In a Multidisciplinary Design Feasible (MDF) problem, the disciplines are directly coupled via some kind of solver, and the design variables are optimized all at the top level. The following spaghetti diagram illustrates MDF applied to the Sellar problem.

Notice in this diagram, that the optimizer at the top has some data that passes from it to each of the disciplines. In MDF, both the global and local design variables are all controlled by the top level solver. So the data connections you see represent both.

The diagram also shows a solver that takes the output of the component dataflow
and feeds it back into the input. OpenMDAO has two solvers in the standard library: FixedPointIterator
and BroydenSolver. The FixedPointIterator is a solver that performs fixed point iteration,
which means that it keeps driving `x_new = f(x_old)` until convergence is achieved. In
other words, *y2* is passed from the output of `SellarDiscipline2` to the input of `SellarDiscipline1`,
and the loop keeps executing until the change in the value of *y2* between iterations is
smaller than a tolerance. The BroydenSolver is based on a quasi-Newton-Raphson
algorithm that uses a Broyden update to approximate the Jacobian. This solver reads
the output and calculates a new input each iteration. Convergence is achieved when the
residual between the output and input is driven to zero.

An important thing to take note of in the problem setup for MDF is the presence of
nested driver and multiple workflows. Drivers can be nested in OpenMDAO using WorkFlows
in the iteration hierarchy. A *WorkFlow* is an object that determines execution
order for a group of Components. Each driver contains a single WorkFlow. For
each iteration, a Driver will execute one pass through the WorkFlow, executing
the components contained therein in the order the WorkFlow prescribes.
Although in many cases a WorkFlow contains just Components, it can also
contain Drivers, which then have thier own workflows. This allows nested iterative processes to be created. The
following diagram shows an iteration hierarchy for the MDF problem.

Note that this iteration hierarchy does not contain any information about the data connections necessary to complete the MDF implementation. Workflows describe only process.

Now, let’s take the iteration hierarchy we just discussed and put in into an assembly, so we can actually run it.

```
from openmdao.main.api import Assembly, set_as_top
from openmdao.lib.drivers.api import SLSQPdriver, FixedPointIterator
from openmdao.lib.optproblems import sellar
class SellarMDF(Assembly):
""" Optimization of the Sellar problem using MDF
Disciplines coupled with FixedPointIterator.
"""
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# create Optimizer instance
self.add('driver', SLSQPdriver())
# Outer Loop - Global Optimization
self.add('solver', FixedPointIterator())
self.driver.workflow.add(['solver'])
```

So far nothing is really new in terms of syntax. Note that the top level driver, in this case an
instance of SLSQPdriver, is always named *‘driver’*. However, all other drivers can be given any valid name. For this
model, we’ve chosen to use the `FixedPointIterator` for our solver and we named it *‘solver’* in the code.

Next, we need to create the workflow for the solver. Add instances of `SellarDiscipline1`
and `SellarDiscipline2` to the assembly. Then add those instances to the workflow of `'solver'`

```
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
self.solver.workflow.add(['dis1', 'dis2'])
```

Now the iteration hierarchy pictured above is finished. To complete the MDF architecture though, we still need to hook up the data connections and configure the optimization and the fixed point iteration.

Recall that there are two global design variables, `z1` and `z2`. In the model we constructed,
you find `z1` in two places: `dis1.z1` and `dis2.z1`. The same is true for `z2`:
`dis1.z2` and `dis2.z2`. This means that when you add a parameter to the driver for `z1` or `z2`,
it needs to point to both locations in the model. We accomplish that below, by just passing a tuple of
variable names, as the first argument to the `add_parameter` method.

```
# Add Parameters to optimizer
self.driver.add_parameter(('dis1.z1','dis2.z1'), low = -10.0, high = 10.0)
self.driver.add_parameter(('dis1.z2','dis2.z2'), low = 0.0, high = 10.0)
```

There is only one local design variable for this problem, `x1`, which is found in `dis1.x1`. Since local
design variables point to only one place in the model, we just add them using `add_parameter` with a
single name as the first argument (just like we’ve shown you in previous tutorials).

```
self.driver.add_parameter('dis1.x1', low = 0.0, high = 10.0)
```

Since we’re using a fixed point iteration to converge the disciplines, only one of the coupling variables
(`y2`) is directly varied by the solver. The other one (`y1`) is just passed from discipline 1 to
discipline 2 directly each iteration. The choice of which variable to let the solver vary and which to pass
directly is arbitrary. You could have swapped the two, and the problem would still converge.

To tell a FixedPointIterator which variable to vary, we just use `add_parameter` again. During
iteration, this is the variable that is going to be sent to the input of `SellarDiscipline1`, which is
`'dis1y2'`. We specify very small and large values for the low and high arguments because solvers
shouldn’t really be constrained like that. Similarly, we setup the convergence constraint as an equality
constraint. A solver essentially tries to drive something to zero. In this case, we want to drive the
residual error in the coupled variable `y2` to zero. An equality constraint is defined with an expression
string which is parsed for the equals sign. In the above example, you see that `'dis2.y2 = dis1.y2'` is
equivalent to `'dis2.y2 - dis1.y2 = 0'`. We also set the maximum number of iterations and a convergence
tolerance.

```
# Make all connections
self.connect('dis1.y1','dis2.y1')
# Iteration loop
self.solver.add_parameter('dis1.y2', low=-9.e99, high=9.e99)
self.solver.add_constraint('dis2.y2 = dis1.y2')
self.solver.max_iteration = 1000
self.solver.tolerance = .0001
```

Finally, the optimization is set up. We add the objective function as well as the
constraints, from the problem formulation, to the driver. The objective function includes
references to the global design variables. When this happens, you can pick any of the locations
that the global design variable points to. In this case, we used `dis1.z2`, but we could have
just as easily picked `dis2.z2`.

```
# Optimization parameters
self.driver.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
self.driver.add_constraint('3.16 < dis1.y1')
#Or use any of the equivalent forms below
#self.driver.add_constraint('3.16 - dis1.y1 < 0')
#self.driver.add_constraint('3.16 < dis1.y1')
#self.driver.add_constraint('-3.16 > -dis1.y1')
self.driver.add_constraint('dis2.y2 < 24.0')
```

As before, the `add_constraint` method is used to add our constraints. This time however, we used a more
general expression for the first constraint. Alternative examples of the same constraint, composed
slightly differently, are commented out in the example below.

Finally, putting it all together gives:

```
from openmdao.main.api import Assembly, set_as_top
from openmdao.lib.drivers.api import SLSQPdriver, FixedPointIterator
from openmdao.lib.optproblems import sellar
class SellarMDF(Assembly):
""" Optimization of the Sellar problem using MDF
Disciplines coupled with FixedPointIterator.
"""
def configure(self):
""" Creates a new Assembly with this problem
Optimal Design at (1.9776, 0, 0)
Optimal Objective = 3.18339"""
# create Optimizer instance
self.add('driver', SLSQPdriver())
# Outer Loop - Global Optimization
self.add('solver', FixedPointIterator())
self.driver.workflow.add(['solver'])
# Inner Loop - Full Multidisciplinary Solve via fixed point iteration
self.add('dis1', sellar.Discipline1())
self.add('dis2', sellar.Discipline2())
self.solver.workflow.add(['dis1', 'dis2'])
# Add Parameters to optimizer
self.driver.add_parameter(('dis1.z1','dis2.z1'), low = -10.0, high = 10.0)
self.driver.add_parameter(('dis1.z2','dis2.z2'), low = 0.0, high = 10.0)
self.driver.add_parameter('dis1.x1', low = 0.0, high = 10.0)
# Make all connections
self.connect('dis1.y1','dis2.y1')
# Iteration loop
self.solver.add_parameter('dis1.y2', low=-9.e99, high=9.e99)
self.solver.add_constraint('dis2.y2 = dis1.y2')
# equivalent form
# self.solver.add_constraint('dis2.y2 - dis1.y2 = 0')
#Driver settings
self.solver.max_iteration = 1000
self.solver.tolerance = .0001
# Optimization parameters
self.driver.add_objective('(dis1.x1)**2 + dis1.z2 + dis1.y1 + math.exp(-dis2.y2)')
self.driver.add_constraint('3.16 < dis1.y1')
self.driver.add_constraint('dis2.y2 < 24.0')
if __name__ == "__main__": # pragma: no cover
import time
prob = set_as_top(SellarMDF())
prob.name = "top"
prob.dis1.z1 = prob.dis2.z1 = 5.0
prob.dis1.z2 = prob.dis2.z2 = 2.0
prob.dis1.x1 = 1.0
tt = time.time()
prob.run()
print "\n"
print "Minimum found at (%f, %f, %f)" % (prob.dis1.z1, \
prob.dis1.z2, \
prob.dis1.x1)
print "Couping vars: %f, %f" % (prob.dis1.y1, prob.dis2.y2)
print "Minimum objective: ", prob.driver.eval_objective()
print "Elapsed time: ", time.time()-tt, "seconds"
# End sellar_MDF.py
```

This problem is contained in
`sellar_MDF.py`.
We added just a few lines at the end to instantiate the assembly class we defined and then run it and
print out some useful information. Executing it at the command line should produce
output that resembles this:

```
$ python sellar_MDF.py
Minimum found at (1.977657, 0.000000, 0.000000)
Couping vars: 3.160068, 3.755315
Minimum objective: 3.18346116811
Elapsed time: 0.121051073074 seconds
```

We initially chose to use FixedPointIterator for our solver, but you could replace that with a different
one. Fixed point iteration works for some problems, including this one, but sometimes another type of solver
might be preferred. OpenMDAO also contains a Broyden solver called *BroydenSolver*. This solver is based on
a quasi-Newton-Raphson algorithm found in `scipy.nonlinear`. It uses a Broyden update to approximate the
Jacobian. If we replace `FixedPointIterator` with `BroydenSolver`, the optimizer’s workflow looks like
this:

```
# Don't forget to put the import in your header
from openmdao.lib.drivers.api import BroydenSolver
# Outer Loop - Global Optimization
self.add('solver', BroydenSolver())
self.driver.workflow.add('solver')
```

Next, we set up our parameters for the inner loop. The Broyden solver is connected using the same interface as the fixed point iterator, so that code does not change at all. We just change some of solver specific settings.

```
# Iteration loop
self.solver.add_parameter('dis1.y2', low=-9.e99, high=9.e99)
self.solver.add_constraint('dis2.y2 = dis1.y2')
# equivalent form
# self.solver.add_constraint('dis2.y2 - dis1.y2 = 0')
self.solver.itmax = 10
self.solver.alpha = .4
self.solver.tol = .0000001
self.solver.algorithm = "broyden2"
```

The rest of the file does not change at all either. So you can see that it’s pretty easy to reconfigure drivers
using this setup. Here is the new file, with the modifications:
`sellar_MDF_solver.py`.