ok, gonna try this again. i was in tears after being ridiculed this morning. Match the functions f with the plots of their gradient vector fields labeled A-D. f(x,y)=sqrt(x^2+y^2) 2. f(x,y)=x^2−y^2 3. f(x,y)=x^2+y^2 4. f(x,y)=xy
1.* f(x,y)=sqrt(x^2+y^2) 2. f(x,y)=x^2−y^2 3. f(x,y)=x^2+y^2 4. f(x,y)=xy
that didn't actually answer anything. just restated what i already said. and you're supposed to be blocked. leave me alone.
@ganeshie8 , can you help me here? pretty please?
the easiest function is 3. f(x,y)=x^2+y^2
gradient = <2x, 2y> which clearly points in the same direction as position vector, right ?
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at each point (x, y) in the plane, the gradiant points in the same direction as position vector. the magnitude would be twice larger
oh, so it would be D. ok. not that clear on drawing vectors.
C looks good
use this to draw if u want http://kevinmehall.net/p/equationexplorer/vectorfield.html
for x^2+y^2 it's C? if it's the same magnitude, wouldn't it be D?
yeah, i tried using that this morning and it gave me fields that weren't any of my choices so i was confused.
<2x, 2y> the size of vectors must increase as you go away from center
i don't see them increasing in option D, so..
oh. didn't know they had to decrease as they moved from the origin. is that cause they double in size each time?
exactly!
as the name says, `gradient vector` is just a vector defined at `each point`
so it's C for x^2+y^2
Yep!
ok, but then x^2-y^2 is a similar vector <2x, -2y> which would suggest to me that it goes toward the origin, but i don't see any like that.
<2x, -2y> Notice that when y is positive, -2y is negative that means the vector shoots DOWN in I, II quadrants
b is the only one that does that, but the vectors there are curved. why?
because each vector has "x" component also
the y component is down but the x component is positive in quadrant I, so it faces right+down in quadrant I
ok, so x^2-y^2 is B. is xy D cause it doesn't increase or decrease? stays constant? just <y, x>
its number 3
<y, x> is NOT constant it changes with position
i mean the magnitude is not increasing like when it was doubling for x^2+y^2. sorry
an easy way to identify this is : on `y axis`, x=0 so the gradient would be : <y, 0>
that means, on y axis, the x component of gradient is increases as u move up
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