##### Objectives
1. Understand the basic properties of orthogonal complements.
2. Learn to compute the orthogonal complement of a subspace.
3. Recipes: shortcuts for computing the orthogonal complements of common subspaces.
4. Picture: orthogonal complements in and
5. Theorem: row rank equals column rank.
6. Vocabulary words: orthogonal complement, row space.

It will be important to compute the set of all vectors that are orthogonal to a given set of vectors. It turns out that a vector is orthogonal to a set of vectors if and only if it is orthogonal to the span of those vectors, which is a subspace, so we restrict ourselves to the case of subspaces.

# Subsection6.2.1Definition of the Orthogonal Complement

Taking the orthogonal complement is an operation that is performed on subspaces.

##### Definition

Let be a subspace of Its orthogonal complement is the subspace

The symbol is sometimes read perp.”

This is the set of all vectors in that are orthogonal to all of the vectors in We will show below that is indeed a subspace.

##### Note

We now have two similar-looking pieces of notation:

Try not to confuse the two.

##### Pictures of orthogonal complements

The orthogonal complement of a line through the origin in is the perpendicular line

The orthogonal complement of a line in is the perpendicular plane

The orthogonal complement of a plane in is the perpendicular line

We see in the above pictures that

##### Example

The orthogonal complement of is since the zero vector is the only vector that is orthogonal to all of the vectors in

For the same reason, we have

# Subsection6.2.2Computing Orthogonal Complements

Since any subspace is a span, the following proposition gives a recipe for computing the orthogonal complement of any subspace. However, below we will give several shortcuts for computing the orthogonal complements of other common kinds of subspaces–in particular, null spaces. To compute the orthogonal complement of a general subspace, usually it is best to rewrite the subspace as the column space or null space of a matrix, as in this important note in Section 2.6.

Since column spaces are the same as spans, we can rephrase the proposition as follows. Let be vectors in and let Then

Again, it is important to be able to go easily back and forth between spans and column spaces. If you are handed a span, you can apply the proposition once you have rewritten your span as a column space.

By the proposition, computing the orthogonal complement of a span means solving a system of linear equations. For example, if

then is the solution set of the homogeneous linear system associated to the matrix

This is the solution set of the system of equations

In order to find shortcuts for computing orthogonal complements, we need the following basic facts. Looking back the the above examples, all of these facts should be believable.

See these paragraphs  for pictures of the second property. As for the third: for example, if is a (-dimensional) plane in then is another (-dimensional) plane. Explicitly, we have

the orthogonal complement of the -plane is the -plane.

##### Definition

The row space of a matrix is the span of the rows of and is denoted

If is an matrix, then the rows of are vectors with entries, so is a subspace of Equivalently, since the rows of are the columns of the row space of is the column space of

We showed in the above proposition that if has rows then

Taking orthogonal complements of both sides and using the second fact gives

Replacing by and remembering that gives

To summarize:

##### Recipes: Shortcuts for computing orthogonal complements

For any vectors we have

For any matrix we have

As mentioned in the beginning of this subsection, in order to compute the orthogonal complement of a general subspace, usually it is best to rewrite the subspace as the column space or null space of a matrix.

# Subsection6.2.3Row rank and column rank

Suppose that is an matrix. Let us refer to the dimensions of and as the row rank and the column rank of (note that the column rank of is the same as the rank of ). The next theorem says that the row and column ranks are the same. This is surprising for a couple of reasons. First, lies in and lies in Also, the theorem implies that and have the same number of pivots, even though the reduced row echelon forms of and have nothing to do with each other otherwise.

##### Proof

In particular, by this corollary in Section 2.7 both the row rank and the column rank are equal to the number of pivots of