Sensitivity Toolbox¶
The sensitivity toolbox provides a Pyomo interface to sIPOPT to very quickly compute approximate solutions to nonlinear programs with a small perturbation in model parameters. See the sIPOPT documentation or the following paper for additional details:
 Pirnay, R. LopezNegrete, and L.T. Biegler, Optimal Sensitivity based on IPOPT, Math. Prog. Comp., 4(4):307–331, 2012.
Using the Sensitivity Toolbox¶
We will start with a motivating example:
Here \(x_1\), \(x_2\), and \(x_3\) are the decision variables while \(p_1\) and \(p_2\) are parameters. At first, let’s consider \(p_1 = 4.5\) and \(p_2 = 1.0\). Below is the model implemented in Pyomo.
Import Pyomo and sipopt from the sensitivity toolbox
>>> from pyomo.environ import *
>>> from pyomo.contrib.sensitivity_toolbox.sens import sipopt
Create a concrete model
>>> m = ConcreteModel()
Define the variables with bounds and initial values
>>> m.x1 = Var(initialize = 0.15, within=NonNegativeReals)
>>> m.x2 = Var(initialize = 0.15, within=NonNegativeReals)
>>> m.x3 = Var(initialize = 0.0, within=NonNegativeReals)
Define the parameters
>>> m.eta1 = Param(initialize=4.5,mutable=True)
>>> m.eta2 = Param(initialize=1.0,mutable=True)
Define the constraints and objective
>>> m.const1 = Constraint(expr=6*m.x1+3*m.x2+2*m.x3m.eta1 ==0)
>>> m.const2 = Constraint(expr=m.eta2*m.x1+m.x2m.x31 ==0)
>>> m.cost = Objective(expr=m.x1**2+m.x2**2+m.x3**2)
The solution of this optimization problem is \(x_1^* = 0.5\), \(x_2^* = 0.5\), and \(x_3^* = 0.0\). But what if we change the parameter values to \(\hat{p}_1 = 4.0\) and \(\hat{p}_2 = 1.0\)? Is there a quick way to approximate the new solution \(\hat{x}_1^*\), \(\hat{x}_2^*\), and \(\hat{x}_3^*\)? Yes! This is the main functionality of sIPOPT.
Next we define the perturbed parameter values \(\hat{p}_1\) and \(\hat{p}_2\):
>>> m.perturbed_eta1 = Param(initialize = 4.0)
>>> m.perturbed_eta2 = Param(initialize = 1.0)
And finally we call sIPOPT:
>>> m_sipopt = sipopt(m,[m.eta1,m.eta2], [m.perturbed_eta1,m.perturbed_eta2], streamSoln=True)
Ipopt ...: run_sens=yes
...
******************************************************************************
This program contains Ipopt, a library for largescale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coinor.org/Ipopt
******************************************************************************
...
EXIT: Optimal Solution Found.
The first argument is the Pyomo model. The second argument is a list of the original parameters. The third argument is a list of the perturbed parameters. sIPOPT requires these two lists are the same length. The `...`
represents extra lines of output that were cut from this page for brevity.
First, we can inspect the initial point:
>>> print("eta1 = %0.3f" % m.eta1())
eta1 = 4.500
>>> print("eta2 = %0.3f" % m.eta2())
eta2 = 1.000
Initial point (not feasible):
>>> print("Objective = %0.3f" % m.cost())
Objective = 0.045
>>> print("x1 = %0.3f" % m.x1())
x1 = 0.150
>>> print("x2 = %0.3f" % m.x2())
x2 = 0.150
>>> print("x3 = %0.3f" % m.x3())
x3 = 0.000
Next, we inspect the solution \(x_1^*\), \(x_2^*\), and \(x_3^*\):
Solution with the original parameter values:
>>> print("Objective = %0.3f" % m_sipopt.cost())
Objective = 0.500
>>> print("x1 = %0.3f" % m_sipopt.x1())
x1 = 0.500
>>> print("x2 = %0.3f" % m_sipopt.x2())
x2 = 0.500
>>> print("x3 = %0.3f" % m_sipopt.x3())
x3 = 0.000
Finally, we inspect the approximate solution \(\hat{x}_1^*\), \(\hat{x}_2^*\), and \(\hat{x}_3^*\):
New parameter values:
>>> print("eta1 = %0.3f" %m_sipopt.perturbed_eta1())
eta1 = 4.000
>>> print("eta2 = %0.3f" % m_sipopt.perturbed_eta2())
eta2 = 1.000
(Approximate) solution with the new parameter values:
>>> x1 = m_sipopt.sens_sol_state_1[m_sipopt.x1]
>>> x2 = m_sipopt.sens_sol_state_1[m_sipopt.x2]
>>> x3 = m_sipopt.sens_sol_state_1[m_sipopt.x3]
>>> print("Objective = %0.3f" % (x1**2 + x2**2 + x3**2))
Objective = 0.556
>>> print("x1 = %0.3f" % x1)
x1 = 0.333
>>> print("x2 = %0.3f" % x2)
x2 = 0.667
>>> print("x3 = %0.3f" % x3)
x3 = 0.000
Installing sIPOPT¶
The sensitivity toolbox requires sIPOPT is installed and available in your system PATH. See the IPOPT documentation for detailed instructions:
 https://coinor.github.io/Ipopt/INSTALL.html
 https://projects.coinor.org/Ipopt/wiki/sIpopt
 https://coinor.github.io/coinbrew/
Note
If you get an error that ipopt_sens
cannot be found, you need to make sure sIPOPT was installed and that it is in the system path.
Sensitivity Toolbox Interface¶

pyomo.contrib.sensitivity_toolbox.sens.
sipopt
(instance, paramSubList, perturbList, cloneModel=True, streamSoln=False, keepfiles=False)[source]¶ This function accepts a Pyomo ConcreteModel, a list of parameters, along with their corresponding perterbation list. The model is then converted into the design structure required to call sipopt to get an approximation perturbed solution with updated bounds on the decision variable.
Parameters:  instance (ConcreteModel) – pyomo model object
 paramSubList (list) – list of mutable parameters
 perturbList (list) – list of perturbed parameter values
 cloneModel (bool, optional) – indicator to clone the model. If set to False, the original model will be altered
 streamSoln (bool, optional) – indicator to stream IPOPT solution
 keepfiles (bool, optional) – preserve solver interface files
Returns: model – The model modified for use with sipopt. The returned model has three
Suffix
members defined:model.sol_state_1
: the approximated results at the perturbation pointmodel.sol_state_1_z_L
: the updated lower boundmodel.sol_state_1_z_U
: the updated upper bound
Return type: Raises: ValueError
– perturbList argument is expecting a List of ParamsValueError
– length(paramSubList) must equal length(perturbList)ValueError
– paramSubList will not map to perturbList