Pseudo Boolean Rules
Interaction
As a starting point for the pseudo boolean reasoning we will get constraints from the user, which are subject to a normalization process. Then the rules can be applied to produce further inequalities \[ xs := [0,1] \]
\[ as := [(1,0),(2,0)] \equiv x + 2y \]
\[ bs := [(0,3),(2,0)] \equiv 3 \overline{x} + 2y \]
\[ {{c1 := \sum_i{as_i xs_i} \ge 2}\qquad {c2 := \sum_i{bs_i xs_i} \ge 5}} \]
def xs : Fin 2 → Fin 2 := ![0,1]
def c1 : PBIneq ![(1,0),(2,0)] xs 2
def c2 : PBIneq ![(0,3),(2,0)] xs 5
Addition Rule
Two constraints can be added together, adding the coefficients and the constants. Another behaviour is that \(x_i\) and \(\overline{x_i}\) that may appear together cancel each other: \[ \frac {{\sum_i{a_i l_i} \ge A}\qquad {\sum_i{b_i l_i} \ge B}} {\sum_i{(a_i + b_i) l_i} \ge (A+B)} \]
def xs : Fin 2 → Fin 2 := ![0,1]
def c1 : PBIneq ![(1,0),(2,0)] xs 2
def c2 : PBIneq ![(0,3),(2,0)] xs 5
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def c3 : PBIneq ![(0,2),(4,0)] xs 6 := by apply Addition c1 c2
Multiplication Rule
A constraint can be multiplied by any \( c \in \mathbb{N}^{+} \): \[ \frac {\sum_i{a_i l_i} \ge A} {\sum_i{c a_i l_i} \ge c A} \]
def xs : Fin 2 → Fin 2
def c1 : PBIneq ![(1,0),(2,0)] xs 2
-----------------------------------
def c2 : PBIneq ![(2,0),(4,0)] xs 4 := by apply Multiplication c1 2
Division Rule
A constraint can be divided by any \( c \in \mathbb{N}^{+} \), and the the ceiling of this division in applied: \[ \frac {\sum_i{a_i l_i} \ge A} {\sum_i{ \lceil \frac{a_i}{c} \rceil l_i} \ge \lceil \frac{A}{c} \rceil} \]
def xs : Fin 2 → Fin 2
def c1 : PBIneq ![(3,0),(6,0)] xs 7
-----------------------------------
def c2 : PBIneq ![(1,0),(2,0)] xs 3 := by apply Division c1 3
Saturation Rule
A constraint can replace its coefficients by the minimum between them and the constant: \[ \frac {\sum_i{a_i l_i} \ge A} {\sum_i{ \min(a_i,A)\cdot l_i} \ge A} \]
def xs : Fin 2 → Fin 2
def c1 : PBIneq ![(3,0),(6,0)] xs 3
-----------------------------------
def c2 : PBIneq ![(3,0),(3,0)] xs 3 := by apply Saturation c1
All these rules can be seen in practice in the Worked Example