First we will handle the diagonalizable case.
Assume A is diagonalizable, A=VDV-1.
Thus AT=(V-1)TDVT,
and D= VT AT(V-1)T.
Finally we have that A= VVT AT(V-1)TV-1, hence A is similar to AT
with matrix VVT.
If A is not diagonalizable, then we must consider its Jordan canonical form,
A=VJV-1, where J is block diagonal with Jordan blocks along the diagonal.
Recall that a Jordan block of size m with eigenvalue at L is a mxm matrix having L along the diagonal and ones along the superdiagonal.
A Jordan block is similar to its transpose via the permutation that has ones along the antidiagonal, and zeros elsewhere.
With this in mind we proceed as in the diagonalizable case,
AT=(V-1)TJTVT.
There exists a block diagonal permutation matrix P such that
JT=PJPT, thus J=PTVT AT(V-1)TP.
Finally we have that A= VPTVT AT(V-1)TPV-1, hence A is similar to AT
with matrix VPTVT.
Q.E.D.
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The punnet square gives the results as probable because is it similar to rolling dice. If you have a die with four sides, when you roll it, the chance is 1 out of 4 that a certain number will show. You could roll it 20 times and it COULD show the same number 20 times. But the probability is 1 in 4 each roll. The Punnet square is the same.
I would show it as 3*sqrt(2).
Because the reels the show is stored in is square and if the tv wasnt square it would be like put a square in a circle
You can use the theorems like SSS, SSA to show that they are similar. For example if two triangles have the same 3 sides length or two side lengths equal and 1 angle equal they are similar. * * * * * That is congruent, not similar! Similar is a weaker requirement. All that is needed is that two corresponding angles are the same. Equivalently, the three corresponding sides are in the same proportion.
show that SQUARE MATRIX THE LINEAR DEPENDENCE OF THE ROW VECTOR?
First let's be clear on the definitions.A matrix M is orthogonal if MT=M-1Or multiply both sides by M and you have1) M MT=Ior2) MTM=IWhere I is the identity matrix.So our definition tells us a matrix is orthogonal if its transpose equals its inverse or if the product ( left or right) of the the matrix and its transpose is the identity.Now we want to show why the inverse of an orthogonal matrix is also orthogonal.Let A be orthogonal. We are assuming it is square since it has an inverse.Now we want to show that A-1 is orthogonal.We need to show that the inverse is equal to the transpose.Since A is orthogonal, A=ATLet's multiply both sides by A-1A-1 A= A-1 ATOr A-1 AT =ICompare this to the definition above in 1) (M MT=I)do you see how A-1 now fits the definition of orthogonal?Or course we could have multiplied on the left and then we would have arrived at 2) above.
that is not a ?
It is not possible to show that since it is not necessarily true.There is absolutely nothing in the information that is given in the question which implies that AB is not invertible.
nope
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For small matrices the simplest way is to show that its determinant is not zero.
no. in the film the show him looking up.
Larry Freeman has a very nice proof ( one you can find in most linear algebra texts) on his blogspot.I encourage you to look at it and go over it line by line.http://mathrefresher.blogspot.com/2007/06/column-space.html
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"Matrix ping pong is definitely not an online game. It is a show in which, two Japanese people play ping pong, while moving in matrix like motions, thus making it matrix ping pong."