Examples of using Another vector in English and their translations into Thai
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Computer
We construct another vector.
So this guy right here is going to be a member of Rm, another vector.
Let me draw another vector field.
Well first, this is going to give us another vector.
So let's say I have another vector here that's w, and it's also a member of Rn.
Now let me create another vector.
Let me define another vector that's in u, or that's in the span of these vectors. .
Well this equals another vector.
Let's build with another vector field that only has vectors in j direction, or the vertical direction.
The second one times another vector.
If I just added another vector here, if I just added the vector 1, 0, is S now a basis for r2?
I'm just going to get another vector.
And the ultimate would be, let me draw another vector field, the ultimate would be if I had this situation.
For example, if this spans for v, then so would-- let me add another vector.
So you're going to get another vector in Rn like that.
You can view the difference as, how do you get from one vector to another vector, right?
And you can imagine in three space, if you have one vector that looks like this and another vector that looks like this, two vectors that aren't collinear, they're going to define a kind of two-dimensional space.
So if you multiply any vector in Rm times some scalar, you're still going to have another vector in Rm.
If we add two vectors in Rm to each other, we get another vector in Rm because Rm is a valid subspace.
So in this case I have a T applied to one vector, and I'm summing it to a T applied to another vector.
When you apply mapping, you get another vector that's in Y.
And you could do that any vector plus any other vector on this line is going to equal another vector on this line.
When I take this product, I'm just going to get another vector, and what's it going to be?
And one thing we can do is, when I created this projection-- let me actually draw another projection of another line or another vector just so you get the idea.
If my vector a looks like that and my vector b is essentially another vector that's going in the same direction, then theta is 0.
So in this calculation, I have three times a vector plus a vector minus another vector divided by three.
The third one times another vector.
Similarly, let me define another vector.
You apply the linear transformation T to that, then you get another vector that's at set Z.
Now let's say that we have any another position vector function.