How to make the code efficient?

I have made the following code .
x=[0:n-1];
y=[0:n-1];
k = 0;
for i=1:n
for j=1:n
if rem(((y(j))^2)-((x(i))^3)-2*(x(i))-3,n)==0
k = k+1;
xy_mtx(k,:) = [x(i) y(j)];
end
end
end
I want to make it efficient, as efficient as it can be. Is this possible

回答(3 个)

n=10;
[x,y]=meshgrid(0:n-1);
idx=mod(y.^2-x.^3-2*x-3,n)==0;
xy_mtx=[x(idx),y(idx)];

4 个评论

for n=1000, the code above is 180 times faster.
for n=10, the code above is 1.25 times faster.
How large is your n? n cannot be much larger than 2e4 or else the matrices get too large (too much memory).
You could try the help memory suggestions to try to increase memory, or you could add nested for-loop and do the meshgrids in batches of 2e4 (will be slower). Also keep in mind floating point limitations.
n=1e6;
xy_mtx=[];
for k=1:50
for j=1:50
[x,y]=meshgrid(0+(k-1)*2e4:min(2e4-1+(k-1)*2e4,n-1),0+(j-1)*2e4:min(2e4-1+(j-1)*2e4,n-1));
idx=mod(y.^2-x.^3-2*x-3,n)==0;
xy_mtx=[xy_mtx;x(idx),y(idx)];
end
end

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You can do things all at once cine you're not dependent on previous values.
n=4
n = 4
x=[0:n-1];
y=[0:n-1];
k = 0;
%%Orig_code
tic
for i=1:n
for j=1:n
if rem(((y(j))^2)-((x(i))^3)-2*(x(i))-3,n)==0
k = k+1;
xy_mtx(k,:) = [x(i) y(j)];
end
end
end
time1=toc;
tic
%%do everything at once
[X Y] = meshgrid(x,y); %generate combinations of x and y
REMmat = rem((Y.^2)-(X.^3)-2*(X)-3,n); %perform all rem calculations at once
[indexies]=find(REMmat==0); %find which index has a rem of 0
xy_mtxM = [X(indexies) Y(indexies)]; %put only the X and Y comb. where rem above is -
time2=toc;
disp([xy_mtx xy_mtxM])
3 0 3 0 3 2 3 2
disp(['time original:' num2str(time1) 's'])
time original:0.008312s
disp(['time new:' num2str(time2) 's'])
time new:0.016845s
disp(['time delta:' num2str(time1-time2) 's'])
time delta:-0.008533s

2 个评论

hm... not sure why the time improvement above is worse but locally it appears much better
Look at my code.

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Steven Lord
Steven Lord 2021-6-8

1 个投票

Are you trying to find the points on an elliptic curve over F_n?
You could probably try a variant of the naive approach given on this Wikipedia page.

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