Fastest (parallelized, maybe) way to run exponential fit function in a big matrix of data

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Brunno Machado de Campos
评论： Brunno Machado de Campos 2021-9-29
Dear experts,
I am trying to ensure the most optimized syntax to run a part of my code. In summary, I need to perform an exponential fit of a series with 6 points, but ~2 million times (independent samples). In other words, I have a reshaped matrix 6x2,000,000.
This is my code right now (each loop iteration takes 0.02 seconds what makes this process prohibitive):
Xax = [30;60;90;120;150]; % The constant X series.
f = @(b,Xax) b(1).*exp(b(2).*Xax); % Exponential model
% LoopMap == my 6x2,000,000 matrix
for k = 1:size(LoopMap,2) %parfor? Any GPU model (if this is and optimal example)
bet = fminsearch(@(b) norm(LoopMap(:,k) - f(b,Xax)),[0;0]);
CoefAtmp(1,k) = abs(1/bet(2));
end
My PC is a standard machine (8th gen Core i7, 32Gb ram) with a not exceptional graphic card.
Thank you all in advance.

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采纳的回答

Matt J
Matt J 2021-9-29
编辑：Matt J 2021-9-29
Additional speed-up should also be possible by reducing your problem to a 1-variable estimation and removing subsref operations from your objective function.
Xax = [30;60;90;120;150]; % The constant X series.
B2=nan(1,size(LoopMap,2));
Initial=Xa.^[0,1]\log(LoopMap);
Initial=Initial(2,:);
for k = 1:size(LoopMap,2) %parfor? Any GPU model (if this is and optimal example)
y=LoopMap(:,k);
B2(k) = fminsearch( @(b2) objective(b2,Xax,y) , Initial(k));
end
CoefAtmp = abs(1./B2);
function cost=objective(b2,Xax,y)
ex=exp(b2*Xa);
b1=ex\y;
cost=norm(b1*ex-y);
end

更多回答（1 个）

Matt J
Matt J 2021-9-29
编辑：Matt J 2021-9-29
If it's acceptable to you, a log-linear least squares fit can be done very fast and without loops
A=Xax.^[0,1];
Bet=A\log(LoopMap);
CoefAtmp=1./abs(Bet(2,:));
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Matt J
Matt J 2021-9-29
编辑：Matt J 2021-9-29
Both methods are exponential fits.
Do you mean you must have a linear least squares objective? If so, intiailizing fminsearch with the log-linear solution will probably speed things up.
However, you should first check that the linear and the loglinear methods give significantly different results over a small but representative subset of your LoopMap(:,k). If not, you can use that to defend the loglinear method to the academic community.
Brunno Machado de Campos
Brunno Machado de Campos 2021-9-29
Right! Sure, thank you. Since a simple exponential fits well the model, both option will give very similar answeres.
So (just to documment):
For an example were sig = [527.32;398.95;284.22;197.562;155.01];
1)
Xax = [30;60;90;120;150]; % The constant X series.
f = @(b,Xax) b(1).*exp(b(2).*Xax);
bet = fminsearch(@(b) norm(sig - f(b,Xax)),[0;0]);
CoefAtmp1 = abs(1/bet(2));
0.02 seconds
2)
Xax = [30;60;90;120;150]; % The constant X series.
M = bsxfun(@power,Xax,0:1);
c2 = M\log(sig);
CoefAtmp2 = abs(1./c2(2));
0.000002 seconds
Nothing more to do here, thanks again!

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