Add BoneMarketModel.AddDivisionApproximateExponentiationEquality
This method combines two common operations, avoiding the need for intermediate variables at the call site.
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@ -22,6 +22,22 @@ Set `upto` to a value that is unlikely to come into play.
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Each parameter is interpreted as a BoundedLinearExpression, and a layer of indirection is applied such that each Constraint in the returned tuple can accept an enforcement literal."""
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return self.AddAllowedAssignments((target, var), ((int(base**exp), base) for base in range(upto + 1)))
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def AddDivisionApproximateExponentiationEquality(self, target, num, denom, exp, upto):
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"""Adds `target == (num // denom)**exp` using a lookup table.
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Set `upto` to a value that is unlikely to come into play.
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`target`, `num`, and `denom` are interpreted as a BoundedLinearExpression, and a layer of indirection is applied such that each Constraint in the returned tuple can accept an enforcement literal."""
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quotient = self.NewIntVar(f'{repr(target)} == ({repr(num)} // {repr(denom)})**{repr(exp)}: quotient')
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intermediate_num, num_constraint = self.NewIntermediateIntVar(num, f'{repr(target)} == ({repr(num)} // {repr(denom)})**{repr(exp)}: num', lb = 0)
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intermediate_denom, denom_constraint = self.NewIntermediateIntVar(denom, f'{repr(target)} == ({repr(num)} // {repr(denom)})**{repr(exp)}: denom', lb = 1)
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intermediate_target, target_constraint = self.NewIntermediateIntVar(target, f'{repr(target)} == ({repr(num)} // {repr(denom)})**{repr(exp)}: target')
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super().AddDivisionEquality(quotient, intermediate_num, intermediate_denom)
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super().AddAllowedAssignments((intermediate_target, quotient), ((int(base**exp), base) for base in range(upto + 1)))
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return (num_constraint, denom_constraint, target_constraint)
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def AddDivisionEquality(self, target, num, denom):
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"""Adds `target == num // denom` (integer division rounded towards 0).
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