obbt_analysis

(function from pyomo.contrib.alternative_solutions.obbt)

pyomo.contrib.alternative_solutions.obbt.obbt_analysis(model, *, variables=None, rel_opt_gap=None, abs_opt_gap=None, refine_discrete_bounds=False, warmstart=True, solver='gurobi', solver_options={}, tee=False)[source]

Calculates the bounds on each variable by solving a series of min and max optimization problems where each variable is used as the objective function This can be applied to any class of problem supported by the selected solver.

Parameters:

  • model (ConcreteModel) – A concrete Pyomo model.

  • variables (None or a collection of Pyomo _GeneralVarData variables) – The variables for which bounds will be generated. None indicates that all variables will be included. Alternatively, a collection of _GeneralVarData variables can be provided.

  • rel_opt_gap (float or None) – The relative optimality gap for the original objective for which variable bounds will be found. None indicates that a relative gap constraint will not be added to the model.

  • abs_opt_gap (float or None) – The absolute optimality gap for the original objective for which variable bounds will be found. None indicates that an absolute gap constraint will not be added to the model.

  • refine_discrete_bounds (boolean) – Boolean indicating that new constraints should be added to the model at each iteration to tighten the bounds for discrete variables.

  • warmstart (boolean) – Boolean indicating that the solver should be warmstarted from the best previously discovered solution.

  • solver (string) – The solver to be used.

  • solver_options (dict) – Solver option-value pairs to be passed to the solver.

  • tee (boolean) – Boolean indicating that the solver output should be displayed.

Returns:

A Pyomo ComponentMap containing the bounds for each variable. {variable: (lower_bound, upper_bound)}. An exception is raised when the solver encountered an issue.

Return type:

variable_ranges