This is the matrix of cofactors.

So let's just begin with space requirements. What are they for the adjacency matrix?

And then I apply the formula.

Joins adjacent cells when they are in the same row.

We now know that this is the final distance and then we delete that from the dist so far structure.

So, we start up v. We mark v.

France is adjacent to Spain.

The first law is two colorability.

Germany borders on France.

You can try computing when a graph is all connected. You put the neighbors on a list, the open list.

h shares borders with g and e and d . gcde

f shares borders with g , b , and a

The two villages adjoin each other.

These glial cells become activated.

It actually kind of matters, but zero seems like a reasonable choice.

And that is exactly what we found.

And it can actually be all the way to one if we have a cleek.

Okay, well, let's go to j and do a mark_component from j.

We mark A as visited and we add it to the open list.

So given a node v or computing the clustering coefficient involves

We're going to average it for the n nodes in the graph.

And then I have my alters, which are my friends.

You also have a rotation matrix that will rotate the model or anything else around the y axis.

I'm assuming here that the vertices are named 1, 2, 3, 4, et cetera all the way up to n.

This just means my

So if we take in a word say,

We've got this one, and this one, and this one, and this one.

And so on this slide, you know this matrix is the matrix A, and this matrix is the matrix A inverse.

Although I think a Hummer is as pretty close as a car gets to pure consumption.

So for each predictor, we're gonna have one beta or one regression coefficient.

I got a 2 by 2 matrix.

The embassy is located next to the Supreme Court.

Then what we're going to do is we're going to count up the number of links between these neighboring nodes.

E is already in there but we can add in F at the tree edge.

Let me expand the first one. This one over here.

And our instructions again say that we look at all the neighbors of v, w and z.

Then we go through its neighbors.

Matrix

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The way that we are going to do it is we pull it off the list then we go to all the neighbors of the node that we're working on which is p and any that are unmarked, we do two things to them.

In particular, we're going to make each region on the map correspond to a node on the graph and we're going to connect two nodes on the graph if they share a border and therefore can't have the same color on the map. a shares a border with b and f

learned from Wikipedia, the correct term is the adjugate of matrix a.

I think you see the pattern.

So the inverse of matrix a is equal to 1 over the determinant of a times the adjugate, or adjoint, of matrix a.

So the matrix A is a m by n dimensional matrix, so m rows and n columns and we are going to multiply that by a n by 1 matrix, in other words an n dimensional vector.