A Paper Presentation on “ DETERMINING NEED FOR CNF TO DNF CONVERSION BY IMPLEMENING POLYNOMIAL ALGORITHMS USING GRID COMPUTING IN PARALLEL”. Presented By: Mayuresh S. Pardeshi
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APaper Presentationon“DETERMINING NEED FOR CNF TO DNF CONVERSION BY IMPLEMENING POLYNOMIAL ALGORITHMS USING GRID COMPUTING IN PARALLEL”
Presented By:
Mayuresh S. Pardeshi
M. Tech
Department of Computer Science and Engineering. Walchand College of Engineering, Sangli.
Contents
Recently, high dimension optimal conjunctive normal form to optimal(prime implicants) disjunctive normal form conversion is considered to be as an NP hard problem. So we will be converting it to an NP complete.
Conversion of NP hard problem to NP complete.
Achieve maximum simplification of boolean functions.
Operations on high dimension data as much as possible.
Improve performance by utilizing grid resources in parallel.
Monotone case:
Functions with small cnfsize but large dnfsize.
Functions with conjunction of small parity and majority functions.
Lemma to estimate cnfsize and dnfsize of such functions.
3. Upper Bounds on the Blow-up
Lemma-2: For all S c V and all boolean functions f with variable V,
dnfsize(f) ≤ ΣpєRS ^V dnfsize(fp)
Let 2n≤m≤2n-1. There is a function f with cnfsize(f) ≤ m and
dnfsize(f)≥2[n-2n/log(m/n) ]
Let 4n ≤ m ≤ (n / [n/2]). Then, there is a monotone function h with cnfsize(h) ≤ m and dnfsize(h) ≥ 2[n-nloglog(m/n) ]/log(m/n) – log(m/n) .
Algorithm for Multi-Line Boole
We save the first step and define f ∈ B4 by Q4 , the set of implicants of length 4.
Q4: Q4,4 = ø , Q4,3 = {a b c d ;a b c d },Q4,2 = {a b c d ;a b c d ; a b c d },
Q4,1 = {a b c d ;a b c d ; a b c d},Q4,0 = {a b c d} .
Q3: Q3,3 = ø , Q3,2 = {a b c ;a b c ; b c d }
Q3,1 = {a b d ; a c d ; a b c ; a c d ; b c d }, Q3,0 = {a b c ; a c d ; b c d },
P4 = ø.
Q2: Q2,2 = ø , Q2;1 = {b c} , Q2;0 = {c d ; a c}
P3 = {a b c; a b d} .
Q1 = ø .
P2 = Q2 .
PI(f) = { a b ; ac ; a d }:
Results for Multi-Line boole’s algorithm:
Problem statement taken is:
∀x [∀y Animal(y) ⇒ Loves(x, y)] ⇒ [∃y Loves(y, x)]
Solution achieved using coding:
1. Eliminate implications: ∀x [¬∀y ¬Animal(y) ∨ Loves(x, y)] ∨ [∃y Loves(y, x)]
2. Move ¬ inwards
• ∀x [∃y ¬(¬Animal(y) ∨ Loves(x, y))] ∨ [∃y Loves(y, x)]
• ∀x [∃y ¬¬Animal(y) ∧ ¬Loves(x, y)] ∨ [∃y Loves(y, x)] (De Morgan)
• ∀x [∃y Animal(y) ∧ ¬Loves(x, y)] ∨ [∃y Loves(y, x)] (double negation)
3. Standardize variables: ∀x [∃y Animal(y) ∧ ¬Loves(x, y)] ∨ [∃z Loves(z, x)]
4. Skolemization: ∀x [Animal(F(x)) ∧ ¬Loves(x, F(x))] ∨ [Loves(G(x), x)]
5. Drop universal quantiﬁers: [Animal(F(x)) ∧ ¬Loves(x, F(x))] ∨ [Loves(G(x), x)]
6. Distribute ∨ over ∧: [Animal(F(x)) ∨ Loves(G(x), x)] ∧ [¬Loves(x, F(x)) ∨ Loves(G(x), x)]
p cnf 100 160
16 30 95 0
-16 30 95 0
-30 35 78 0
-30 -78 85 0
-78 -85 95 0
8 55 100 0
8 55 -95 0
9 52 100 0
9 73 -100 0
-8 -9 52 0
38 66 83 0
-38 83 87 0
-52 83 -87 0
66 74 -83 0
-52 -66 89 0
-52 73 -89 0
-52 73 -74 0
-8 -73 -95 0
Implementation of algorithm using MPICH-G2, a globus tool for inherent parallelism in grid computing.
Improvement in terms of reduction in complexity.
REFERENCES:
[1] Wegener, I. , The Complexity of Boolean Functions , John Wiley & Sons Ltd, and B. G. Teubner, Stuttgart.
[2] Peter Bro Miltersen, JaikumarRadhakrishnan and Ingo Wegener On Converting CNF To DNF Electronic Colloquium on Computational Complexity, Report No. 17 (2003)
[3] Hisayuki TATSUMI , Masahiro MIYAKAWA Masao MUKAIDONO , Upper and Lower Bounds on the Number of Disjunctive Forms , Proceedings of the 36th International Symposium on Multiple-Valued Logic (ISMVL’06), IEEE computer society.
[4] Paul Beame, A Switching Lemma Primer, University of Toronto.
[5]RajatAuora and Michael S. Hsiao, CNF Formula Simplication Using Implication Reasoning" 0-7803-8714-7104 2004 IEEE Transactions.
[6] Kenneth H. Rosen, Discrete Mathematics and It's Applications , 6th Edition, CRC Press.
[7] Wahid Chrabakh , Rich Wolski , GrADSAT: A Parallel SAT Solver for the Grid, UCSB Computer Science Technical Report Number 2003-05, Department of Computer Science, University of California Santa Barbara
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[10] Gi-Joon Nam, FadiAloul, A Comparative Study of Two Boolean Formulations of FPGA Detailed Routing Constraints, IEEE TRANSACTIONS ON COMPUTERS, VOL. 53, NO. 6, JUNE 2004.
[11] Marcus Fontoura, Suhas Sadanandan, Jayavel Shanmugasundaram Sergei Vassilvitski, Erik Vee, SrihariVenkatesan, Jason Zien, Eciently Evaluating Complex Boolean Expressions, SIGMOD10, June 611, 2010, Indianapolis, Indiana, USA.
[12] DragosSbirlea, QuineMcKluskey Algorithm,
http://www.codeproject.com/Articles/15756/Quine-McKluskey- Algorithm
[13] Prof. Michael B. Feldman, BINARY TREES, The George Washington University http://www.seas.gwu.edu/~csci133/fall05
[14] Douglas Gregor and Benjamin Martin, MPI.NET Tutorial in C#, Indiana University.
Thank You