scipy dblquad providing the wrong result in simple double integral










0















I am trying to calculate a straightforward doble definite integral in Python: function Max(0, (4-12x) + (6-12y)) in the square [0,1] x [0,1].



We can do it with Mathematica and get the exact result:



Integrate[Max[0, 4-12*u1 + 6-12*u2], u1, 0, 1, u2, 0,1] = 125/108.


With a simple Monte Carlo simulation I can confirm this result. However, using scipy.integrate.dblquad I am getting a value of 0.0005772072907971, with error 0.0000000000031299



from scipy.integrate import dblquad

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))
sol_int, err = dblquad(integ, 0, 1, lambda _:0, lambda _:1, epsabs=1E-12, epsrel=1E-12)
print("dblquad: %0.16f. Error: %0.16f" % (sol_int, err) )


Agreed that the function is not derivable, but it is continuous, I see no reason for this particular integral to be problematic.



I thought maybe dblquad has an 'options' argument where I can try different numerical methods, but I found nothing like that.



So, what am I doing wrong?










share|improve this question

















  • 1





    When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

    – Warren Weckesser
    Nov 12 '18 at 15:16











  • @Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

    – zeycus
    Nov 12 '18 at 16:00











  • Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

    – Warren Weckesser
    Nov 12 '18 at 16:01











  • Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

    – zeycus
    Nov 12 '18 at 16:36






  • 2





    Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

    – Warren Weckesser
    Nov 12 '18 at 17:30















0















I am trying to calculate a straightforward doble definite integral in Python: function Max(0, (4-12x) + (6-12y)) in the square [0,1] x [0,1].



We can do it with Mathematica and get the exact result:



Integrate[Max[0, 4-12*u1 + 6-12*u2], u1, 0, 1, u2, 0,1] = 125/108.


With a simple Monte Carlo simulation I can confirm this result. However, using scipy.integrate.dblquad I am getting a value of 0.0005772072907971, with error 0.0000000000031299



from scipy.integrate import dblquad

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))
sol_int, err = dblquad(integ, 0, 1, lambda _:0, lambda _:1, epsabs=1E-12, epsrel=1E-12)
print("dblquad: %0.16f. Error: %0.16f" % (sol_int, err) )


Agreed that the function is not derivable, but it is continuous, I see no reason for this particular integral to be problematic.



I thought maybe dblquad has an 'options' argument where I can try different numerical methods, but I found nothing like that.



So, what am I doing wrong?










share|improve this question

















  • 1





    When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

    – Warren Weckesser
    Nov 12 '18 at 15:16











  • @Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

    – zeycus
    Nov 12 '18 at 16:00











  • Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

    – Warren Weckesser
    Nov 12 '18 at 16:01











  • Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

    – zeycus
    Nov 12 '18 at 16:36






  • 2





    Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

    – Warren Weckesser
    Nov 12 '18 at 17:30













0












0








0








I am trying to calculate a straightforward doble definite integral in Python: function Max(0, (4-12x) + (6-12y)) in the square [0,1] x [0,1].



We can do it with Mathematica and get the exact result:



Integrate[Max[0, 4-12*u1 + 6-12*u2], u1, 0, 1, u2, 0,1] = 125/108.


With a simple Monte Carlo simulation I can confirm this result. However, using scipy.integrate.dblquad I am getting a value of 0.0005772072907971, with error 0.0000000000031299



from scipy.integrate import dblquad

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))
sol_int, err = dblquad(integ, 0, 1, lambda _:0, lambda _:1, epsabs=1E-12, epsrel=1E-12)
print("dblquad: %0.16f. Error: %0.16f" % (sol_int, err) )


Agreed that the function is not derivable, but it is continuous, I see no reason for this particular integral to be problematic.



I thought maybe dblquad has an 'options' argument where I can try different numerical methods, but I found nothing like that.



So, what am I doing wrong?










share|improve this question














I am trying to calculate a straightforward doble definite integral in Python: function Max(0, (4-12x) + (6-12y)) in the square [0,1] x [0,1].



We can do it with Mathematica and get the exact result:



Integrate[Max[0, 4-12*u1 + 6-12*u2], u1, 0, 1, u2, 0,1] = 125/108.


With a simple Monte Carlo simulation I can confirm this result. However, using scipy.integrate.dblquad I am getting a value of 0.0005772072907971, with error 0.0000000000031299



from scipy.integrate import dblquad

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))
sol_int, err = dblquad(integ, 0, 1, lambda _:0, lambda _:1, epsabs=1E-12, epsrel=1E-12)
print("dblquad: %0.16f. Error: %0.16f" % (sol_int, err) )


Agreed that the function is not derivable, but it is continuous, I see no reason for this particular integral to be problematic.



I thought maybe dblquad has an 'options' argument where I can try different numerical methods, but I found nothing like that.



So, what am I doing wrong?







scipy integration






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 12 '18 at 14:59









zeycuszeycus

186113




186113







  • 1





    When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

    – Warren Weckesser
    Nov 12 '18 at 15:16











  • @Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

    – zeycus
    Nov 12 '18 at 16:00











  • Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

    – Warren Weckesser
    Nov 12 '18 at 16:01











  • Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

    – zeycus
    Nov 12 '18 at 16:36






  • 2





    Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

    – Warren Weckesser
    Nov 12 '18 at 17:30












  • 1





    When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

    – Warren Weckesser
    Nov 12 '18 at 15:16











  • @Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

    – zeycus
    Nov 12 '18 at 16:00











  • Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

    – Warren Weckesser
    Nov 12 '18 at 16:01











  • Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

    – zeycus
    Nov 12 '18 at 16:36






  • 2





    Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

    – Warren Weckesser
    Nov 12 '18 at 17:30







1




1





When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

– Warren Weckesser
Nov 12 '18 at 15:16





When I run this using scipy 1.1.0 and numpy 1.15.4, the output is dblquad: 1.1574073869306833. Error: 0.0000000000031299

– Warren Weckesser
Nov 12 '18 at 15:16













@Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

– zeycus
Nov 12 '18 at 16:00





@Warren thank you, very interesting, that would be correct. I see I am using numpy 1.14.3, scipy 1.1.0. Python 3.6.5

– zeycus
Nov 12 '18 at 16:00













Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

– Warren Weckesser
Nov 12 '18 at 16:01





Are you sure the code that you show in the question is exactly the same as the code that gave you 0.0005772072907971?

– Warren Weckesser
Nov 12 '18 at 16:01













Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

– zeycus
Nov 12 '18 at 16:36





Yes, I just recopied from this screen to a new file, and executed it. Same 0.000577 result.

– zeycus
Nov 12 '18 at 16:36




2




2





Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

– Warren Weckesser
Nov 12 '18 at 17:30





Are you on Windows? How did you install scipy? You might be hitting this bug: github.com/scipy/scipy/issues/6882; see also stackoverflow.com/questions/27270044/…

– Warren Weckesser
Nov 12 '18 at 17:30












1 Answer
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oldest

votes


















1















try different numerical methods




That's what I would suggest, given the trouble that iterated quad has on Windows. After changing it to an explicit two-step process, you can replace one of quad with another method, romberg seems the best alternative to me.



from scipy.integrate import quad, romberg

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))

sol_int = romberg(lambda u1: quad(lambda u2: integ(u1, u2), 0, 1)[0], 0, 1)
print("romberg-quad: %0.16f " % sol_int)


This prints 1.1574073959987758 on my computer, and hopefully you will get the same.






share|improve this answer























  • This does solve it, thanks! I did not know about the romberg function.

    – zeycus
    Nov 13 '18 at 8:14










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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









1















try different numerical methods




That's what I would suggest, given the trouble that iterated quad has on Windows. After changing it to an explicit two-step process, you can replace one of quad with another method, romberg seems the best alternative to me.



from scipy.integrate import quad, romberg

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))

sol_int = romberg(lambda u1: quad(lambda u2: integ(u1, u2), 0, 1)[0], 0, 1)
print("romberg-quad: %0.16f " % sol_int)


This prints 1.1574073959987758 on my computer, and hopefully you will get the same.






share|improve this answer























  • This does solve it, thanks! I did not know about the romberg function.

    – zeycus
    Nov 13 '18 at 8:14















1















try different numerical methods




That's what I would suggest, given the trouble that iterated quad has on Windows. After changing it to an explicit two-step process, you can replace one of quad with another method, romberg seems the best alternative to me.



from scipy.integrate import quad, romberg

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))

sol_int = romberg(lambda u1: quad(lambda u2: integ(u1, u2), 0, 1)[0], 0, 1)
print("romberg-quad: %0.16f " % sol_int)


This prints 1.1574073959987758 on my computer, and hopefully you will get the same.






share|improve this answer























  • This does solve it, thanks! I did not know about the romberg function.

    – zeycus
    Nov 13 '18 at 8:14













1












1








1








try different numerical methods




That's what I would suggest, given the trouble that iterated quad has on Windows. After changing it to an explicit two-step process, you can replace one of quad with another method, romberg seems the best alternative to me.



from scipy.integrate import quad, romberg

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))

sol_int = romberg(lambda u1: quad(lambda u2: integ(u1, u2), 0, 1)[0], 0, 1)
print("romberg-quad: %0.16f " % sol_int)


This prints 1.1574073959987758 on my computer, and hopefully you will get the same.






share|improve this answer














try different numerical methods




That's what I would suggest, given the trouble that iterated quad has on Windows. After changing it to an explicit two-step process, you can replace one of quad with another method, romberg seems the best alternative to me.



from scipy.integrate import quad, romberg

def integ(u1, u2):
return max(0, (4 - 12*u1) + (6 - 12*u2))

sol_int = romberg(lambda u1: quad(lambda u2: integ(u1, u2), 0, 1)[0], 0, 1)
print("romberg-quad: %0.16f " % sol_int)


This prints 1.1574073959987758 on my computer, and hopefully you will get the same.







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 13 '18 at 4:43







user6655984



















  • This does solve it, thanks! I did not know about the romberg function.

    – zeycus
    Nov 13 '18 at 8:14

















  • This does solve it, thanks! I did not know about the romberg function.

    – zeycus
    Nov 13 '18 at 8:14
















This does solve it, thanks! I did not know about the romberg function.

– zeycus
Nov 13 '18 at 8:14





This does solve it, thanks! I did not know about the romberg function.

– zeycus
Nov 13 '18 at 8:14

















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