Examples of using Column vector in English and their translations into Arabic
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Colloquial
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Political
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Ecclesiastic
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Ecclesiastic
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Computer
B out as just a collection of column vectors.
This is a column vector but it has a length of 3, right?
So this guy could have n column vectors.
These are column vectors, so they actually have some verticality to them.
We could just write it as a set of column vectors.
You just go from a column vector to a row vector. .
And similarly, the matrix B is just a bunch of column vectors.
That's going to be the first row of A expressed as a column vector, so we can write it like this, 1, minus 1, 2 dot 0, 0, 1.
A matrix is justreally just a way of writing a set of column vectors.
We're essentially adding the column vectors of those two guys.
I haven't formally defined a row vector times a column vector.
So the next one, this row of A expressed as a column vector, 1, minus 1, 2, and we're going to dot it with this vector right there, 1, 1, 0.
And the matrix C canalso be represented as just a bunch of column vectors.
C1, A times the column vector C2-- these are just the different columns of this matrix-- and we just then have the matrix A times the column vector Cn.
And if I were to multiply that by soon. column vector matrix xy.
I'm going to replace d with d times a or a times d minus c times C1 in this column vector.
Because this row will have3 elements because there's 3 columns, and each column vector here will have 3 elements, because there's 3 rows.
So if you have a 1 and a 0,the 0 is going to cancel out anything but the first term in this column vector.
And then the nth column is going to be the matrix-- keep going--A times the column vector Bn, plus matrix A times the column vector C.
So I'm saying that my row operation I'm going toperform is equivalent to a linear transformation on the column vector.
Where the n × n matrix A has a nonzero determinant, and the vector x= ( x 1,… , x n) T{\displaystyle x( x{ 1},\ ldots, x{ n}){\ mathrm{T}}}is the column vector of the variables. Then the theorem states that in this case the system has a unique solution, whose individual values for the unknowns are given by.
The third column is going to be the matrix A times the column vector 1, 1, 0.
The values in the second parameter determine the upper boundaries of the intervals. The intervals include the upper boundaries.The returned array is a column vector and has one more element than the second parameter; the last element represents the number of all elements greater than the last value in second parameter. If the second parameter is empty, all values in the first parameter are counted.
And then the fourth column in our product vector is going to be the matrix A times the column vector 1, minus 1, 2.
And then, actually,and then we have our and then the second row of A dotted with this column vector, so we have 0, minus 2, 1, dotted with 0, 0, 1.
Where A i{\displaystyle A_{i}} is the matrix formed by replacing the i-th column of A by the column vector b.
Let's say we have some matrix A and let's say that its terms are, or its columns are v1-- column vector is v2, all the way to vn.
But we know that this product right here can be written be as each of thesescalar terms in x times its corresponding column vector in A.
But like any technology, can fail, no matter whether you use the machine from Electrolux and Bosch,or bought a column vector, Neva, Oasis, Astra or another manufacturer.
So AB,-- let me rewrite it-- AB, my product vector, is going to be equal to-- so this first column isthe matrix A times the column vector 1, 2, 3.