# vectorizing or speeding looped code

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Paul Schenk on 22 Sep 2020
Commented: Paul Schenk on 23 Sep 2020
i am trying to speed up some code with multiple functions and have found the one that takes the most time (it is all a converted fortran code). it runs a nested for loop 4 times (each with modified input). i've had some success elsewhere in the code eliminating slow points but this one just cant get right. any ideas? (in 2016b for compatability reasons)
for II= 1:N
XP(1)= 1.0D0;
for JJ=2:IORD1
XP(JJ)=XP(JJ-1)*double(A1(II));
end
for JJ= 1:IORD1
for KK= 1:IORD1
B(JJ,KK)=B(JJ,KK)+XP(JJ)*XP(KK);
end
C(JJ)=C(JJ)+XP(JJ)*A2(II);
end
end

Turlough Hughes on 22 Sep 2020
This should help, though I've already made assumptions about the sizes of arrays. How many rows/columns are in each variable?
XP(1) = 1;
for II= 1:N
XP(2:end) = XP(1:end-1)*double(A1(II));
B = B + XP(:).*XP(:).';
C = C + XP*A2(II);
end
##### 2 CommentsShowHide 1 older comment
Turlough Hughes on 22 Sep 2020
Did the code work?
Some questions: is XP a column vector/row vector? Does A2 have as many elements as XP? Also, does B contain IORD1 rows and columns? Similarly does C have IORD1 elements?

Paul Schenk on 22 Sep 2020
not as entered. tripled cpu time.
here are preallo's to variables:
ORDER_MAX = 20;
C = zeros(ORDER_MAX+1,1);
IORD=MAXOR;
IORD1=IORD+1;
B = single(zeros(ORDER_MAX+1,ORDER_MAX+1)); % Solution matrix
XP = double(zeros(ORDER_MAX+1,1)); % Array of solutions
##### 2 CommentsShowHide 1 older comment
Turlough Hughes on 23 Sep 2020
Vectorisation is usually faster but not always. Probably best to just provide a minimum working example (google it) of code that I (or others) can test and profile. Otherwise it really is just guess work.

Paul Schenk on 23 Sep 2020
hope this works for you. it is essentially a customized LS fit solution.
Paul Schenk on 23 Sep 2020
yes, my mistake!