Examples of using First 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
It's collinear with this first vector.
The first vector in this second cross product.
You point your RiGHT thumb… in the direction of the first vector.
You take the first vector times the dot product of the.
Linear combination of these, if I just use this substitution right here, can be reduced to just a scalar multiple of my first vector.
This first vector is always being multiplied by the scalar x1.
So if I define my vectors this way, that's the first vector, that's the second vector, than I can rewrite my matrix A.
So this first vector right here, this yellow one, 1, 1, 1, it will look like this-- 1, 1, 1.
Using this result, the dot product of two matrices-- or sorry, the dot product of two vectors is equal to the transpose of the first vector as a kind of a matrix.
The second row is the first vector I'm taking the cross product of.
You take the first vector there, so vector B and you multiply that times the dot product of the other two vectors. .
We said in order for them to be linearly independent, the only solution to c1 times my first vector, 1, minus 1, 2, plus c2 times my second vector, 2, 1, 3, plus c3 times my third vector, minus 1, 0, 2.
So this is the first vector, and I put the tail of the second there, and then the sum of those two, as we predicted, should be equal to this last one.
OK, so now we're in the second row, and we get our row information from the first vector-- and let me do a red that I never use because I think it's kind of tacky, this red right here.
So if you take the first vector, put your index finger in the direction of the first vector, middle finger in the direction of the second vector, and your other fingers can do what they need to do.
Well, you write i, j, k on top like you're taking the cross product of any two three dimensional vectors, and then you take the first vector-- but it's really a vector operator, but it's this del operator.
Let's say the first vector is x, the second vector is y.
So this is a right-hand rule, essentially--… in the direction of the first vector… and then you put your index finger in the direction of… the second vector… right over here.
I could have c1 times the first vector, 1, minus 1, 2 plus some other arbitrary constant c2, some scalar, times the second vector, 2, 1, 2 plus some third scaling vector times the third vector minus 1, 0, 2.
And we get our row information from the first vector-- let me circle it with this color-- and it is 3 times 5 plus 4 times 7.
If I multiply minus 4 times our first vector, 2,1, that's c1, plus 3 times our second vector, 3,2 minus 1 times our third vector, 1,2 this should be equal to 0.
The only solution to the equation c1 times the first vector plus c2 times the second vector equaling the 0 vector, that the only solution to this is when both of these equal 0.
This scalar times this first column vector will essentially just get you-- what will this look like?
Let's say my first position vector is x0 and it is equal to minus 2, minus 2.
If you want to do this definition, we just have to turn this guy into a unit vector first.
So my first position vector is the zero vector. .
So it's going to be equal to i-- you're not used to seeing the unit vector written first, but we can switch the order.
And we know that Ax can be rewritten as this is equal to c times x1 times the first column vector in a, so a1 plus x2 times a2,xn all the way to plus xn times an.
So this product is going to be x1 times the first column vector of A, plus x2 times the second column vector of A, all the way to plus xn times the nth column vector of A.