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numerical integration - Slow evaluation of NIntegrate when used as a pure function


I asked a perhaps related question here.


Here is my code in below. The goal is that to define a function which must be integrated numerically. The function itself first is calculated over different times, then I give it another input and finally it is integrated . The time needed to complete calculations is 57 seconds. I want to use this function inside NMinimize to obtain some parameters. However, I removed almost everything to make the problem clear. I think one reason for slow calculation is BesselJZero function. Note that this function in my application has a variable input but I made it fix for simplicity. Without it calculation is done in 10 seconds, but still it is slow. As I said I need to use this function in NMinimize so slow evaluation makes it longer to find the desired parameters. I have the same code in Matlab. It calculates in 0.28 seconds.


What am I doing wrong in Mathematica?


When there is a few time point and hence the length of the list which NIntegrate act upon is small, there isn't any difference between Mathematica and Matlab. Yet by increasing the number of elements of this list Mathematica is left behind Matlab. Is this a clue to solve this problem?!


Mathematica code:


taxis = Table[i, {i, 1, 2046, 2}];
model = Sum[

Exp[-(v)^2*N@BesselJZero[0.5, n]*c*t] /. {c -> #1, v -> #2, t -> #3}, {n, 1, 30}];
model2 = Evaluate[model] &;(* I make a pure function from the previous expression*)
model3 = NIntegrate[model2[#1, v, taxis], {v, 0, 100}, MinRecursion -> 11 , MaxRecursion -> 12, AccuracyGoal -> 5] &;(* I put the time points in the pure function model2 and the integrate over v and define a pure function *)
Total[model3[1.]] // AbsoluteTiming

Matlab code, you need besselzero code which can be downloaded from here. If you don't want to download that code you can evaluate it by removing the commented part and bz(n):


function G = test(p)
c = p(1);
t = 1:2:2046;
iter = 30;

bz = besselzero((3/2)-1,iter,1);% If you don't want to download besselzero function delete this line and bz(n) in the below
    Sum = 0;
    for n = 1:iter
        Sum = Sum + integral(@(v) exp(-(v^2)*bz(n)*c*t),0,100,'ArrayValued',true);
    end
G = sum(Sum) ;
end

run Matlab's code:


clc 

clear all
tic
test(1)
toc

Note than the upper limit on integration is 100 which meant to represent infinity. When I used infinity I got some errors.


Edit


I can't see how this might help. But here is another example which cannot be integrated in a closed form:


taxis = Table[2.*i, {i, 1, 1023}];
model = Sum[

Exp[-(v^(-2/d)+v)*N@BesselJZero[0.5*d-1, n]*c*t] /. {d->#1, c -> #2, v -> #3, t -> #4}, {n, 1, 30}];
model2 = Evaluate[model] &;(* I make a pure function from the previous expression*)
model3 = NIntegrate[model2[#1,#2, v, taxis], {v, 0, 100}, MinRecursion -> 11 , MaxRecursion -> 12, AccuracyGoal -> 5] &;(* I put the time points in the pure function model2 and the integrate over v and define a pure function *)
Total[model3[1.65,1.]] // AbsoluteTiming

The example works. Before I missed a minus sign so it didn't work. I changed the taxis in Mathematica code to make it the same as t in Matlab code, not a big difference though.


Edit2


This code runs two times faster than the example shown in the question(not the one in the edit section)


the example in the question:


model3 = NIntegrate[model2[#1, v, taxis], {v, 0, 100}, MinRecursion -> 11 , MaxRecursion -> 12, AccuracyGoal -> 5] &;


faster version:


model4 = NIntegrate[Total[model2[#1, v, taxis]], {v, 0, 100},MinRecursion -> 11, MaxRecursion -> 12, AccuracyGoal -> 5] &;

In the first one I ran NIntegrate over a list and finally in the next line I summed all the terms. In the improved one at first I summed the terms and then used NIntegrate. Yet, it is much slower than Matlab.




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