[SciPy-User] Inequality constrains definition and violation in slsqp

jagan prabhu jagan_cbe2003@yahoo.co...
Fri Dec 11 10:37:26 CST 2009


My function is a simple least square, it look like,

func = sum((observeddata[ ] - calculated[ ] )**2)  or sum(abs(observed[ ] - calculated[ ] ))

The slsqp optimization code  is coupled to a third part software, beginning from the initial values for each function evaluation my optimization code passes 8 parameters to the software. Based on these 8 parameters software calculates the data and its is stored in calculated[ ] array and observeddata[ ]  is a constant data through out the process, aim is to minimize the error between the observedata[ ] and calculated[ ]  by choosing the proper parameter. Basically i want to do a inverse process identification.

observeddata[ ]  and calculated[ ] will be an array of 200 to 400 entries.

My software is very sensitive to the constraints, if it violates  is stops execution and  the entire process stops.

End of the process i don't know whether it is violated or not, because once in between the process constrains are violated the coupling software stops execution and my optimization process stops. 

So i am forced to search of a better way to define my constraints so that it will not violated during function evaluation.

--- On Fri, 11/12/09, josef.pktd@gmail.com <josef.pktd@gmail.com> wrote:

From: josef.pktd@gmail.com <josef.pktd@gmail.com>
Subject: Re: [SciPy-User] Inequality constrains definition and violation in slsqp
To: "SciPy Users List" <scipy-user@scipy.org>
Date: Friday, 11 December, 2009, 8:41 PM



On Fri, Dec 11, 2009 at 9:39 AM, jagan prabhu <jagan_cbe2003@yahoo.co.in> wrote:



my objective function is a simple least square function, i have 8 parameters and  3 inequality constraints, i used fmin_slsqp optimization algorithm for my problem, but often my constraints are violated while execution. My


code looks like,



######### begin #####################################

Init = [8.0, 14.0, 16.0, 7.0, 13.0, 50.0, 9.0, 4.0]  #

[a,b,c,d,e,f,g,h]

# constraints  (c > d, g > h &  e>=a)



bounds =  [(3.0, 4000.0), (1.0, 6000.0), (2.0, 10000.0), (1.0,

4000.0), (4.0, 6000.0), (1.0, 5000.0), (2.0, 10000.0), (1.0, 4000.0)]

# constraints

con1 = lambda  x:  x[2]-x[3]

con2 = lambda  x:  x[6]-x[7]

con3 = lambda  x:  x[4]-x[0]

cons = [con1, con2, con3]

###

# gradient function for my problem

def grad(s,*args):

 f = func

 step = 1.e-3

 fini = f(*((s,)+args))

 grad = numpy.zeros((len(s),), float)

 st = numpy.zeros((len(s),), float)

 for i in range(len(s)):

   st[i] = step*s[i]

   grad[i] = (f(*((s+st,)+args)) - fini)/st[i]

   st[i] = 0.0

 return grad



opt = fmin_slsqp(func, Init, ieqcons=cons, bounds= bounds, fprime =

grad, iter=50000,iprint=2, acc=0.01)



##############  end of the code #############################



Here the major problem i face is the constraints (c > d, g > h & e>=a) are violated frequently during execution, is there any error with my constrain definition method or any better way to avoid constrain violations?


What's your func?

I don't see anything obviously wrong. 

With "violated frequently during execution", do you mean during optimization but not at the final result?

If yes, your numerical gradient calculation doesn't check whether the parameters for the functions f(*((s+st,)+args)) 

are satisfying the constraints, and your stepsizes are pretty big. I don't know if slsqp itself only evaluates at points that statisfy the constraints.

If that's not the point, then I would need more information to understand what's going on.


Josef


 



       
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