APPSI

Auto-Persistent Pyomo Solver Interfaces

• APPSI Base Classes
• Solvers
  • Gurobi
    • Handling Gurobi licenses through the APPSI interface
  • Ipopt
  • Cplex
  • Cbc
  • HiGHS
  • MAiNGO

APPSI solver interfaces are designed to work very similarly to most Pyomo solver interfaces but are very efficient for resolving the same model with small changes. This is very beneficial for applications such as Benders’ Decomposition, Optimization-Based Bounds Tightening, Progressive Hedging, Outer-Approximation, and many others. Here is an example of using an APPSI solver interface.

>>> import pyomo.environ as pyo
>>> from pyomo.contrib import appsi
>>> import numpy as np
>>> from pyomo.common.timing import HierarchicalTimer
>>> m = pyo.ConcreteModel()
>>> m.x = pyo.Var()
>>> m.y = pyo.Var()
>>> m.p = pyo.Param(mutable=True)
>>> m.obj = pyo.Objective(expr=m.x**2 + m.y**2)
>>> m.c1 = pyo.Constraint(expr=m.y >= pyo.exp(m.x))
>>> m.c2 = pyo.Constraint(expr=m.y >= (m.x - m.p)**2)
>>> opt = appsi.solvers.Ipopt()
>>> timer = HierarchicalTimer()
>>> for p_val in np.linspace(1, 10, 100):
>>>     m.p.value = float(p_val)
>>>     res = opt.solve(m, timer=timer)
>>>     assert res.termination_condition == appsi.base.TerminationCondition.optimal
>>>     print(res.best_feasible_objective)
>>> print(timer)

Alternatively, you can access the APPSI solvers through the classic SolverFactory using the pattern appsi_solvername.

>>> import pyomo.environ as pyo
>>> opt_ipopt = pyo.SolverFactory('appsi_ipopt')
>>> opt_highs = pyo.SolverFactory('appsi_highs')

Extra performance improvements can be made if you know exactly what changes will be made in your model. In the example above, only parameter values are changed, so we can setup the UpdateConfig so that the solver does not check for changes in variables or constraints.

>>> timer = HierarchicalTimer()
>>> opt.update_config.check_for_new_or_removed_constraints = False
>>> opt.update_config.check_for_new_or_removed_vars = False
>>> opt.update_config.update_constraints = False
>>> opt.update_config.update_vars = False
>>> for p_val in np.linspace(1, 10, 100):
>>>     m.p.value = float(p_val)
>>>     res = opt.solve(m, timer=timer)
>>>     assert res.termination_condition == appsi.base.TerminationCondition.optimal
>>>     print(res.best_feasible_objective)
>>> print(timer)

Solver independent options can be specified with the SolverConfig or derived classes. For example:

>>> opt.config.stream_solver = True

Solver specific options can be specified with the solver_options() attribute. For example:

>>> opt.solver_options['max_iter'] = 20

Installation

There are a few ways to install Appsi listed below.

Option1:

pyomo build-extensions

Option2:

cd pyomo/contrib/appsi/
python build.py

Option3:

python
>>> from pyomo.contrib.appsi.build import build_appsi
>>> build_appsi()