Let A) 1 -3 -5 1 1 -2 1 -3 1 1 1 4 and b= -6 1 1 6
a) apply the Gram-Schmidt process to determine an orthonormal basis for R(A)
I have no idea
so b is the basis for the solution space
sure
so is b only one vector?
Yes
i am not sure, but i think we can apply an alternative form of the gram-schmidt, but if anybody else has any ideas, feel free to chime in
last time i did something like this i crossed v1xv2 and v1xv3 to find othogonal vectors dunno if itll work here tho;
dunno how we would cross 2, 4component tho .. yet :)
from what i have seen with the gram shmidth , B has two vectors at least
matrix A is already linear independant; it just aint orthogonal
had to look up what an orthonormal was; its an orthogonal set of vectors that are unit length
what is vector b? is it defined in terms of the given matrix, or is it defined in termsof the orthonromal components?
or is b in terms of the standard components of 1000, 0100, 0010
I am reading up on chapter too
the GS v1 = x1; W1 = span{x1} v2 = x2 - proj(W1)x2 -3 x2.x1 1 - ----- x1 -3 x1.x1 1
1 1 1 1 -3 1 -3 1 ----------- -3+1-3+1 = -4 ---------- --- = -1 1+1+1+1 = 4 v2 = -3 1 -2 1 1 2 -3 + 1 = -2 1 1 2
v3 = x3 - proj(W2) x3 which gets tricky
W2 = span{v1,v2}
proj(W2)x3 = the sum of the same process above but using x3 in place of x2, and dotting it to v1 and then v2
-5-4 1 4 1 1 1 1 --------- -5-4+1+4 = -4 ---------- --- <1,1,1,1> = <-1,-1,-1,-1> 1+1+1+1 = 4 -5-4 1 4 -2 2-2 2 --------- -10-8-2+8 = -12 ----------- --- <-2,2,-2,2> = <3,-3,3,-3> 4+4+4+4 = 16 -1-1-1 -1 3 -3 3 -3 --------- <2,-4,2,-4> = v3
\[\begin{vmatrix}1&-2&2\\1&2&-4\\1&-2&2\\1&-2&-4 \end{vmatrix}\] would be the orthogonal basis; the orthonormal is to unit length those
middle bottom; not negative; just 2
thanks , amistre I will study this
i think i made a mistake in the v3 parts
what i have for v3 is spose to be subtracted from x3 i think
-5-4 1 4 x3 -2,4,-2,4 -(badv3) ---------- -7,0,-1,8 = v3
much better :) http://www.wolframalpha.com/input/?i=rref%7B%7B1%2C-2%2C-7%7D%2C%7B1%2C2%2C0%7D%2C%7B1%2C-2%2C-1%7D%2C%7B1%2C2%2C8%7D%7D
again, these are orthogonal basis; orthoNormal is same but unit these vectors
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