Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
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1 Configurational Analysis beyond the Quine-McCluskey algorithm Adrian Dușa University of Bucharest Milan, 8 February 2018 Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
2 Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
3 QCA and the Boolean minimization process If two or more instances of the phenomenon under investigation have only one circumstance in common, the circumstance in which alone all the instances agree, is the cause (or effect) of the given phenomenon. John Stuart Mill, A System of Logic, Vol p A B Y Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
4 Boolean minimization algorithms classical Quine-McCluskey (QMC) improved QMC enhanced QMC (eqmc) Espresso CCubes All of these algorithms have a truth table as an input. Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
5 The truth table A B C D These are all possible (16) configurations of 4 causal conditions, where the total number of rows is usually equal to 2 k, where k is the number of causal conditions. In fact, the real number of rows for a truth table is: k TT = l c (1) c=1 Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
6 The truth table A B C D OUT These are all possible (16) configurations of 4 causal conditions, where the total number of rows is usually equal to 2 k, where k is the number of causal conditions. In fact, the real number of rows for a truth table is: k TT = l c c=1 Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
7 The truth table A B C D OUT ? ? ? ? ? ? ? ? ? ? Main solution types: Conservative (complex) solution: minimization based on the observed positive output configurations only Parsimonious solution: the observed positive output configurations plus the remainders (aka don t cares in electrical engineering) Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
8 Classical QMC It is a two-level logic minimization, with: Step 1. Find all prime implicants (PIs) of a given set of initial configurations (with or without including the remainders) Step 2. Solve the PI chart that translates the PIs to the positive output configurations Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
9 Classical QMC step 1: finding the prime implicants verify all possible pairs of rows for each pair of rows, check all pairs of columns for differences if there is a single difference, minimize the pair of rows otherwise return This procedure is NP complete (nondeterministic polynomial time), with exponential increase in complexity and memory consumption. Configurational Analysis beyond the Quine-McCluskeyMilan, algorithm 8 February / 29
10 Classical QMC step 2: solving the PI chart B x x AD x A D x x C D x Find all possible combinations of (a minimal number of) prime implicants that cover all initial positive output configurations. Two models in the parsimonious solution: 1 AD + B 2 AD + A D Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
11 Improved (step 1 of) QMC: the 2x y rule A B C D Translated in the 3 k matrix: A B C D = 65 Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
12 Improved QMC Dușa (2010) A mathematical approach to the Boolean minimization problem, Quality & Quantity 44(1), All possible prime implicants are found in the implicant matrix: for binary causal conditions, it has 3 k rows. A more general formula that is valid for multi-value causal conditions is: k IM = (l c + 1) (2) c=1 where l c is the number of levels for condition c = 1... k Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
13 Improved QMC Further mathematical tricks are applied to determine which pairs of rows can be minimized, to make sure the 2x y rule is applied only where possible, requiring either computation time or memory to store the matrix of minimizable pairs of rows (that has 2 k rows and k columns). Compared to classical QCA that has an upper complexity ceiling of about causal conditions, it managed to: increase the complexity to about causal conditions (the difference between 3 12 and 3 15 ) find solutions in many orders of magnitude less time than QMC Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
14 eqmc It only concentrates on the observed (positive and the negative) instances. A B C D OUT A B C D Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
15 eqmc Supersets of the positive output configurations: Supersets of the negative output configurations: Difference: Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
16 eqmc A B C D Non-redundant PIs: A B C D C D B AD A D Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
17 eqmc Dușa and Thiem (2015) Enhancing the Minimization of Boolean and Multivalue Output Functions With eqmc, Journal of Mathematical Sociology 39, The complexity level reached 18 causal conditions, with: a minimal memory consumption, decreasing from about 1.5 GB for the improved QMC, to some tens of MB for eqmc hundreds of times faster than the (improved) QMC Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
18 Espresso Developed more than 30 years ago at MIT Different method from QMC: instead of expanding a logic function with minterms, it iteratively manipulates the product terms in the ON, OFF and DC sets Can deal with any number of inputs and outputs It is a heuristic logic minimizer, with a very quick solution that is not guaranteed to be a global minimum (just very closely approximated) Not suitable for QCA, where we need all possible solutions for a given problem, not just the first quick one Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
19 CCubes Top - down From complex towards parsimonious Bottom - up From parsimonious towards more complex Definition: A prime implicant is the simplest possible, non-redundant, fully consistent superset of a positive output configuration. Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
20 CCubes A B C D A B C D ~B OUT Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
21 CCubes ~A~D ~AD A~D AD Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
22 CCubes: partitioning the search space The implicant matrix from equation 2 can be partitioned to: S MV = k c=1 ( ) k c l s (3) c s=1 S CS = k c=1 ( ) k c 2 = c s=1 k c=1 ( ) k 2 c = 3 k 1 (4) c Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
23 CCubes: partitioning the search space The following equality (between equations 2 and 3) holds: ( ) k k k c (l c + 1) = 1 + l s c c=1 c=1 s=1 Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
24 CCubes: shortening the search space ~A~D ~AD A~D AD A D OUT Not all combinations of 2 out of 4 conditions are meaningful / useful Concentrate only on the observed combinations, which are already found in the truth table Both AD and A D are fully consistent with the positive output, because they are not found in the negative output configurations. Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
25 CCubes: search depth Usually, about half of the entire search space is void, does not produce any prime implicants. where: P = prime implicants k c=1 U = unique subsets pf the prime implicants ( ) k c l s = P + U + N (5) c s=1 N = non prime implicants (supersets of the negative configurations) Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
26 CCubes: search depth and min.pin Check after each complexity level if: the PI chart can be solved the PI chart can be minimally solved: the disjunctive solution has the same length as the one from the previous level of complexity the PIs generated at the current complexity level contribute to minimally solving the PI chart (argument min.pin) Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
27 Performance benchmark hundreds of times faster than eqmc works with up to 30 conditions (compared to a maximum of 18 for eqmc) the Espresso challenge: 1000 random samples, 20 conditions and 20 cases Espresso x = seconds s = seconds CCubes x = seconds s = seconds x MP = seconds Dușa (2018, forthcoming) Consistency Cubes: A Fast, Efficient Method for exact Boolean Minimization., R Journal. Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
28 CCubes: pseudo-counterfactual method CCubes does not explicitly uses the remainders but it always finds exactly the same solutions as the classical QMC when including the remainders it can be used to find intermediate solutions it can be used for ESA, by excluding the problematic remainders (which are added to the set of negative output configurations) Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
29 Thank you Dușa (2018, forthcoming) QCA with R. A comprehensive resource. dusa.adrian@unibuc.ro Configurational Analysis beyond the Quine-McCluskey Milan, algorithm 8 February / 29
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