Sensitivity Toolbox

The sensitivity toolbox provides a Pyomo interface to sIPOPT and k_aug 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:

  1. Pirnay, R. Lopez-Negrete, and L.T. Biegler, Optimal Sensitivity based on IPOPT, Math. Prog. Comp., 4(4):307–331, 2012.

The details of k_aug can be found in the following link:

David Thierry (2020). k_aug, https://github.com/dthierry/k_aug

Using the Sensitivity Toolbox

We will start with a motivating example:

\[\begin{split}\begin{align*} \min_{x_1,x_2,x_3} \quad & x_1^2 + x_2^2 + x_3^2 \\ \mathrm{s.t.} \qquad & 6 x_1 + 3 x_2 + 2 x_3 - p_1 = 0 \\ & p_2 x_1 + x_2 - x_3 - 1 = 0 \\ & x_1, x_2, x_3 \geq 0 \end{align*}\end{split}\]

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 the sensitivity toolbox
>>> from pyomo.environ import *
>>> from pyomo.contrib.sensitivity_toolbox.sens import sensitivity_calculation

# 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.x3-m.eta1 ==0)
>>> m.const2 = Constraint(expr=m.eta2*m.x1+m.x2-m.x3-1 ==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 and k_aug.

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 or k_aug:

>>> m_sipopt = sensitivity_calculation('sipopt', m, [m.eta1, m.eta2], [m.perturbed_eta1, m.perturbed_eta2], tee=False)
>>> m_kaug_dsdp = sensitivity_calculation('k_aug', m, [m.eta1, m.eta2], [m.perturbed_eta1, m.perturbed_eta2], tee=False)

The first argument specifies the method, either ‘sipopt’ or ‘k_aug’. The second argument is the Pyomo model. The third argument is a list of the original parameters. The fourth argument is a list of the perturbed parameters. It’s important that these two lists are the same length and in the same order.

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

Note that k_aug does not save the solution with the original parameter values. Finally, we inspect the approximate solution \(\hat{x}_1^*\), \(\hat{x}_2^*\), and \(\hat{x}_3^*\):

# *sIPOPT*
# 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

# *k_aug*
# New parameter values:
>>> print("eta1 = %0.3f" %m_kaug_dsdp.perturbed_eta1())
eta1 = 4.000

>>> print("eta2 = %0.3f" % m_kaug_dsdp.perturbed_eta2())
eta2 = 1.000

# (Approximate) solution with the new parameter values:
>>> x1 = m_kaug_dsdp.x1()
>>> x2 = m_kaug_dsdp.x2()
>>> x3 = m_kaug_dsdp.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 and k_aug

The sensitivity toolbox requires either sIPOPT or k_aug to be installed and available in your system PATH. See the sIPOPT and k_aug documentation for detailed instructions:

Note

If you get an error that ipopt_sens or k_aug and dot_sens cannot be found, double check your installation and make sure the build directories containing the executables were added to your system PATH.

Sensitivity Toolbox Interface

pyomo.contrib.sensitivity_toolbox.sens.sensitivity_calculation(method, instance, paramList, perturbList, cloneModel=True, tee=False, keepfiles=False, solver_options=None)[source]

This function accepts a Pyomo ConcreteModel, a list of parameters, and their corresponding perturbation list. The model is then augmented with dummy constraints required to call sipopt or k_aug to get an approximation of the perturbed solution.

Parameters:
  • method (string) – ‘sipopt’ or ‘k_aug’
  • instance (Block) – pyomo block or model object
  • paramSubList (list) – list of mutable parameters or fixed variables
  • 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
  • tee (bool, optional) – indicator to stream solver log
  • keepfiles (bool, optional) – preserve solver interface files
  • solver_options (dict, optional) – Provides options to the solver (also the name of an attribute)
Returns:

Return type:

The model that was manipulated by the sensitivity interface