openmdao v1.4 optimization with metamodel - openmdao

Question

I which to perform an optimization with openmdao 1.4 on a metamodel. Using the tutorials I have build u p problem that i do not mange to solve: I think the problem is coming from a misuse of setup() and run() : I do not manage to train my metamodel and to optimize on it at the same time (perhpas I should use two differentes "groups" to do this ..)
Here is my code :
from __future__ import print_function
from openmdao.api import Component, Group, MetaModel ,IndepVarComp, ExecComp, NLGaussSeidel, KrigingSurrogate, FloatKrigingSurrogate
import numpy as np
class KrigMM(Group):
''' FloatKriging gives responses as floats '''
def __init__(self):
super(KrigMM, self).__init__()
# Create meta_model for f_x as the response
pmm = self.add("pmm", MetaModel())
pmm.add_param('x', val=0.)
pmm.add_output('f_x:float', val=0., surrogate=FloatKrigingSurrogate())
self.add('p1', IndepVarComp('x', 0.0))
self.connect('p1.x','pmm.x')
# mm.add_output('f_xy:norm_dist', val=(0.,0.), surrogate=KrigingSurrogate())
if __name__ == '__main__':
# Setup and run the model.
from openmdao.core.problem import Problem
from openmdao.drivers.scipy_optimizer import ScipyOptimizer
from openmdao.core.driver import Driver
import numpy as np
import doe_lhs
#prob = Problem(root=ParaboloidProblem())
###########################################################
prob = Problem(root=Group())
prob.root.add('meta',KrigMM(), promotes=['*'])
prob.driver = ScipyOptimizer()
prob.driver.options['optimizer'] = 'SLSQP'
prob.driver.add_desvar('p1.x', lower=0, upper=10)
prob.driver.add_objective('pmm.f_x:float')
prob.setup()
prob['pmm.train:x'] = np.linspace(0,10,20)
prob['pmm.train:f_x:float'] = np.sin(prob['pmm.train:x'])
prob.run()
print('\n')
print('Minimum of %f found for meta at %f' % (prob['pmm.f_x:float'],prob['pmm.x'])) #predicted value

Answer 1

I believe your problem is actually working fine. Its just that the sinusiod you've picked has an local optimum at 0.0, which happens to be your initial condition.
If I change the initial condition as follows:
prob.setup()
prob['p1.x'] = 5
prob['pmm.train:x'] = np.linspace(0,10,20)
prob['pmm.train:f_x:float'] = np.sin(prob['pmm.train:x'])
prob.run()
I get:
Optimization terminated successfully. (Exit mode 0)
Current function value: [-1.00004544]
Iterations: 3
Function evaluations: 3
Gradient evaluations: 3
Optimization Complete
-----------------------------------
Minimum of -1.000045 found for meta at 4.710483