix explains how a nonzero vector can lie in its null space.

Compare original passages and timestamps · 2
A determinant of 0 would mean that all of space is squished onto something with 0 volume
The determinant | Chapter 6, Essence of linear algebra · ▶ 6:32–6:42
make this determinant zero, meaning the tweaked transformation squishes space into a lower dimension
Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra · ▶ 7:58–8:07

How do rotations change in four dimensions?

Compare a fixed axis in three dimensions with planes of rotation in four. The familiar axis description does not carry over unchanged.

Compare original passages and timestamps · 2
what you have found is the axis of rotation
Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra · ▶ 4:11–4:20
two separate 2D rotations, happening perpendicular to and in sync with each other
Visualizing the 4d numbers Quaternions · ▶ 23:19–23:34

Try it on your own example

Choose a matrix that flattens the plane. Draw one reachable and one unreachable target, then check both by combining its columns.