Feasibility pump and updates in GOA #12
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| solve_data.LB = results['Problem'][0]['Lower bound'] | ||
| elif not math.isnan(results['Problem'][0]['Upper bound']): | ||
| solve_data.UB = results['Problem'][0]['Upper bound'] | ||
| # TODO: should we use the bound of the rNLP here? |
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If the solver provides a lower bound, we could use it (in the case for example we do not trust the optimal solution of the rNLP). I think that in general, we decide to trust the bound though.
pyomo/contrib/mindtpy/nlp_solve.py
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| @@ -48,7 +48,10 @@ def solve_subproblem(solve_data, config): | |||
| TransformationFactory('core.fix_integer_vars').apply_to(fixed_nlp) | |||
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| MindtPy.MindtPy_linear_cuts.deactivate() | |||
| # TODO: Do we really need to calculate tmp_duals by default? | |||
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This is a good point. If we do not add the equality relaxation cuts this is time-consuming and dumb. Consider removing it.
pyomo/contrib/mindtpy/nlp_solve.py
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| @@ -71,7 +74,8 @@ def solve_subproblem(solve_data, config): | |||
| try: | |||
| fixed_nlp.tmp_duals[c] = c_geq * max( | |||
| 0, c_geq*(rhs - value(c.body))) | |||
| except (ValueError, OverflowError) as error: | |||
| except (ValueError, OverflowError, AttributeError) as error: | |||
| # AttributeError is included to condition when value(c.body) belongs to complex type | |||
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This happens when value(c.body) is a complex number. In some examples, due to the numerical computation, 1+0.0001j may show up.
| counter += 1 | ||
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| config.logger.info("Added %s affine cuts" % counter) | ||
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| def add_lazy_nogood_cuts(self, var_values, solve_data, config, opt, feasible=False): | ||
| def add_lazy_no_good_cuts(self, var_values, solve_data, config, opt, feasible=False): |
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I thought there was a TODO above mentioning this. Are we using no-good in single tree? Did you try the taboo list mentioned in the storm of feasibility pump paper?
A way of implementing this is their section 5.2 using https://www.ibm.com/support/knowledgecenter/SSSA5P_12.10.0/ilog.odms.cplex.help/refpythoncplex/html/cplex.callbacks.Context-class.html#reject_candidate
| @@ -23,7 +23,7 @@ | |||
| from pyomo.contrib.mindtpy.tests.feasibility_pump1 import Feasibility_Pump1 | |||
| from pyomo.contrib.mindtpy.tests.feasibility_pump2 import Feasibility_Pump2 | |||
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| required_solvers = ('ipopt', 'cplex') | |||
| required_solvers = ('ipopt', 'glpk', 'cplex') | |||
| if all(SolverFactory(s).available() for s in required_solvers): | |||
| subsolvers_available = True | |||
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This will skip some of the tests of CPLEX is not available although mots of them run with GLPK.
I am a little confused about how they handle an integer solution found by CPLEX at the root node appears in the tabu list. Will the root node make a difference? |
I am pretty sure this had a syntax error (undefined rank variable) and was never used (certainly not covered by any tests).
Convert `Disjunct.indicator_var` from binary to Boolean variable
Allow assigning None to a unitted variable
Update Pyomo book examples & tests for the 3rd edition
pyomo.network / pyomo.gdp interoperability
…o replace-simple
Embed dependencies in setup.py
Move constrained_layout tests to 'expensive' suite
Implementation of LOA single-tree
Update IDAES branch to latest Pyomo master
Fixes # .
Summary/Motivation:
Changes proposed in this PR:
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