# The Boolean Satisfiability Problem vilely censured.

## Here I expose some ultimate techniques to make great tools for logic operations. Apologies: someone hacked the article to avoid sharing this technology.

Jun 3, 2019 · 8 min read

# Improving our notation

`>>> from numpy import matrix>>> Amatrix([[ True, False, False],        [False,  True,  True],        [False,  True,  True]])>>> Bmatrix([[ True, False,  True],        [False,  True, False],        [ True, False,  True]])>>> A&Bmatrix([[ True, False, False],        [False,  True, False],        [False, False,  True]])`
`def cMatrix(clauseC, clauseR):    R = matrix([[True] * len(clauseC)] * len(clauseR))    for i, X in enumerate(clauseC):        if X in clauseR:            j = clauseR.index(X)            R &= matrix([[(i==col) == (j==row) \                         for col in range(len(clauseC))] \                        for row in range(len(clauseR))])    return R`
`>>> cMatrix((1, 2, 4, 5), (1, 2, 3, 6, 7))matrix([[ True, False, False, False],        [False,  True, False, False],        [False, False,  True,  True],        [False, False,  True,  True],        [False, False,  True,  True]])`

# Coherence between more than two clauses

`tables[(C,D)] &= tables[(A, D)]*tables[(C, A)]`

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