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Problem-specific certificates #118

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Problem-specific certificates #118

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9e6edaf
Added problem-specific certificates to solveLP_sparse
ericahan22 Sep 29, 2023
b9c0ac7
Attempted to convert all equalities into two inequalities, getting we…
ericahan22 Oct 9, 2023
fa206f7
Modified problem-specific certificates to be in the form c.x >= (slx-…
ericahan22 Oct 13, 2023
604485d
Caught minor bug with setting variable bounds for known values
ericahan22 Oct 20, 2023
4ee6dc1
Cleaned up problem-specific certificates
ericahan22 Oct 20, 2023
1bab607
Reimplemented feasibility as optimization by relaxing inequalities (r…
ericahan22 Oct 27, 2023
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25 changes: 8 additions & 17 deletions inflation/lp/InflationLP.py
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Original file line number Diff line number Diff line change
Expand Up @@ -490,8 +490,7 @@ def set_extra_inequalities(self,
for ineq in extra_inequalities]

def solve(self,
relax_known_vars: bool = False,
relax_inequalities: bool = False,
relaxation: bool = False,
solve_dual: bool = True,
solverparameters: dict = None,
verbose: int = None,
Expand All @@ -503,12 +502,7 @@ def solve(self,

Parameters
----------
relax_known_vars : bool, optional
Do feasibility as optimization where each known value equality
becomes two relaxed inequality constraints. E.g., P(A) = 0.7
becomes P(A) + lambda >= 0.7 and P(A) - lambda <= 0.7, where lambda
is a slack variable. By default, ``False``.
relax_inequalities : bool, optional
relaxation : bool, optional
Do feasibility as optimization where each inequality is relaxed by
the non-negative slack variable lambda. By default, ``False``.
solve_dual : bool, optional
Expand All @@ -524,13 +518,11 @@ def solve(self,
given by ``_prepare_solver_arguments()``. However, a user may
manually override these arguments by passing their own here.
"""
if (relax_known_vars or relax_inequalities) and \
len(self.objective) > 0:
if relaxation and len(self.objective) > 0:
warn("You have a non-trivial objective, but set to solve a "
"feasibility problem as optimization. Setting "
"relax_known_vars=False, relax_inequalities=False, and "
"optimizing the objective...")
relax_known_vars = relax_inequalities = False
"relaxation=False and optimizing the objective...")
relaxation = False
if verbose is None:
real_verbose = self.verbose
else:
Expand All @@ -545,15 +537,14 @@ def solve(self,
# Still allow for 'feas_as_optim' as an argument
if 'feas_as_optim' in solver_arguments:
if solver_arguments["feas_as_optim"]:
relax_known_vars = relax_inequalities = True
relaxation = True
del solver_arguments["feas_as_optim"]
else:
relax_known_vars = relax_inequalities = False
relaxation = False
del solver_arguments["feas_as_optim"]
args.update(solver_arguments)

args.update({"relax_known_vars": relax_known_vars,
"relax_inequalities": relax_inequalities,
args.update({"relaxation": relaxation,
"verbose": real_verbose,
"default_non_negative": real_default_non_negative,
"solverparameters": solverparameters,
Expand Down