inequality constrains definition and violation in slsqp

[pear]

Subject: inequality constrains definition and violation in slsqp my
objective function is a simple least square function, i have 8
parameters, 3 inequality constraints, i used fmin_slsqp optimization
algorithm for my problem, but often my constraints are violated during
execution. My code looks like,

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 the execution, is there any error with
my constrain definition method or any better way to avoid constrain
violations?

For my problem, constraints should not be violated during execution of
the optimization routine.

Please help me.

[Hans-Bernhard Bröker]

pear wrote:
> Subject: inequality constrains definition and violation in slsqp my
> objective function is a simple least square function, i have 8
> parameters, 3 inequality constraints, i used fmin_slsqp optimization

[...]

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