Created by Prof. Alex Dowling (adowling@nd.edu) at the University of Notre Dame. This notebook (and Degeneracy Hunter) are available through the Institute for the Design of Advanced Energy Systems under a BSD-3 license.
This notebook shows how to use the following Degeneracy Hunter features using two motivating examples:
# This code cell installs packages on Colab
import sys
if "google.colab" in sys.modules:
!wget "https://raw.githubusercontent.com/ndcbe/CBE60499/main/notebooks/helper.py"
import helper
helper.install_idaes()
helper.install_ipopt()
helper.install_cbc()
We start by importing Pyomo and Degeneracy Hunter.
import pyomo.environ as pyo
from idaes.core.util.model_diagnostics import DegeneracyHunter
milp_solver = pyo.SolverFactory('cbc')
Consider the following "well-behaved" nonlinear optimization problem.
This problem is feasible, well-initialized, and standard constraint qualifications hold. As expected, we have no trouble solving this problem.
We start by defining the optimization problem in Pyomo.
m = pyo.ConcreteModel()
m.I = pyo.Set(initialize=[i for i in range(5)])
m.x = pyo.Var(m.I,bounds=(-10,10),initialize=1.0)
m.con1 = pyo.Constraint(expr=m.x[0] + m.x[1] - m.x[3] >= 10)
m.con2 = pyo.Constraint(expr=m.x[0]*m.x[3] + m.x[1] >= 0)
m.con3 = pyo.Constraint(expr=m.x[4]*m.x[3] + m.x[0]*m.x[3] - m.x[4] == 0)
m.obj = pyo.Objective(expr=sum(m.x[i]**2 for i in m.I))
m.pprint()
1 Set Declarations
I : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 5 : {0, 1, 2, 3, 4}
1 Var Declarations
x : Size=5, Index=I
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : -10 : 1.0 : 10 : False : False : Reals
1 : -10 : 1.0 : 10 : False : False : Reals
2 : -10 : 1.0 : 10 : False : False : Reals
3 : -10 : 1.0 : 10 : False : False : Reals
4 : -10 : 1.0 : 10 : False : False : Reals
1 Objective Declarations
obj : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : minimize : x[0]**2 + x[1]**2 + x[2]**2 + x[3]**2 + x[4]**2
3 Constraint Declarations
con1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 10.0 : x[0] + x[1] - x[3] : +Inf : True
con2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x[0]*x[3] + x[1] : +Inf : True
con3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x[4]*x[3] + x[0]*x[3] - x[4] : 0.0 : True
6 Declarations: I x con1 con2 con3 obj
Initialization is extremely important for nonlinear optimization problems. By setting the Ipopt option max_iter to zero, we can inspect the initial point.
# Specify Ipopt as the solver
opt = pyo.SolverFactory('ipopt')
# Specifying an iteration limit of 0 allows us to inspect the initial point
opt.options['max_iter'] = 0
# "Solving" the model with an iteration limit of 0 load the initial point and applies
# any preprocessors (e.g., enforces bounds)
opt.solve(m, tee=True)
# Create Degeneracy Hunter object
dh = DegeneracyHunter(m, solver=milp_solver)
Ipopt 3.13.2: max_iter=0
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 3
Number of nonzeros in inequality constraint Jacobian.: 6
Number of nonzeros in Lagrangian Hessian.............: 7
Total number of variables............................: 5
variables with only lower bounds: 0
variables with lower and upper bounds: 5
variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 2
inequality constraints with only lower bounds: 2
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 5.0000000e+00 9.00e+00 2.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
Number of Iterations....: 0
(scaled) (unscaled)
Objective...............: 5.0000000000000000e+00 5.0000000000000000e+00
Dual infeasibility......: 2.0000000000000000e+00 2.0000000000000000e+00
Constraint violation....: 8.9999999000000006e+00 8.9999999000000006e+00
Complementarity.........: 1.1000000099999999e+01 1.1000000099999999e+01
Overall NLP error.......: 1.1000000099999999e+01 1.1000000099999999e+01
Number of objective function evaluations = 1
Number of objective gradient evaluations = 1
Number of equality constraint evaluations = 1
Number of inequality constraint evaluations = 1
Number of equality constraint Jacobian evaluations = 1
Number of inequality constraint Jacobian evaluations = 1
Number of Lagrangian Hessian evaluations = 0
Total CPU secs in IPOPT (w/o function evaluations) = 0.000
Total CPU secs in NLP function evaluations = 0.000
EXIT: Maximum Number of Iterations Exceeded.
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: maxIterations
- message from solver: Ipopt 3.13.2\x3a Maximum Number of Iterations
Exceeded.
We expect the exit status Maximum Number of Iterations Exceeded because we told Ipopt to take zero iterations (only evaluate the initial point).
When developing nonlinear optimization models, one often wants to know: "what constraints are violated at the initial point (or more generally the point the solver terminated) within a given tolerance?" Degeneracy Hunter makes this very easy by provided a simple interface to several IDAES utility functions.
The following line of code will print out all constraints with residuals larger than 0.1:
dh.check_residuals(tol=0.1)
All constraints with residuals larger than 0.1 :
count = 0 |residual| = 9.0
con1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 10.0 : x[0] + x[1] - x[3] : +Inf : True
variable lower value upper
x[0] -10 1.0 10
x[1] -10 1.0 10
x[3] -10 1.0 10
count = 1 |residual| = 1.0
con3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x[4]*x[3] + x[0]*x[3] - x[4] : 0.0 : True
variable lower value upper
x[4] -10 1.0 10
x[3] -10 1.0 10
x[0] -10 1.0 10
No constraints with residuals larger than 0.1 !
dict_keys([<pyomo.core.base.constraint.SimpleConstraint object at 0x1519cc2e0>, <pyomo.core.base.constraint.SimpleConstraint object at 0x1519cc400>])
Important: Ipopt does several preprocessing steps when we executed it with zero iterations. When checking the initial point, it is strongly recommended to call Ipopt with zero iterations first. Otherwise, you will not be analyzing the initial point Ipopt starts with.
Another common question when developing optimization models is, "Which variables are within their bounds by a given tolerance?" Below is the syntax:
dh.check_variable_bounds(tol=1.0)
No variables within 1.0 (absolute) of their bounds.
<pyomo.common.collections.component_set.ComponentSet at 0x151a26790>
Now we can solve the optimization problem. We first set the number of iterations to 50 and then resolve with Ipopt.
opt.options['max_iter'] = 50
opt.solve(m, tee=True)
Ipopt 3.13.2: max_iter=50
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 3
Number of nonzeros in inequality constraint Jacobian.: 6
Number of nonzeros in Lagrangian Hessian.............: 7
Total number of variables............................: 5
variables with only lower bounds: 0
variables with lower and upper bounds: 5
variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 2
inequality constraints with only lower bounds: 2
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 5.0000000e+00 9.00e+00 2.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.5940309e+00 8.19e+00 2.58e+00 -1.0 4.12e+00 - 3.29e-01 9.99e-02h 1
2 4.3446689e+01 2.13e+00 2.10e+00 -1.0 3.67e+00 - 7.64e-01 1.00e+00h 1
3 5.0854561e+01 6.80e-01 1.02e+00 -1.0 1.02e+00 0.0 9.34e-01 1.00e+00h 1
4 4.9204340e+01 4.73e-01 1.26e+00 -1.0 1.78e+00 - 1.00e+00 1.00e+00f 1
5 4.8955163e+01 9.44e-01 1.57e+00 -1.0 4.62e+00 -0.5 1.00e+00 3.51e-01f 2
6 4.5850988e+01 8.92e-01 1.56e+00 -1.0 2.14e+00 -0.1 1.00e+00 7.67e-01f 1
7 4.5704084e+01 1.19e+00 1.75e+00 -1.0 4.73e+00 - 1.00e+00 2.50e-01f 3
8 4.5331671e+01 1.31e+00 3.01e+00 -1.0 1.56e+01 - 7.20e-01 5.98e-02f 4
9 4.1480725e+01 7.76e-03 1.98e-01 -1.0 4.94e-01 - 1.00e+00 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 4.1177584e+01 1.30e-03 8.56e-03 -2.5 1.48e-01 - 1.00e+00 1.00e+00f 1
11 4.1157407e+01 7.91e-06 1.09e-05 -3.8 9.88e-03 - 1.00e+00 1.00e+00h 1
12 4.1157067e+01 2.59e-10 1.57e-09 -5.7 1.30e-04 - 1.00e+00 1.00e+00h 1
13 4.1157063e+01 2.24e-14 1.63e-13 -8.6 1.52e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 13
(scaled) (unscaled)
Objective...............: 4.1157063321491172e+01 4.1157063321491172e+01
Dual infeasibility......: 1.6278818285040336e-13 1.6278818285040336e-13
Constraint violation....: 2.2426505097428162e-14 2.2426505097428162e-14
Complementarity.........: 2.5065697306197698e-09 2.5065697306197698e-09
Overall NLP error.......: 2.5065697306197698e-09 2.5065697306197698e-09
Number of objective function evaluations = 24
Number of objective gradient evaluations = 14
Number of equality constraint evaluations = 24
Number of inequality constraint evaluations = 24
Number of equality constraint Jacobian evaluations = 14
Number of inequality constraint Jacobian evaluations = 14
Number of Lagrangian Hessian evaluations = 13
Total CPU secs in IPOPT (w/o function evaluations) = 0.004
Total CPU secs in NLP function evaluations = 0.000
EXIT: Optimal Solution Found.
{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 3, 'Number of variables': 5, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.04221987724304199}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}
As expected, Ipopt has no trouble solving this optimization problem.
Let's now inspect the new solution to see which (if any) constraints have residuals larger than 10$^{-14}$.
dh.check_residuals(tol=1E-14)
All constraints with residuals larger than 1e-14 :
count = 0 |residual| = 9.97185747309004e-08
con1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 10.0 : x[0] + x[1] - x[3] : +Inf : True
variable lower value upper
x[0] -10 1.4516741106008322 10
x[1] -10 5.061595738300216 10
x[3] -10 -3.4867300513803765 10
count = 1 |residual| = 7.942586144338293e-09
con2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x[0]*x[3] + x[1] : +Inf : True
variable lower value upper
x[1] -10 5.061595738300216 10
x[0] -10 1.4516741106008322 10
x[3] -10 -3.4867300513803765 10
count = 2 |residual| = 2.2426505097428162e-14
con3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 0.0 : x[4]*x[3] + x[0]*x[3] - x[4] : 0.0 : True
variable lower value upper
x[4] -10 -1.128125759356881 10
x[3] -10 -3.4867300513803765 10
x[0] -10 1.4516741106008322 10
No constraints with residuals larger than 1e-14 !
dict_keys([<pyomo.core.base.constraint.SimpleConstraint object at 0x1519cc2e0>, <pyomo.core.base.constraint.SimpleConstraint object at 0x1519cc340>, <pyomo.core.base.constraint.SimpleConstraint object at 0x1519cc400>])
As expected, all of the constraints are satisfied, even with this fairly tight tolerance.
Finally, let's check if any of the variables are near their bounds at the new solution.
dh.check_variable_bounds(tol=1E-5)
No variables within 1e-05 (absolute) of their bounds.
<pyomo.common.collections.component_set.ComponentSet at 0x151aeaa30>
Great, no variables are near their bounds. If a variable was at its bound, it is important the inspect the model and confirm the bound is physically sensible/what you intended.
The main feature of Degeneracy Hunter is to check if an optimization problem is poorly formulated. Let's see what happens when we check the rank on a carefully formulated optimization problem:
dh.check_rank_equality_constraints()
Checking rank of Jacobian of equality constraints... Model contains 1 equality constraints and 5 variables. Model needs at least 2 equality constraints to check rank.
-1
ids = dh.find_irreducible_degenerate_sets(verbose=True)
*** Searching for a Single Degenerate Set ***
Building MILP model...
2 Set Declarations
C : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 1 : {0,}
V : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 5 : {0, 1, 2, 3, 4}
4 Var Declarations
abs_nu : Size=1, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 100000.00001 : False : True : Reals
nu : Size=1, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : -100000.00001 : 1.0 : 100000.00001 : False : False : Reals
y_neg : Size=1, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 1 : False : True : Binary
y_pos : Size=1, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 1 : False : True : Binary
1 Constraint Declarations
pos_xor_neg : Size=0, Index=C, Active=True
Key : Lower : Body : Upper : Active
7 Declarations: C V nu y_pos y_neg abs_nu pos_xor_neg
Solving MILP model...
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: infeasible
- message from solver: <undefined>
No candidate equations. The Jacobian is likely full rank.
Now let's apply Degeneracy Hunter to a poorly formulated optimization problem:
$$\begin{align*} \min_{\mathbf{x}} \quad & \sum_{i=\{1,...,3\}} x_i \\ \mathrm{s.t.}~~& x_1 + x_2 \geq 1 \\ & x_1 + x_2 + x_3 = 1 \\ & x_2 - 2 x_3 \leq 1 \\ & x_1 + x_3 \geq 1 \\ & x_1 + x_2 + x_3 = 1 \\ \end{align*} $$Notice the two equality constraints are redundant. This means the constraint qualifications (e.g., LICQ) do not hold which has three important implications:
The absolute best defense against this is to detect degenerate equations and reformulate the model to remove them; this is the primary purpose of Degeneracy Hunter. Let's see it in action.
def example2(with_degenerate_constraint=True):
''' Create the Pyomo model for Example 2
Arguments:
with_degenerate_constraint: Boolean, if True, include the redundant linear constraint
Returns:
m2: Pyomo model
'''
m2 = pyo.ConcreteModel()
m2.I = pyo.Set(initialize=[i for i in range(1,4)])
m2.x = pyo.Var(m2.I,bounds=(0,5),initialize=1.0)
m2.con1 = pyo.Constraint(expr=m2.x[1] + m2.x[2] >= 1)
m2.con2 = pyo.Constraint(expr=m2.x[1] + m2.x[2] + m2.x[3] == 1)
m2.con3 = pyo.Constraint(expr=m2.x[2] - 2*m2.x[3] <= 1)
m2.con4 = pyo.Constraint(expr=m2.x[1] + m2.x[3] >= 1)
if with_degenerate_constraint:
m2.con5 = pyo.Constraint(expr=m2.x[1] + m2.x[2] + m2.x[3] == 1)
m2.obj = pyo.Objective(expr=sum(m2.x[i] for i in m2.I))
m2.pprint()
return m2
# Create the Pyomo model for Example 2 including the redundant constraint
m2 = example2()
1 Set Declarations
I : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 3 : {1, 2, 3}
1 Var Declarations
x : Size=3, Index=I
Key : Lower : Value : Upper : Fixed : Stale : Domain
1 : 0 : 1.0 : 5 : False : False : Reals
2 : 0 : 1.0 : 5 : False : False : Reals
3 : 0 : 1.0 : 5 : False : False : Reals
1 Objective Declarations
obj : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : minimize : x[1] + x[2] + x[3]
5 Constraint Declarations
con1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] : +Inf : True
con2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] + x[3] : 1.0 : True
con3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : -Inf : x[2] - 2*x[3] : 1.0 : True
con4 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[3] : +Inf : True
con5 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] + x[3] : 1.0 : True
8 Declarations: I x con1 con2 con3 con4 con5 obj
# Specifying an iteration limit of 0 allows us to inspect the initial point
opt.options['max_iter'] = 0
# "Solving" the model with an iteration limit of 0 load the initial point and applies
# any preprocessors (e.g., enforces bounds)
opt.solve(m2, tee=True)
# Create Degeneracy Hunter object
dh2 = DegeneracyHunter(m2, solver=milp_solver)
Ipopt 3.13.2: max_iter=0
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6
Number of nonzeros in inequality constraint Jacobian.: 6
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 3
variables with only lower bounds: 0
variables with lower and upper bounds: 3
variables with only upper bounds: 0
Total number of equality constraints.................: 2
Total number of inequality constraints...............: 3
inequality constraints with only lower bounds: 2
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+00 2.00e+00 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
Number of Iterations....: 0
(scaled) (unscaled)
Objective...............: 3.0000000000000000e+00 3.0000000000000000e+00
Dual infeasibility......: 1.0000000000000000e+00 1.0000000000000000e+00
Constraint violation....: 2.0000000000000000e+00 2.0000000000000000e+00
Complementarity.........: 4.0000000499999997e+00 4.0000000499999997e+00
Overall NLP error.......: 4.0000000499999997e+00 4.0000000499999997e+00
Number of objective function evaluations = 1
Number of objective gradient evaluations = 1
Number of equality constraint evaluations = 1
Number of inequality constraint evaluations = 1
Number of equality constraint Jacobian evaluations = 1
Number of inequality constraint Jacobian evaluations = 1
Number of Lagrangian Hessian evaluations = 0
Total CPU secs in IPOPT (w/o function evaluations) = 0.000
Total CPU secs in NLP function evaluations = 0.000
EXIT: Maximum Number of Iterations Exceeded.
WARNING: Loading a SolverResults object with a warning status into
model.name="unknown";
- termination condition: maxIterations
- message from solver: Ipopt 3.13.2\x3a Maximum Number of Iterations
Exceeded.
dh2.check_residuals(tol=0.1)
All constraints with residuals larger than 0.1 :
count = 0 |residual| = 2.0
con2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] + x[3] : 1.0 : True
variable lower value upper
x[1] 0 1.0 5
x[2] 0 1.0 5
x[3] 0 1.0 5
count = 1 |residual| = 2.0
con5 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] + x[3] : 1.0 : True
variable lower value upper
x[1] 0 1.0 5
x[2] 0 1.0 5
x[3] 0 1.0 5
No constraints with residuals larger than 0.1 !
dict_keys([<pyomo.core.base.constraint.SimpleConstraint object at 0x151add700>, <pyomo.core.base.constraint.SimpleConstraint object at 0x1519c4c40>])
Now let's solve the optimization problem.
opt.options['max_iter'] = 50
opt.solve(m2, tee=True)
for i in m2.I:
print("x[",i,"]=",m2.x[i]())
Ipopt 3.13.2: max_iter=50
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 6
Number of nonzeros in inequality constraint Jacobian.: 6
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 3
variables with only lower bounds: 0
variables with lower and upper bounds: 3
variables with only upper bounds: 0
Total number of equality constraints.................: 2
Total number of inequality constraints...............: 3
inequality constraints with only lower bounds: 2
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+00 2.00e+00 1.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3934810e+00 3.93e-01 2.17e-01 -1.0 1.23e+00 - 7.90e-01 8.03e-01h 1
2 1.0184419e+00 1.84e-02 1.26e-02 -1.7 7.02e-01 - 9.41e-01 9.53e-01h 1
3 1.0011246e+00 1.12e-03 3.82e-02 -2.5 1.50e-02 - 1.00e+00 9.39e-01h 1
4 1.0006914e+00 6.91e-04 3.12e+00 -2.5 3.17e-03 - 1.00e+00 3.85e-01h 1
5 1.0002664e+00 2.66e-04 4.35e+00 -2.5 1.12e-03 - 1.00e+00 6.15e-01h 1
6 1.0001115e+00 1.12e-04 1.07e+01 -2.5 4.99e-04 - 1.00e+00 5.82e-01h 1
7 1.0000788e+00 7.88e-05 4.33e+01 -2.5 1.88e-04 - 1.00e+00 2.94e-01f 2
8 1.0000153e+00 1.53e-05 2.19e+01 -2.5 6.85e-05 - 1.00e+00 8.08e-01h 1
9 1.0000118e+00 1.18e-05 2.78e+02 -2.5 3.47e-05 - 1.00e+00 2.45e-01f 2
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.0000014e+00 1.44e-06 2.83e-08 -2.5 7.79e-06 - 1.00e+00 1.00e+00h 1
11 1.0000000e+00 1.39e-09 1.84e-11 -5.7 2.56e-06 - 1.00e+00 1.00e+00h 1
12 1.0000000e+00 1.39e-09 1.24e+02 -8.6 1.16e-08 - 1.00e+00 9.54e-07h 21
13 9.9999999e-01 9.82e-09 1.14e-13 -8.6 1.33e-08 - 1.00e+00 1.00e+00s 22
14 9.9999999e-01 9.96e-09 6.46e-14 -8.6 2.29e-10 - 1.00e+00 1.00e+00s 22
15 9.9999999e-01 9.96e-09 1.82e+02 -9.0 4.00e-11 - 1.00e+00 9.54e-07h 21
16 9.9999999e-01 1.00e-08 1.52e-13 -9.0 5.89e-11 - 1.00e+00 1.00e+00s 22
Number of Iterations....: 16
(scaled) (unscaled)
Objective...............: 9.9999999000238915e-01 9.9999999000238915e-01
Dual infeasibility......: 1.5188693260016487e-13 1.5188693260016487e-13
Constraint violation....: 9.9976108502985994e-09 9.9976108502985994e-09
Complementarity.........: 9.2217032157601989e-10 9.2217032157601989e-10
Overall NLP error.......: 9.9976108502985994e-09 9.9976108502985994e-09
Number of objective function evaluations = 111
Number of objective gradient evaluations = 17
Number of equality constraint evaluations = 111
Number of inequality constraint evaluations = 111
Number of equality constraint Jacobian evaluations = 17
Number of inequality constraint Jacobian evaluations = 17
Number of Lagrangian Hessian evaluations = 16
Total CPU secs in IPOPT (w/o function evaluations) = 0.007
Total CPU secs in NLP function evaluations = 0.000
EXIT: Optimal Solution Found.
x[ 1 ]= 1.0000000099975996
x[ 2 ]= 0.0
x[ 3 ]= 0.0
We got luck here. Ipopt implements several algorithmic and numerical safeguards to handle (mildy) degenerate equations. Nevertheless, notice the last column of the Ipopt output labeled ls. This is the number of linesearch evaluations. For iterations 0 to 11, ls is 1, which means Ipopt is taking full steps. For iterations 12 to 16, however, ls is greater than 20. This means Ipopt is struggling (a little) to converge to the solution.
n_deficient = dh2.check_rank_equality_constraints()
Checking rank of Jacobian of equality constraints... Model contains 2 equality constraints and 3 variables. Computing the 1 smallest singular value(s) Smallest singular value(s): 0.000E+00
A singular value near 0 indicates the Jacobian of the equality constraints is rank deficient. For each near-zero signular value, there is likely one degenerate constraint.
ds2 = dh2.find_candidate_equations(verbose=True,tee=True)
*** Searching for a Single Degenerate Set ***
Building MILP model...
2 Set Declarations
C : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 2 : {0, 1}
V : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 3 : {0, 1, 2}
4 Var Declarations
abs_nu : Size=2, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 100000.00001 : False : True : Reals
1 : 0 : None : 100000.00001 : False : True : Reals
nu : Size=2, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : -100000.00001 : 1.0 : 100000.00001 : False : False : Reals
1 : -100000.00001 : 1.0 : 100000.00001 : False : False : Reals
y_neg : Size=2, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 1 : False : True : Binary
1 : 0 : None : 1 : False : True : Binary
y_pos : Size=2, Index=C
Key : Lower : Value : Upper : Fixed : Stale : Domain
0 : 0 : None : 1 : False : True : Binary
1 : 0 : None : 1 : False : True : Binary
1 Constraint Declarations
pos_xor_neg : Size=0, Index=C, Active=True
Key : Lower : Body : Upper : Active
7 Declarations: C V nu y_pos y_neg abs_nu pos_xor_neg
Solving MILP model...
Welcome to the CBC MILP Solver
Version: 2.10.5
Build Date: Dec 8 2020
command line - /anaconda3/envs/spring2021/bin/cbc -printingOptions all -import /var/folders/xy/24xvnyss36v3d8mw68tygxdw0000gp/T/tmp69c_1cob.pyomo.lp -stat=1 -solve -solu /var/folders/xy/24xvnyss36v3d8mw68tygxdw0000gp/T/tmp69c_1cob.pyomo.soln (default strategy 1)
Option for printingOptions changed from normal to all
Presolve 9 (-8) rows, 7 (-2) columns and 20 (-15) elements
Statistics for presolved model
Original problem has 4 integers (4 of which binary)
Presolved problem has 4 integers (4 of which binary)
==== 5 zero objective 2 different
5 variables have objective of 0
2 variables have objective of 1
==== absolute objective values 2 different
5 variables have objective of 0
2 variables have objective of 1
==== for integers 4 zero objective 1 different
4 variables have objective of 0
==== for integers absolute objective values 1 different
4 variables have objective of 0
===== end objective counts
Problem has 9 rows, 7 columns (2 with objective) and 20 elements
Column breakdown:
0 of type 0.0->inf, 2 of type 0.0->up, 0 of type lo->inf,
1 of type lo->up, 0 of type free, 0 of type fixed,
0 of type -inf->0.0, 0 of type -inf->up, 4 of type 0.0->1.0
Row breakdown:
0 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0,
0 of type E other, 0 of type G 0.0, 1 of type G 1.0,
0 of type G other, 4 of type L 0.0, 0 of type L 1.0,
4 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other,
0 of type Free
Continuous objective value is 0 - 0.00 seconds
Cgl0003I 0 fixed, 0 tightened bounds, 2 strengthened rows, 0 substitutions
Cgl0004I processed model has 9 rows, 7 columns (4 integer (4 of which binary)) and 22 elements
Cbc0038I Initial state - 2 integers unsatisfied sum - 1
Cbc0038I Pass 1: suminf. 0.00000 (0) obj. 1.99999e-05 iterations 3
Cbc0038I Solution found of 1.99999e-05
Cbc0038I Relaxing continuous gives 2e-05
Cbc0038I Rounding solution of 1.99999e-05 is better than previous of 2e-05
Cbc0038I Before mini branch and bound, 2 integers at bound fixed and 0 continuous
Cbc0038I Full problem 9 rows 7 columns, reduced to 7 rows 5 columns
Cbc0038I Mini branch and bound did not improve solution (0.00 seconds)
Cbc0038I Round again with cutoff of 8.99991e-06
Cbc0038I Pass 2: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 2
Cbc0038I Pass 3: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 4: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 1
Cbc0038I Pass 5: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 6: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 7: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 8: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 9: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 10: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 11: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 12: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 1
Cbc0038I Pass 13: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 14: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 1
Cbc0038I Pass 15: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 16: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 17: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 18: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 5
Cbc0038I Pass 19: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 1
Cbc0038I Pass 20: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 21: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 22: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 23: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 24: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 25: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 26: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 2
Cbc0038I Pass 27: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 2
Cbc0038I Pass 28: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 0
Cbc0038I Pass 29: suminf. 1.00000 (3) obj. 8.99991e-06 iterations 3
Cbc0038I Pass 30: suminf. 0.55000 (2) obj. 8.99991e-06 iterations 2
Cbc0038I Pass 31: suminf. 1.00000 (3) obj. 8.99991e-06 iterations 2
Cbc0038I No solution found this major pass
Cbc0038I Before mini branch and bound, 0 integers at bound fixed and 0 continuous
Cbc0038I Full problem 9 rows 7 columns, reduced to 9 rows 7 columns
Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)
Cbc0038I After 0.01 seconds - Feasibility pump exiting with objective of 1.99999e-05 - took 0.01 seconds
Cbc0012I Integer solution of 2e-05 found by feasibility pump after 0 iterations and 0 nodes (0.01 seconds)
Cbc0038I Full problem 9 rows 7 columns, reduced to 7 rows 5 columns
Cbc0006I The LP relaxation is infeasible or too expensive
Cbc0031I 2 added rows had average density of 2
Cbc0013I At root node, 2 cuts changed objective from 0 to 1e-05 in 3 passes
Cbc0014I Cut generator 0 (Probing) - 2 row cuts average 1.0 elements, 2 column cuts (2 active) in 0.000 seconds - new frequency is 1
Cbc0014I Cut generator 1 (Gomory) - 2 row cuts average 2.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1
Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100
Cbc0014I Cut generator 6 (TwoMirCuts) - 2 row cuts average 2.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1
Cbc0001I Search completed - best objective 2.000000000000001e-05, took 6 iterations and 0 nodes (0.01 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
Cuts at root node changed objective from 0 to 1e-05
Probing was tried 3 times and created 4 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Gomory was tried 2 times and created 2 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Knapsack was tried 2 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Clique was tried 2 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
MixedIntegerRounding2 was tried 2 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
FlowCover was tried 2 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
TwoMirCuts was tried 2 times and created 2 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)
Result - Optimal solution found
Objective value: 0.00002000
Enumerated nodes: 0
Total iterations: 6
Time (CPU seconds): 0.02
Time (Wallclock seconds): 0.02
Total time (CPU seconds): 0.02 (Wallclock seconds): 0.02
ids = dh2.find_irreducible_degenerate_sets(verbose=True)
*** Searching for Irreducible Degenerate Sets *** Building MILP model... Solving MILP 1 of 2 ... Solving MILP 2 of 2 ... Irreducible Degenerate Set 0 nu Constraint Name 1.0 con2 -1.0 con5 Irreducible Degenerate Set 1 nu Constraint Name -1.0 con2 1.0 con5
Now let's reformulate the model by skipping/removing the redundant equality constraint:
$$\begin{align*} \min_{\mathbf{x}} \quad & \sum_{i=\{1,...,3\}} x_i \\ \mathrm{s.t.}~~& x_1 + x_2 \geq 1 \\ & x_1 + x_2 + x_3 = 1 \\ & x_2 - 2 x_3 \leq 1 \\ & x_1 + x_3 \geq 1 \end{align*} $$m2b = example2(with_degenerate_constraint=False)
1 Set Declarations
I : Size=1, Index=None, Ordered=Insertion
Key : Dimen : Domain : Size : Members
None : 1 : Any : 3 : {1, 2, 3}
1 Var Declarations
x : Size=3, Index=I
Key : Lower : Value : Upper : Fixed : Stale : Domain
1 : 0 : 1.0 : 5 : False : False : Reals
2 : 0 : 1.0 : 5 : False : False : Reals
3 : 0 : 1.0 : 5 : False : False : Reals
1 Objective Declarations
obj : Size=1, Index=None, Active=True
Key : Active : Sense : Expression
None : True : minimize : x[1] + x[2] + x[3]
4 Constraint Declarations
con1 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] : +Inf : True
con2 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[2] + x[3] : 1.0 : True
con3 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : -Inf : x[2] - 2*x[3] : 1.0 : True
con4 : Size=1, Index=None, Active=True
Key : Lower : Body : Upper : Active
None : 1.0 : x[1] + x[3] : +Inf : True
7 Declarations: I x con1 con2 con3 con4 obj
opt.options['max_iter'] = 50
opt.solve(m2b, tee=True)
for i in m2b.I:
print("x[",i,"]=",m.x[i]())
Ipopt 3.13.2: max_iter=50
******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
Ipopt is released as open source code under the Eclipse Public License (EPL).
For more information visit http://projects.coin-or.org/Ipopt
This version of Ipopt was compiled from source code available at
https://github.com/IDAES/Ipopt as part of the Institute for the Design of
Advanced Energy Systems Process Systems Engineering Framework (IDAES PSE
Framework) Copyright (c) 2018-2019. See https://github.com/IDAES/idaes-pse.
This version of Ipopt was compiled using HSL, a collection of Fortran codes
for large-scale scientific computation. All technical papers, sales and
publicity material resulting from use of the HSL codes within IPOPT must
contain the following acknowledgement:
HSL, a collection of Fortran codes for large-scale scientific
computation. See http://www.hsl.rl.ac.uk.
******************************************************************************
This is Ipopt version 3.13.2, running with linear solver ma27.
Number of nonzeros in equality constraint Jacobian...: 3
Number of nonzeros in inequality constraint Jacobian.: 6
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 3
variables with only lower bounds: 0
variables with lower and upper bounds: 3
variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 3
inequality constraints with only lower bounds: 2
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+00 2.00e+00 6.30e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3934810e+00 3.93e-01 1.34e-01 -1.0 1.23e+00 - 7.90e-01 8.03e-01f 1
2 1.0184419e+00 1.84e-02 9.15e-03 -1.7 7.02e-01 - 9.41e-01 9.53e-01h 1
3 1.0011246e+00 1.12e-03 3.88e-02 -2.5 1.50e-02 - 1.00e+00 9.39e-01h 1
4 1.0006914e+00 6.91e-04 3.12e+00 -2.5 3.17e-03 - 1.00e+00 3.85e-01h 1
5 1.0002664e+00 2.66e-04 4.35e+00 -2.5 1.12e-03 - 1.00e+00 6.15e-01h 1
6 1.0001115e+00 1.12e-04 1.07e+01 -2.5 5.00e-04 - 1.00e+00 5.81e-01h 1
7 1.0000788e+00 7.88e-05 4.34e+01 -2.5 1.88e-04 - 1.00e+00 2.93e-01f 2
8 1.0000154e+00 1.54e-05 2.26e+01 -2.5 6.89e-05 - 1.00e+00 8.04e-01h 1
9 1.0000118e+00 1.18e-05 2.98e+02 -2.5 3.64e-05 - 1.00e+00 2.33e-01f 2
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.0000020e+00 2.04e-06 1.33e+02 -2.5 9.72e-06 - 1.00e+00 8.28e-01h 1
11 1.0000016e+00 1.61e-06 2.26e+03 -2.5 4.79e-06 - 1.00e+00 2.12e-01f 2
12 1.0000002e+00 2.46e-07 8.72e+02 -2.5 1.20e-06 - 1.00e+00 8.47e-01h 1
13 1.0000002e+00 1.95e-07 1.71e+04 -2.5 7.02e-07 - 1.00e+00 2.09e-01f 2
14 1.0000000e+00 1.54e-08 3.23e+03 -2.5 1.50e-07 - 1.00e+00 9.21e-01h 1
15 1.0000000e+00 1.15e-08 9.99e+04 -2.5 6.89e-08 - 1.00e+00 2.54e-01f 2
16 1.0000000e+00 2.22e-16 2.83e-08 -2.5 8.21e-09 - 1.00e+00 1.00e+00h 1
17 1.0000000e+00 2.22e-16 4.14e-11 -8.6 8.25e-16 - 1.00e+00 1.00e+00 0
Number of Iterations....: 17
(scaled) (unscaled)
Objective...............: 9.9999999999999978e-01 9.9999999999999978e-01
Dual infeasibility......: 4.1425156707686206e-11 4.1425156707686206e-11
Constraint violation....: 2.2204460492503131e-16 2.2204460492503131e-16
Complementarity.........: 2.6658012082875325e-09 2.6658012082875325e-09
Overall NLP error.......: 2.6658012082875325e-09 2.6658012082875325e-09
Number of objective function evaluations = 23
Number of objective gradient evaluations = 18
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 18
Number of inequality constraint Jacobian evaluations = 18
Number of Lagrangian Hessian evaluations = 17
Total CPU secs in IPOPT (w/o function evaluations) = 0.005
Total CPU secs in NLP function evaluations = 0.000
EXIT: Optimal Solution Found.
x[ 1 ]= 5.061595738300216
x[ 2 ]= 9.184026563775058e-26
x[ 3 ]= -3.4867300513803765
We get the same answer as before, but careful inspection of the Ipopt output reveals a subtle improvement. Notice ls is only 1 or 2 for all of the iterations, in contrast to more than 20 for the original model. This means Ipopt is taking (nearly) full steps for all iterations.
Let's also compare the number of function evaluations.
Original model (using Ipopt 3.13.2 with ma27):
Number of objective function evaluations = 111
Number of objective gradient evaluations = 17
Number of equality constraint evaluations = 111
Number of inequality constraint evaluations = 111
Number of equality constraint Jacobian evaluations = 17
Number of inequality constraint Jacobian evaluations = 17
Number of Lagrangian Hessian evaluations = 16Reformulated model (using Ipopt 3.13.2 with ma27):
Number of objective function evaluations = 23
Number of objective gradient evaluations = 18
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 18
Number of inequality constraint Jacobian evaluations = 18
Number of Lagrangian Hessian evaluations = 17Removing a single redundant constraint reduced the number of objective and constraint evaluations 5-fold!