Many optimization algorithms make use of gradients. In our simple example problem, the SLSQP driver estimates the gradient at various times during the solution procedure by performing a local finite-difference step. Calculating the gradient typically involves one or more executions of the objective function depending on the finite difference method that is used. This, of course, means that your model is executed additional times each iteration.

Sometimes, the solution process can be sped up by having a component supply its own derivatives. These derivatives may be analytical (as you will see in this example), or they might be estimated by some other means. Additionally, these derivatives can be more accurate than those estimated by finite differencing the component, and they are not dependent on the right choice of a step-size parameter.

In OpenMDAO, derivatives can be specified in the component API. A component’s set of specified derivatives is used to replace that component’s output with the first-order Taylor series expansion whenever the optimizer initiates a finite difference estimation of the gradient. This is called Finite Difference with Analytical Derivatives (FFAD). It provides an efficient way of calculating gradients for mixed models – models with components that can provide derivatives and those that cannot. Via FFAD you can specify gradients (first derivatives) and Hessians (second derivatives) in mixed models. Not all optimizers will use gradients or Hessians, so they only get calculated if requested by an optimizer.

Four steps are involved in specifying derivatives for a component:

1. Inherit from ComponentWithDerivatives instead of Component
2. Declare derivatives in the "__init__" method
3. Calculate the first derivatives in the "calculate_first_derivatives" method
4. Calculate the second derivatives in the "calculate_second_derivatives" method (if needed)

You must declare the derivatives that you want to define so that the component can be checked for missing derivatives. In declaration, you aren’t defining a value but just declaring that this derivative is provided by the component. In the general case, you need to have derivatives for all possible permutations between the inputs and outputs of your component. However, during any specific optimization, you only need the derivatives for inputs that are connected to upstream components and outputs that pass info to downstream components. This set can be reduced further when you consider that you only need the inputs and outputs that are active in the loop between the optimizer’s parameters and its objective and constraints. Derivatives are valid only for the Float variable type.

Derivative declaration is guided by the sparse matrix policy: if you don’t declare a derivative, it is assumed to be zero. You don’t have to actively set it to zero, and there is no superfluous multiplication by zero in any part of the calculation. This philosophy leads to a clean interface and efficient calculation, but the burden is on the component developer to make sure not to miss a declaration of a derivative for an important output pair.

You can add analytical derivatives to the Paraboloid component by following the steps mentioned above. First, we must inherit from ComponentWithDerivatives.

from openmdao.main.api import ComponentWithDerivatives
from openmdao.lib.datatypes.api import Float

class ParaboloidDerivative(ComponentWithDerivatives):
""" Evaluates the equation f(x,y) = (x-3)^2 + xy + (y+4)^2 - 3 """

# set up interface to the framework
# pylint: disable-msg=E1101
x = Float(0.0, iotype='in', desc='The variable x')
y = Float(0.0, iotype='in', desc='The variable y')

f_xy = Float(iotype='out', desc='F(x,y)')


The __init__ method is a function that every class calls when it is instantiated. We need to add an __init__ method that defines derivatives between the inputs (x, y) and the output f_xy. Let’s add both first and second derivatives.

def __init__(self):
""" declare what derivatives that we can provide"""

super(Paraboloid_Derivative, self).__init__()

self.derivatives.declare_first_derivative('f_xy', 'x')
self.derivatives.declare_first_derivative('f_xy', 'y')
self.derivatives.declare_second_derivative('f_xy', 'x', 'x')
self.derivatives.declare_second_derivative('f_xy', 'x', 'y')
self.derivatives.declare_second_derivative('f_xy', 'y', 'y')


The super command executes the parent’s __init__ function. This is required for the component to behave properly in OpenMDAO, so don’t forget to include it.

Also, don’t forget the cross-variable terms when declaring second derivatives (in this case, the second derivative of f_xy with respect to x and y.)

Next, we define the calculate_first_derivatives and the calculate_second_derivatives methods.

def calculate_first_derivatives(self):
"""Analytical first derivatives"""

df_dx = 2.0*self.x - 6.0 + self.y
df_dy = 2.0*self.y + 8.0 + self.x

self.derivatives.set_first_derivative('f_xy', 'x', df_dx)
self.derivatives.set_first_derivative('f_xy', 'y', df_dy)

def calculate_second_derivatives(self):
"""Analytical second derivatives"""

df_dxdx = 2.0
df_dxdy = 1.0
df_dydy = 2.0

self.derivatives.set_second_derivative('f_xy', 'x', 'x', df_dxdx)
self.derivatives.set_second_derivative('f_xy', 'x', 'y', df_dxdy)
self.derivatives.set_second_derivative('f_xy', 'y', 'y', df_dydy)


The Hessian matrix is symmetric, so df/dxdy is the same as df/dydx, and only one of these has to be set.

Note that no changes are required to the OptimizationConstrained or OptimizationUnconstrained assembly at this point. If the driver uses gradients (or Hessians) and can take advantage of the analytical ones you provide, then it will do so. Below is our model, using the new component with derivatives. We put this model in a file called optimization_constrained_derivative.py.

"""
optimization_constrained.py - Top level assembly for the problem.
"""

# Perform an constrained optimization on our paraboloid component.

from openmdao.main.api import Assembly
from openmdao.lib.drivers.api import SLSQPdriver

from openmdao.examples.simple.paraboloid_derivative import ParaboloidDerivative

class OptimizationConstrained(Assembly):
"""Constrained optimization of the Paraboloid Component."""

def configure(self):
""" Creates a new Assembly containing a Paraboloid and an optimizer"""

# Create Paraboloid component instances

# Create Optimizer instance

# Driver process definition

# Optimizer Flags
self.driver.iprint = 0

# Objective

# Design Variables

# Constraints

if __name__ == "__main__": # pragma: no cover

import time

opt_problem = OptimizationConstrained()

tt = time.time()
opt_problem.run()

print "\n"
print "Minimum found at (%f, %f)" % (opt_problem.paraboloid.x, \
opt_problem.paraboloid.y)
print "Elapsed time: ", time.time()-tt, "seconds"

# end optimization_constrained.py


## Benchmarking¶

Sometimes it is useful to know how many times your component executes and how many times it calculates its derivatives. OpenMDAO provides this information for every component through a pair of counters: exec_count is incremented whenever a component executes, and derivative_exec_count is incremented whenever the derivatives are calculated. The following example shows how they can be accessed and used.

    >>> # Paraboloid Model
>>>
>>> from openmdao.examples.simple.optimization_constrained import OptimizationConstrained
>>> model = OptimizationConstrained()
>>> model.run()
>>> print model.paraboloid.exec_count
29
>>> print model.paraboloid.derivative_exec_count
0
>>> # Paraboloid Model with analytical derivatives
>>>
>>> from openmdao.examples.simple.optimization_constrained_derivative import OptimizationConstrained
>>> model = OptimizationConstrained()
>>> model.run()
>>> print model.paraboloid.exec_count
17
>>> print model.paraboloid.derivative_exec_count
6

Here, we’ve printed out the number of function and derivative executions for the paraboloid examples, both without and with analytical derivatives. Because this model is a simple equation, the advantage of using the analytical derivative isn’t evident in a comparison of the clock time, but the number of functional executions is much lower when you have them, at a cost of a small number of derivative evaluations.

This concludes an introduction to OpenMDAO using a simple problem of component creation and execution. The next tutorial introduces a problem with more complexity and presents additional features of the framework.