I used to find it difficult to transform the Miller index in **close-packed hexagonal (HCP)**. I mean, it should not be difficult theoretically, but it seems to be hard to understand. Thus, I spend some time to describe it in Matrix Form, in order to make it easy to grasp, and easy to calculate by programming.

I have to confess that, this article is just a **PRACTICE**.

Perhaps it can be **the very first step** for me to dig into more challenging problems (I feel sorry to say that I’m not a good English user, but I am trying to make it better).

OK, now let’s start.

# Inference

The 3-axis coordination $[UVW]$ represents the **same direction** as the 4-axis coordination $[uvtw]$. And, as we know, $[UVW]$ and $[uvtw]$ are both **vectors**, which can be described with their own primitive vectors. So we have:

In geometry, we have a relationship between vectors, this is:

Also, since it is satiated to describe a vector in a plane with two primitive vectors rather than three, we have relationship between indexes as below:

Having solved all the equations above, we get:

An easier description with matrix is:

In linear algebra, theoretically we can solve vector equation $Ab=x$ by method like $A^{−1}b$, namely, the inversed $A$ times the vector $b$. Therefore, the inversed description of the matrix formula is: