Nonnegative matrix

Imagine you have a big box filled with a bunch of smaller boxes, and each of these smaller boxes has a number written on it. These numbers could be anything, but for now let's imagine they're all positive (greater than zero) numbers.

When we put all of these boxes together, we can imagine them as a big grid or table, with the numbers written in each box. This is what we call a matrix.

Now, let's say that we only want to work with matrices where all of the numbers are non-negative, which means they can't be negative or zero – they have to be greater than zero. This is what we call a nonnegative matrix.

Why might we care about a nonnegative matrix? One reason is that they can be used to represent different types of data. For example, we could use a nonnegative matrix to represent the number of times different words appear in a set of documents, or the number of people who have certain characteristics in a population.

Another reason we might care about nonnegative matrices is that they have some interesting properties. For example, we can use nonnegative matrices to find patterns or structure in a set of data. We can also use them to solve certain types of problems, like finding the shortest path between two points.

So, a nonnegative matrix is just a big table of positive numbers. We might use it to represent data, and it has some special properties that can help us solve problems or find structure in the data.