Tutorial¶

The following shows how to get started with pyOptSparse by solving Schittkowski’s TP37 constrained problem. First, we show the complete program listing and then go through each statement line by line:

import pyoptsparse
def objfunc(xdict):
x = xdict['xvars']
funcs = {}
funcs['obj'] = -x[0]*x[1]*x[2]
conval = [0]*2
conval[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0
conval[1] = -x[0] - 2.*x[1] - 2.*x[2]
funcs['con'] = conval
fail = False

return funcs, fail

optProb = pyoptsparse.Optimization('TP037', objfunc)
print optProb
opt = pyoptsparse.SLSQP()
sol = opt(optProb, sens='FD')
print sol


Start by importing the pyOptSparse package:

>>> import pyoptsparse


Next we define the objective function that takes in the design variable dictionary and returns a dictionary containing the constraints and objective, as well as a (boolean) flag indicating if the objective function evaluation was successful. For the TP37, the objective function is a simple analytic function:

def objfunc(xdict):
x = xdict['xvars']
funcs = {}
funcs['obj'] = -x[0]*x[1]*x[2]
conval = [0]*2
conval[0] = x[0] + 2.*x[1] + 2.*x[2] - 72.0
conval[1] = -x[0] - 2.*x[1] - 2.*x[2]
funcs['con'] = conval
fail = False

return funcs, fail


Notes:

1. The xdict variable is a dictionary whose keys are the names from each addVar() and addVarGroup() call. The line:

x = xdict['xvars']


retrieves an array of length 3 which are all the variables for this optimization.

2. The line:

conval = [0]*2


creates a list of length 2, which stores the numerical values of the two constraints. The funcs dictionary return must contain keys that match the constraint names from addCon and addConGroup as well as the objectives from addObj calls. This is done in the following calls:

funcs['obj'] = -x[0]*x[1]*x[2]
funcs['con'] = conval


Now the optimization problem can be initialized:

>>> optProb = Optimization('TP037', objfunc)


This creates an instance of the optimization class with a name and a reference to the objective function. To complete the setup of the optimization problem, the design variables and constraints need to be defined.

Design variables and constraints can be added either one-by-one or as a group. Adding variables by group is generally recommended for related variables:

>>> optProb.addVarGroup('xvars', 3, 'c', lower=[0,0,0], upper=[42,42,42], value=10)


This calls adds a group of 3 variables with name ‘xvars’. The variable bounds (side constraints) are 0 for the lower bounds, 42 for the upper bounds. The inital values for each variable is 10.0

Now, we must add the constraints. Like design variables, these may be added individually or by group. It is recommended that related constraints are added by group where possible:

>>> optProb.addConGroup('con',2, lower=None, upper=0.0)


This call adds two variables with name ‘con’. There is no lower bound for the variables and the upper bound is 0.0.

We must also assign the the key value for the objective using the addObj() call:

>>> optProb.addObj('obj')


The optimization problem can be printed to verify that it is setup correctly:

>>> print optProb


To solve an optimization problem with pyOptSparse an optimizer must be initialized. The initialization of one or more optimizers is independent of the initialization of any number of optimization problems. To initialize SLSQP, which is an open-source, sequential least squares programming algorithm that comes as part of the pyOptSparse package, use:

>>> opt = pyoptsparse.SLSQP()


This initializes an instance of SLSQP with the default options. The setOption() method can be used to change any optimizer specific option, for example the internal output flag of SLSQP:

>>> opt.setOption('IPRINT', -1)


Now TP37 can be solved using SLSQP and for example, pyOptSparse’s automatic finite difference for the gradients:

>>> sol = opt(optProb, sensType='FD')


We can print the solution objection to view the result of the optimization:

>>> print sol

TP037
================================================================================

Objective Function: objfunc

Solution:
--------------------------------------------------------------------------------
Total Time:                    0.0256
User Objective Time :       0.0003
User Sensitivity Time :     0.0021
Interface Time :            0.0226
Opt Solver Time:            0.0007
Calls to Objective Function :      23
Calls to Sens Function :            9

Objectives:
Name        Value        Optimum
f               0             0

Variables (c - continuous, i - integer, d - discrete):
Name      Type       Value       Lower Bound  Upper Bound
xvars_0     c            24.000000       0.00e+00     4.20e+01
xvars_1     c            12.000000       0.00e+00     4.20e+01
xvars_2     c            12.000000       0.00e+00     4.20e+01

Constraints (i - inequality, e - equality):
Name    Type                    Bounds
con         i        1.00e-20 <= 0.000000 <= 0.00e+00
con         i        1.00e-20 <= 0.000000 <= 0.00e+00

--------------------------------------------------------------------------------