Matlab Code To Approximate The Exponential Function - matlab

Does anyone know how to make the following Matlab code approximate the exponential function more accurately when dealing with large and negative real numbers?
For example when x = 1, the code works well, when x = -100, it returns an answer of 8.7364e+31 when it should be closer to 3.7201e-44.
The code is as follows:
s=1
a=1;
y=1;
for k=1:40
a=a/k;
y=y*x;
s=s+a*y;
end
s
Any assistance is appreciated, cheers.
EDIT:
Ok so the question is as follows:
Which mathematical function does this code approximate? (I say the exponential function.) Does it work when x = 1? (Yes.) Unfortunately, using this when x = -100 produces the answer s = 8.7364e+31. Your colleague believes that there is a silly bug in the program, and asks for your assistance. Explain the behaviour carefully and give a simple fix which produces a better result. [You must suggest a modification to the above code, or it's use. You must also check your simple fix works.]
So I somewhat understand that the problem surrounds large numbers when there is 16 (or more) orders of magnitude between terms, precision is lost, but the solution eludes me.
Thanks
EDIT:
So in the end I went with this:
s = 1;
x = -100;
a = 1;
y = 1;
x1 = 1;
for k=1:40
x1 = x/10;
a = a/k;
y = y*x1;
s = s + a*y;
end
s = s^10;
s
Not sure if it's completely correct but it returns some good approximations.
exp(-100) = 3.720075976020836e-044
s = 3.722053303838800e-044
After further analysis (and unfortunately submitting the assignment), I realised increasing the number of iterations, and thus increasing terms, further improves efficiency. In fact the following was even more efficient:
s = 1;
x = -100;
a = 1;
y = 1;
x1 = 1;
for k=1:200
x1 = x/200;
a = a/k;
y = y*x1;
s = s + a*y;
end
s = s^200;
s
Which gives:
exp(-100) = 3.720075976020836e-044
s = 3.720075976020701e-044

As John points out in a comment, you have an error inside the loop. The y = y*k line does not do what you need. Look more carefully at the terms in the series for exp(x).
Anyway, I assume this is why you have been given this homework assignment, to learn that series like this don't converge very well for large values. Instead, you should consider how to do range reduction.
For example, can you use the identity
exp(x+y) = exp(x)*exp(y)
to your advantage? Suppose you store the value of exp(1) = 2.7182818284590452353...
Now, if I were to ask you to compute the value of exp(1.3), how would you use the above information?
exp(1.3) = exp(1)*exp(0.3)
But we KNOW the value of exp(1) already. In fact, with a little thought, this will let you reduce the range for an exponential down to needing the series to converge rapidly only for abs(x) <= 0.5.
Edit: There is a second way one can do range reduction using a variation of the same identity.
exp(x) = exp(x/2)*exp(x/2) = exp(x/2)^2
Thus, suppose you wish to compute the exponential of large number, perhaps 12.8. Getting this to converge acceptably fast will take many terms in the simple series, and there will be a great deal of subtractive cancellation happening, so you won't get good accuracy anyway. However, if we recognize that
12.8 = 2*6.4 = 2*2*3.2 = ... = 16*0.8
then IF you could efficiently compute the exponential of 0.8, then the desired value is easy to recover, perhaps by repeated squaring.
exp(12.8)
ans =
362217.449611248
a = exp(0.8)
a =
2.22554092849247
a = a*a;
a = a*a;
a = a*a;
a = a*a
362217.449611249
exp(0.8)^16
ans =
362217.449611249
Note that WHENEVER you do range reduction using methods like this, while you may incur numerical problems due to the additional computations necessary, you will usually come out way ahead due to the greatly enhanced convergence of your series.

Why do you think that's the wrong answer? Look at the last term of that sequence, and it's size, and tell me why you expect you should have an answer that's close to 0.
My original answer stated that roundoff error was the problem. That will be a problem with this basic approach, but why do you think 40 is enough terms for the appropriate mathematical ( as opposed to computer floating point arithmetic) answer.
100^40 / 40! ~= 10^31.
Woodchip has the right idea with range reduction. That's the typical approach people use to implement these kinds of functions very quickly. Once you get that all figured out, you deal with roundoff errors of alternating sequences, by summing adjacent terms within the loop, and stepping with k = 1 : 2 : 40 (for instance). That doesn't work here until you use woodchips's idea because for x = -100, the summands grow for a very long time. You need |x| < 1 to guarantee intermediate terms are shrinking, and thus a rewrite will work.

Related

Speeding up matlab for loop

I have a system of 5 ODEs with nonlinear terms involved. I am trying to vary 3 parameters over some ranges to see what parameters would produce the necessary behaviour that I am looking for.
The issue is I have written the code with 3 for loops and it takes a very long time to get the output.
I am also storing the parameter values within the loops when it meets a parameter set that satisfies an ODE event.
This is how I have implemented it in matlab.
function [m,cVal,x,y]=parameters()
b=5000;
q=0;
r=10^4;
s=0;
n=10^-8;
time=3000;
m=[];
cVal=[];
x=[];
y=[];
val1=0.1:0.01:5;
val2=0.1:0.2:8;
val3=10^-13:10^-14:10^-11;
for i=1:length(val1)
for j=1:length(val2)
for k=1:length(val3)
options = odeset('AbsTol',1e-15,'RelTol',1e-13,'Events',#eventfunction);
[t,y,te,ye]=ode45(#(t,y)systemFunc(t,y,[val1(i),val2(j),val3(k)]),0:time,[b,q,s,r,n],options);
if length(te)==1
m=[m;val1(i)];
cVal=[cVal;val2(j)];
x=[x;val3(k)];
y=[y;ye(1)];
end
end
end
end
Is there any other way that I can use to speed up this process?
Profile viewer results
I have written the system of ODEs simply with the a format like
function s=systemFunc(t,y,p)
s= zeros(2,1);
s(1)=f*y(1)*(1-(y(1)/k))-p(1)*y(2)*y(1)/(p(2)*y(2)+y(1));
s(2)=p(3)*y(1)-d*y(2);
end
f,d,k are constant parameters.
The equations are more complicated than what's here as its a system of 5 ODEs with lots of non linear terms interacting with each other.
Tommaso is right. Preallocating will save some time.
But I would guess that there is fundamentally not a lot you can do since you are running ode45 in a loop. ode45 itself may be the bottleneck.
I would suggest you profile your code to see where the bottleneck is:
profile on
parameters(... )
profile viewer
I would guess that ode45 is the problem. Probably you will find that you should actually focus your time on optimizing the systemFunc code for performance. But you won't know that until you run the profiler.
EDIT
Based on the profiler output and additional code, I see some things that will help
It seems like the vectorization of your values is hurting you. Instead of
#(t,y)systemFunc(t,y,[val1(i),val2(j),val3(k)])
try
#(t,y)systemFunc(t,y,val1(i),val2(j),val3(k))
where your system function is defined as
function s=systemFunc(t,y,p1,p2,p3)
s= zeros(2,1);
s(1)=f*y(1)*(1-(y(1)/k))-p1*y(2)*y(1)/(p2*y(2)+y(1));
s(2)=p3*y(1)-d*y(2);
end
Next, note that you don't have to preallocate space in the systemFunc, just combine them in the output:
function s=systemFunc(t,y,p1,p2,p3)
s = [ f*y(1)*(1-(y(1)/k))-p1*y(2)*y(1)/(p2*y(2)+y(1)),
p3*y(1)-d*y(2) ];
end
Finally, note that ode45 is internally taking about 1/3 of your runtime. There is not much you will be able to do about that. If you can live with it, I would suggest increasing your 'AbsTol' and 'RelTol' to more reasonable numbers. Those values are really small, and are making ode45 run for a really long time. If you can live with it, try increasing them to something like 1e-6 or 1e-8 and see how much the performance increases. Alternatively, depending on how smooth your function is, you might be able to do better with a different integrator (like ode23). But your mileage will vary based on how smooth your problem is.
I have two suggestions for you.
Preallocate the vectors in which you store your results and use an
increasing index to populate them into each iteration.
Since the options you use are always the same, instantiate then
outside the loop only once.
Final code:
function [m,cVal,x,y] = parameters()
b = 5000;
q = 0;
r = 10^4;
s = 0;
n = 10^-8;
time = 3000;
options = odeset('AbsTol',1e-15,'RelTol',1e-13,'Events',#eventfunction);
val1 = 0.1:0.01:5;
val1_len = numel(val1);
val2 = 0.1:0.2:8;
val2_len = numel(val2);
val3 = 10^-13:10^-14:10^-11;
val3_len = numel(val3);
total_len = val1_len * val2_len * val3_len;
m = NaN(total_len,1);
cVal = NaN(total_len,1);
x = NaN(total_len,1);
y = NaN(total_len,1);
res_offset = 1;
for i = 1:val1_len
for j = 1:val2_len
for k = 1:val3_len
[t,y,te,ye] = ode45(#(t,y)systemFunc(t,y,[val1(i),val2(j),val3(k)]),0:time,[b,q,s,r,n],options);
if (length(te) == 1)
m(res_offset) = val1(i);
cVal(res_offset) = val2(j);
x(res_offset) = val3(k);
y(res_offset) = ye(1);
end
res_offset = res_offset + 1;
end
end
end
end
If you only want to preserve result values that have been correctly computed, you can remove the rows containing NaNs at the bottom of your function. Indexing on one of the vectors will be enough to clear everything:
rows_ok = ~isnan(y);
m = m(rows_ok);
cVal = cVal(rows_ok);
x = x(rows_ok);
y = y(rows_ok);
In continuation of the other suggestions, I have 2 more suggestions for you:
You might want to try with a different solver, ODE45 is for non-stiff problems, but from the looks of it, it might seem like your problem could be stiff (parameters have a different order of magnitude). Try for instance with the ode23s method.
Secondly, without knowing which event you are looking for, maybe it is possible for you to use a logarithmic search rather than a linear one. e.g. the Bisection method. This will severely cut down on the number of times you have to solve the equation.

Approximating an integral in MATLAB

I've been trying to implement the following integral in MATLAB
Given a number n, I wrote the code that returns an array with n elements, containing approximations of each integral.
First, I tried this using a 'for' loop and the recurrence relationship on the first line. But from the 20th integral and above the values are completely wrong (correct to 0 significant figures and wrong sign).
The same goes if I use the explicit formula on the second line and two 'for' loops.
As n grows larger, so does the error on the approximations.
So the main issue here is that I haven't found a way to minimize the error as much as possible.
Any ideas? Thanks in advance.
Here is an example of the code and the resulting values, using the second formula:
This integral, for positive values of n, cannot have values >1 or <0
First attempt:
I tried the iterative method and found interesting thing. The approximation may not be true for all n. In fact if I keep track of (n-1)*I(n-1) in each loop I can see
I = zeros(20,3);
I(1,1) = 1-1/exp(1);
for ii = 2:20
I(ii,2) = ii-1;
I(ii,3) = (ii-1)*I(ii-1,1);
I(ii,1) = 1-I(ii,3);
end
There is some problem around n=18. In fact, I18 = 0.05719 and 18*I18 = 1.029 which is larger than 1. I don't think there is any numerical error or number overflow in this procedure.
Second attempt:
To make sure the maths is correct (I verified twice on paper) I used trapz to numerically evaluate the integral, and n=18 didn't cause any problem.
>> x = linspace(0,1,1+1e4);
>> f = #(n) exp(-1)*exp(x).*x.^(n-1);
>> f = #(n) exp(-1)*exp(x).*x.^(n-1)*1e-4;
>> trapz(f(5))
ans =
1.708934160520510e-01
>> trapz(f(17))
ans =
5.571936009790170e-02
>> trapz(f(18))
ans =
5.277113416899408e-02
>>
A closer look is as follows. I18 is slightly different (to the 4th significant digit) between the (stable) numerical method and (unstable) iterative method. 18*I18 is therefore possible to exceed 1.
I = zeros(20,3);
I(1,1) = 1-1/exp(1);
for ii = 2:20
I(ii,2) = ii-1;
I(ii,3) = (ii-1)*I(ii-1,1);
I(ii,1) = 1-I(ii,3);
end
J = zeros(20,3);
x = linspace(0,1,1+1e4);
f = #(n) exp(-1)*exp(x).*x.^(n-1)*1e-4;
J(1,1) = trapz(f(1));
for jj = 2:20
J(jj,1) = trapz(f(jj));
J(jj,2) = jj-1;
J(jj,3) = (jj-1)*J(jj-1,1);
end
I suspect there is an error in each iterative step due to the nature of numerical computations. If the iteration is long, the error propagates and, unfortunately in this case, amplifies rapidly. In order to verify this, I combined the above two methods into a hybrid algo. For most of the time the iterative way is used, and once in a while a numerical integral is evaluated from scratch without relying on previous iterations.
K = zeros(40,4);
K(1,1) = 1-1/exp(1);
for kk = 2:40
K(kk,2) = trapz(f(kk));
K(kk,3) = (kk-1)*K(kk-1,1);
K(kk,4) = 1-K(kk,3);
if mod(kk,5) == 0
K(kk,1) = K(kk,2);
else
K(kk,1) = K(kk,4);
end
end
If the iteration lasts more than 4 steps, error amplification will be large enough to invert the sign, and starts nonrecoverable oscillation.
The code should be able to explain all the data structures. Anyway, let me put some focus here. The second column is the result of trapz, which is the numerical integral done on the non-iterative integration definition of I(n). The third column is (n-1)*I(n-1) and should be always positive and less than 1. The forth column is 1-(n-1)*I(n-1) and should always be positive. The first column is the choice I have made between the trapz result and iterative result, to be the "true" value of I(n).
As can be seen here, in each iteration there is a small error compared to the independent numerical way. The error grows in the 3rd and 4th iteration and finally breaks the thing in its 5th. This is observed around n=25, under the case that I pick the numerical result in every 5 loops since the beginning.
Conclusion: There is nothing wrong with any definition of this integral. However the numerical error when evaluating the expressions is unfortunately aggregating, hence limiting the way you can perform the computation.

Matlab logncdf function is not producing expected result

So on this problem it seems pretty straight forward we are given
mean of x = 10,281 and sigma of x = 4112.4
We are asked to determine P(X<15,000)
Now I thought the code for this in matlab should be super straightforward
mu = 10281
sigma = 4112.4
p = logncdf(15000,10281,4112.4)
However this gives
p = .0063
The given answer is .8790 and just looking at p you can tell it is wrong because we are at 15000 which is over the mean which means it should be above .5. What is the deal with this function?
I saw somewhere you might need to take the exp(15000) for x in the function that results in a probability of 1 which is too high.
Any pointers would be much appreciated
%If X is lognormally distributed with parameters:-
mu = 10281;
sigma = 4112.4;
%then log(X) is normally distributed with following parameters:
mew_actual = log((mu^2)/sqrt(sigma^2+mu^2));
sigma_actual = sqrt(log((sigma^2)/(mu^2) +1));
Now you can use either of the following to compute CDF:-
p = cdf('Normal',log(15000),mew_actual,sigma_actual)
or
p=logncdf(15000,mew_actual,sigma_actual)
which gives 0.8796
(which I believe is the correct answer)
The answer given to you is 0.8790 because if you solve the question by hand, you'll get something like: z = 1.172759 and when you look this value in the table, you can only find z = 1.17(without the rest of decimal places) and for which φ(z)=0.8790.
You can verify the exact answer using this calculator. The related screenshot is attached below:

Extended Kalman Filter - error in update step

I have to implement EKF without actually having good mathematical understanding of it. (Great... not...) So far I have been doing well but since I tried to implement the prediction step things started going wrong.
The agent that uses EKF (red) shoots off in a random direction
Eventually some variables (pose, Sigma, S, K) become NaNs and the simulation fails
I base my code on the code from Thrun's "Probabilistic Robotics" on page. 204. This is the part of the code that seems to be messing things up
% Get variables
[x,y,th] = getPose(mu_bar);
numoffeatures = size(map,2);
for f = 1:numoffeatures
j = c(f);
[fx,fy] = getFeatures(map,f);
q = (fx-x).^2 + (fy-y).^2;
z_hat = [sqrt(q);
atan2(fy-y,fx-x)-th;
j];
H = [(-fx-x)/sqrt(q) (-fy-y)/sqrt(q) 0;
(fy-y)/q (-fx-x)/q -1;
0 0 0];
S = H*Sigma_bar*H'+Q;
K = Sigma_bar*H'/inv(S);
mu_bar = mu_bar+K*(z(:,j)-z_hat);
Sigma_bar = (eye(3)-K*H)*Sigma_bar;
end
I am totally clueless... Any ideas and hints will be appreciated. Thank you.
UPDATE
The reason of the agent shooting off is the 'error' when computing the difference between two angles. Those are computed using atan2. Although I know what the problem is I still can't figure out how to fix it.
Let's imagine that after computing atan2 for two objects I have values resulting in a = 135 and b = 45. I computed the difference between them for both possibilities 90 degrees and 270 degrees but the agent still doesn't behave the way it is supposed to. I've never really encountered atan2 before. Is my understanding of calculating the difference between atan2 values wrong? Here is the illustration of my understanding:
Q is the process noise?
You cannot set the process noise as
Q = randn*eye(3);
because you may have negative covariance, this doesn't make sense.

Calculating a interest rate tree in matlab

I would like to calibrate a interest rate tree using the optimization tool in matlab. Need some guidance on doing it.
The interest rate tree looks like this:
How it works:
3.73% = 2.5%*exp(2*0.2)
96.40453 = (0.5*100 + 0.5*100)/(1+3.73%)
94.15801 = (0.5*96.40453+ 0.5*97.56098)/(1+2.50%)
The value of 2.5% is arbitrary and the upper node is obtained by multiplying with an exponential of 2*volatility(here it is 20%).
I need to optimize the problem by varying different values for the lower node.
How do I do this optimization in Matlab?
What I have tried so far?
InterestTree{1}(1,1) = 0.03;
InterestTree{3-1}(1,3-1)= 2.5/100;
InterestTree{3}(2,:) = 100;
InterestTree{3-1}(1,3-2)= (2.5*exp(2*0.2))/100;
InterestTree{3-1}(2,3-1)=(0.5*InterestTree{3}(2,3)+0.5*InterestTree{3}(2,3-1))/(1+InterestTree{3-1}(1,3-1));
j = 3-2;
InterestTree{3-1}(2,3-2)=(0.5*InterestTree{3}(2,j+1)+0.5*InterestTree{3}(2,j))/(1+InterestTree{3-1}(1,j));
InterestTree{3-2}(2,3-2)=(0.5*InterestTree{3-1}(2,j+1)+0.5*InterestTree{3-1}(2,j))/(1+InterestTree{3-2}(1,j));
But I am not sure how to go about the optimization. Any suggestions to improve the code, do tell me..Need some guidance on this..
Are you expecting the tree to increase in size? Or are you just optimizing over the value of the "2.5%" parameter?
If it's the latter, there are two ways. The first is to model the tree using a closed form expression by replacing 2.5% with x, which is possible with the tree. There are nonlinear optimization toolboxes available in Matlab (e.g. more here), but it's been too long since I've done this to give you a more detailed answer.
The seconds is the approach I would immediately do. I'm interpreting the example you gave, so the equations I'm using may be incorrect - however, the principle of using the for loop is the same.
vol = 0.2;
maxival = 100;
val1 = zeros(1,maxival); %Preallocate
finalval = zeros(1,maxival);
for ival=1:maxival
val1(ival) = i/1000; %Use any scaling you want. This will go from 0.1% to 10%
val2=val1(ival)*exp(2*vol);
x1 = (0.5*100+0.5*100)/(1+val2); %Based on the equation you gave
x2 = (0.5*100+0.5*100)/(1+val1(ival)); %I'm assuming this is how you calculate the bottom node
finalval(ival) = x1*0.5+x2*0.5/(1+...); %The example you gave isn't clear, so replace this with whatever it should be
end
[maxval, indmaxval] = max(finalval);
The maximum value is in maxval, and the interest that maximized this is in val1(indmaxval).