# Counting Falsifying Assignments of a 2-CF via Recurrence Equations

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1 Counting Falsifying Assignments of a 2-CF via Recurrence Equations Guillermo De Ita Luna, J. Raymundo Marcial Romero, Fernando Zacarias Flores, Meliza Contreras González and Pedro Bello López Abstract We consider the problem #2UNSAT (counting the number of falsifying assignments for two conjunctive forms). Because #2SAT (F )=2 n #2UNSAT (F ), results about #2UNSAT can dually be established for solving #2SAT.We establish the kind of two conjunctive formulas with a minimum number of falsifying assignment. As a result, we determine the formulas with a maximum number of models among the set of all 2-CF formulas with a same number of variables. We have shown that for any 2-CF F n with n variables, #UNSAT (F n) #SAT (F n) with the exception of totally dependent formulas. Thus, if #UNSAT (F n) apple p(n) for a polynomial on n, then #SAT (F n) apple p(n) too, and then #SAT (F n) can be computed in polynomial time on the size of the formula. Index Terms #SAT, Binary Patterns, Enumerative Combinatorics. I. INTRODUCTION THERE are many real-life problems that can be abstracted as counting combinatorial objects on graphs. For instance, reliability network issues are often equivalent to connected component issues on graphs, e.g. the probability that a graph remains connected is given by the probabilities of failure over each edge, which is essentially the same as counting the number of ways that the edges could fail without losing connectivity [9],[10]. The combinatorial problems that we are going to address are the computation of the number of models and falsifying assignment for Boolean formulas in two Conjunctive Forms (2-CF), denoted as #2SAT and #2UNSAT respectively, based on string patterns formed by their set of falsifying assignments [11]. Both problems are classical #P-complete problems even for the restricted cases of monotone and Horn formulas. Among the class of #P complete problems,#sat is considered a fundamental instance due to both its application in deduction issues and its relevance in establishing a boundary between efficient and intractable counting problems. Previous studies have developed levels of complexity for counting #SAT and due to the duality between #SAT and #UNSAT, bounds can be set for the latter. Several algorithms have been designed for computing #UNSAT (F ) as finer or shorter versions of the application of the inclusion-exclusion formula [6], [5], [7]. However, there are no concise algorithms or procedures which establish Manuscript received December 15, 2014; revised January 2, G. De Ita, F. Zacarias,M. Contreras, P. Bello are with the Faculty of Computer Science, Benemerita Universidad Autónoma de Puebla, Puebla, México J. R. Marcial is with the Department of Computation Faculty of Engineering, Universidad Autónoma del Estado de México, Toluca, México the cases in which #UNSAT (F ) can be computed in polynomial time considering its constrained graph. Since #SAT (F )=2 n #UNSAT (F ), analogous results can be proved for both. We investigate about the minimum number of falsifying assignments that a 2-CF could have. In this paper, we show some cases where #SAT (F ) can be upper bounded by considering a binary pattern analysis over its set of falsifying assignments. II. PRELIMINARIES Let X = {x 1,...,x n } be a set of n boolean variables. A literal is either a variable x i or a negated variable x i. A clause is a disjunction of different and non complementary literals (sometimes, we also consider a clause as a set of literals, e.g. x 1 _ x 2 = {x 1, x 2 }). For a positive integer k, ak-clause is a clause consisting of exactly k literals. A variable x 2 X appears in a clause c if either x or x is an element of c. A conjunctive form (CF) F is a conjunction of nontautological clauses. We say that F is a monotone positive CF if all of its variables appear in unnegated form. A k-cf is a CF containing only k-clauses. (apple k)-cf denotes a CF containing clauses with at most k literals. A 2-CF formula F is said to be strict only if each clause of F is binary. We use (X ) to represent the variables involved in the object X, where X could be a literal, a clause or a CF. For instance, for the clause c = { x 1, x 2 }, (c) ={x 1, x 2 }. Lit(F ) is the set of literals appearing in F, i.e. if X = (F ), then Lit(F )=X [ X = {x 1, x 1,...,x n, x n }. We denote the set of n first integers by [n ] and the cardinality of a set A by A. An assignment s for F is a boolean function s : (F )!{0, 1 }. An assignment s can also be considered as a set of non-complementary pairs of literals, e.g., if l 2 s, then l 62 s, in other words s turns l true and l false. Let c be a clause and s an assignment, c is satisfied by s if and only if c \ s 6= ;. On the other hand, if for all l 2 c, l 2 s, then s falsifies c. If n = (F ), then there are 2 n possible assignments defined over (F ). Let S(F ) be the set of 2 n assignments defined over (F ). Let F be a CF. F is satisfied by an assignment s if each clause in F is satisfied by s. F is contradicted by s if any clause in F is falsified by s. A model of F is an assignment for (F ) that satisfies F. A falsifying assignment of F is an assignment for (F ) that contradicts F. The SAT problem consists of determining whether F has a model. SAT (F ) denotes the set of models of F, then SAT (F ) S(F ). Let UNSAT (F )=S(F ) SAT (F ), i.e. UNSAT (F ) is the set of assignments from S(F ) that falsify F.

2 The #SAT problem (or #SAT (F )) consists of counting the number of models of F defined over (F ), #2SAT denotes #SAT for formulas in 2-CF. So #UNSAT (F )=2 n #SAT (F ). A 2-CF F can be represented by an undirected graph, called the constrained graph of F, and determined as: G F =(V (F ), E(F )), where V (F )= (F ) and E(F )={{ (x), (y)} : {x, y} 2F }. I.e. the vertices of G F are the variables of F, and for each clause {x, y} in F there is an edge { (x), (y)} 2E(F ). In order to compute #SAT (F ), we can consider first the connected components of the constrained graph G F of F. Such connected components {G 1,...,G k } of G F can be determined in linear time with respect to the number of clauses Q in the formula, and then #SAT (F ) = #SAT (G F ) = k i=1 #SAT (G i). The set of connected components of G F conforms a partition of F. So, from now on, we will work with a connected component graph. We say that a 2-CF F is a path, a cycle, or a tree if its corresponding constrained graph G F represents a path, a cycle, or a tree, respectively. III. COMPUTING #2UNSAT VIA RECURRRENCE EQUATIONS Let F = {C 1, C 2,...,C m } be a strict 2-CF (a conjunction of binary clauses) and let n = (F ). The size of F is n m. Let k be a positive integer parameter such that k < 2 n. The values of k where #SAT (F )=k can be determined in polynomial time have been proved for the following cases [8]. If k = 0 or k = 1 the Transitive Closure procedure presented in [1] can be applied. Such procedure has a linear time complexity on the size of the 2-CF. If k is upper bounded by a polynomial function on n, e.g. k apple p(n), then in [2], an exact algorithm was shown for determining when #SAT (F ) apple p(n). Such algorithm has a polynomial time complexity on the size of F. So, the cases where a polynomial time complexity algorithm has not been found to answer whether #SAT (F )=k are given when k > p(n). We further bounded those cases by the following theorem (proved in [4]). Theorem 1. [4] Let F = {C 1, C 2,...,C m } be a 2-CF and n = (F ). If m apple n then #SAT (F ) is computed efficiently. In this section we present the binary patterns used to represent the set of falsifying assignments of a 2-CF. Those patterns allow us to design efficient procedures for computing #2UNSAT. We firstly present some previous results to give a context to this paper. Lemma 1. [4] Let F = {C 1,...,C m } be a 2-CF and n = (F ). Then #UNSAT (F ) has at least 2 n 2 elements. Let F = {C 1,...,C m } be a 2-CF and n = (F ). Assume an enumeration over the variables of (F ), e.g. x 1, x 2,...,x n. For each C i = {x j, x k }, let A i be a set of binary strings such that the length of each string is n. The values at the j -th and k-th positions of each string, 1 apple j, k apple n represent the truth value of x j and x k that falsifies C i. E.g., if x j 2 C i then the j -th element of A i is set to 0. On the other hand, If x j 2 C i then the j -th element of A i is set to 1. The same argument applies to x k. We will use the symbol to represent the elements that can take any truth value in the set A i, e.g. if F = {C 1,...,C m } is a 2-CF, n = (F ), C 1 = {x 1, x 2 } and C 2 = {x 2, x 3 } then we will write A 1 = and A 2 = This abuse of notation will allow us to present a concise and clear representation in the rest of the paper, for considering the string A i as a binary pattern that represents the falsifying assignments of the clause C i. We call A Falsifying String. It is known [5] that for any pair of clauses C i and C j, it holds that #UNSAT (C i [ C j )= #UNSAT (C i )#UNSAT (C j ) #UNSAT (C i \ C j ). The following lemmas show when the number of models can be reduced. Lemma 2. [4] Let F be a 2-CF, n = (F ). If C i 2 F and C j 2 F, i 6= j have no complementary pairs of literals and they share a literal (e.g. C i \ C j 6= ;), then there are exactly 2 n 1 2 n 3 assignments from S(F ) falsifying C i [ C j. Lemma 3. [4] Let F be a 2-CF, n = (F ). If C i 2 F and C j 2 F, i 6= j contain complementary literals, that is x k 2 C i and x k 2 C j, the unsatisfied set of assignments A i and A j form a disjoint set of assignments. Consequently, both clauses suppress exactly 2 n 2 2 n 2 = 2 n 1 assignments from S(F ). Definition III.1. [5] Given two binary clauses C i, C j, If they have at least one complementary literal, it is said that they are independent. Otherwise, we say that both clauses are dependent. Definition III.2. Let F = {C 1, C 2, C m } be a 2-CF. F is called independent if for each pair of clauses C i, C j 2 F, i 6= j, it holds that x k 2 C i and x k 2 C j, otherwise F is called dependent. Let C be a clause and let x be any variable, we have that C =(C _ x) ^ (C _ x) (1) Furthermore, this reduction preserves the number of falsifying assignments, since #UNSAT (C )=2 n ( C 1 ) 2 n ( C 1 ) = #UNSAT ((C _ x)^(c _ x)), because (C _ x) ^(C _ x) are two independent clauses. Given a pair of dependent clauses C 1 and C 2. Let us assume that there exist literals in C 1 which are not in C 2, and let x 1, x 2,...,x p be these literals. There exists a reduction to transform C 2 (or C 1 ) to be independent with C 1 (or C 2 ), we will call this transformation the independence reduction, and this works in the following way. By (1) one can write: C 1 ^ C 2 = C 1 ^ (C 2 _ x 1 ) ^ (C 2 _ x 1 ). Now C 1 and (C 2 _ x 1 ) are independent. Applying (1) to (C 2 _ x 1 ): C 1 ^ C 2 = C 1 ^ (C 2 _ x 1 ) ^ (C 2 _ x 1 _ x 2 ) ^ (C 2 _ x 1 _ x 2 ) The first three clauses are independent. Repeating the independence reduction until x p, we have that C 1 ^ C 2 can be written as: C 1 ^ (C 2 _ x 1 ) ^ (C 2 _ x 1 _ x 2 ) ^...^ (C 2 _ x 1 _ x 2 _... _ x p ) ^ (C 2 _ x 1 _ x 2 _... _ x p ). The last clause contains all literals of C 1, so it can be eliminated, and then

3 C 1 ^ C 2 = C 1 ^ (C 2 _ x 1 ) ^ (C 2 _ x 1 _ x 2 ) ^...^ (C 2 _ x 1 _ x 2 _..._ x p ) (2) Clauses on the right hand side of (2) are independent by construction. For any independent formula F = {C 1,...,C m } involving n variables, we have that: #UNSAT (F )= P m i=1 2 n Ci. In particular, if F is a k-cf then #UNSAT (F )= P m i=1 2 n k. And as F is unsatisfiable when #UNSAT (F )=2 n, then all k-cf with at least 2 k independent clauses will be unsatisfiable. On the other hand, a pair of dependent binary clauses can be one of the following two types: a) with one common literal. b) without any variable in common. Case (a): Let C i, C j be two binary dependent clauses involved in a formula with n variables such that x k 2 C i and x k 2 C j. The number of falsifying assignments for this pair of clauses is: 2 n 1 2 n 2, given by one application of the independence reduction: Case (b): let C i = {x i1, x i2 } and C j = {x j1, x j2 } such that all the variables involved in C i and C j are different. The number of falsifying assignments for this pair of clauses is: 2 n 2 2 n 3 2 n 4, according to a double application of the independence reduction. Example III.1. Let C i, C j 2 F, where C i =(x 1 _ x 2 ), C j =(x 3 _ x 4 ), the transformation to obtain independent clauses is applied over C j as shown: (x 1 _ x 2 ) ^ (x 3 _ x 4 )=(x 1 _ x 2 ) ^ ( x 1 _ x 3 _ x 4 ) ^ (x 1 _ x 2 _ x 3 _ x 4 ). Since the last three clauses are independent we have: #UNSAT (C i ^ C j )=2 n 2 2 n 3 2 n 4. Lemma 4. Let F (n, m) be a 2-CF with n variables and m clauses. Let C = {x, y} be a binary clause not in F. It holds that #UNSAT (F ^ C ) #UNSAT (F ). Proof: It is well known that: A \ B A, A \ B B hence: (UNSAT (F ) \ UNSAT (C )) UNSAT (C ) which means that #(UNSAT (F ) \ UNSAT (C )) #UNSAT (C ). Adding the number of falsifying assignments of F and C to both sides of the inequality: #UNSAT (F )#UNSAT (C ) (#UNSAT (F )\ #UNSAT (C )) #UNSAT (F )#UNSAT (C ) #UNSAT (C ). The left hand size in the inequality is #UNSAT (F ^ C ), while the right hand size is #UNSAT (F ). Definition III.3. A formula F = {C 1, C 2, C m } in which C i = {x 1, l i }, 8C i 2 F and each l i is a literal which comes from a different variable, such formula is called totally dependent. The formula is also totally dependent if each C i = { x 1, l i }, 8C i 2 F and l i involves a different variable with any other l j, i 6= j = 1,...,m. We will call such F a totally dependent positive formula in the first case, and a totally dependent negative formula in the second case. Notice that in a totally dependent formula with m clauses there are m 1 different variables involved. Theorem 2. Let F d (n, n 1 ) be a totally dependent formula with n variables and n 1 clauses, then for any 2-CF F (n, m 1 ), with m 1 n 1, it holds that #UNSAT (F d ) apple #UNSAT (F ). In other words, a totally dependent formula has the minimum number of falsifying assignments into the set of all 2-CF with the same number of variables and at least the same number of clauses. Proof: Let F (n, m 1 ) be a 2-CF such that m 1 > n (the number of clauses is greater than the number of variables). By the result of lemma 4, clauses in F can be chosen until a subformula F 1 (n, n 1 ) F (n, m 1 ) is obtained such that the n variables are considered and #UNSAT (F 1 (n, n 1 )) apple #UNSAT (F (n, m 1 ). It remains to be shown that #UNSAT (F 1 (n, n 1 )) #UNSAT (F d (n, n 1 )). The proof is by induction on the number of clauses in F d. i) For m = 1, it holds, since #UNSAT (F d (2, 1 )) = 1 apple #UNSAT (F 1 (2, 1 )). ii) If m = 2, F d (3, 2 )={(x 1, x 2 ), (x 1, x 3 )} where #UNSAT (F d (3, 2 )) = 3. It can also be easily verified for F 1 (3, 2 ). iii) We asume that the theorem holds for m 1 clauses, that is: #UNSAT (F d (m, m 1 )) apple #UNSAT (F 1 (m, m 1 )). iv) We show that it holds for formulas with m clauses. Let F 1 = {C 1,..,C m } be a 2-CF with m clauses and m 1 variables. Let C m be the only clause where x m1 appears and for any other clause C i 2 F 1, i = 1,...,m 1, x m1 does not appear (hence G F1 is an acyclic graph). As {C 1,...,C m 1 } has less than m clauses, we apply the inductive hypothesis and there exists a totally dependent formula F d (m, m 1 ) such that #UNSAT (F d (m, m 1 )) apple #UNSAT ({C 1,...,C m 1 }). Let us analyze the different cases for (F d (m, m 1 ) ^ C m ). a) If C m = {x 1, x m1 }, then (F d (m, m 1 ) ^ C m ) continues being totally dependent and the theorem holds. Notice that #UNSAT (F d (m 1, m)) = (#UNSAT (F d (m, m 1 ))) 2 1. b) If C m = {x 1, x m1 } then C m is independent with any other clause of F d (m, m 1 ) and #UNSAT (F d (m 1, m)) =#UNSAT (F d (m, m 1 ) ^ C m ) =(#UNSAT (F d (m, m 1 ))) 2 2 m 1 c) If C m = {x i, x m1 }, where (x i ) 6= (x 1 ). By one application of the independence reduction over C m we obtain Cm 0 = { x 1 _ x i _ x m1 } independent with any other clause in F d (m, m 1 ) and #UNSAT (F d (m, m 1 ) ^ C m ) =#UNSAT (F d (m, m 1 ) ^ Cm), 0 then: #UNSAT (F d (m 1, m)) = #UNSAT (F d (m, m 1 ) ^ C m ) =(#UNSAT (F d (m, m 1 ))) 2 2 m 2 As #UNSAT (F d (m 1, m)) = (#UNSAT (F d (m, m 1 )) 2 1 apple (#UNSAT (F d (m, m 1 )) 2 2 m 2 apple (#UNSAT (F d (m, m 1 )) 2 2 m 1,

4 for m > 2. Then, #UNSAT (F d (m 1, m)) apple #UNSAT (F 1 (m 1, m)). A totally dependent formula F d (n, n 1 ) can be represented by a tree of height 2, where the root (level 1) represents the shared literals and each child of the root (level 2) represents the non common literals. In Figure 1 we show a totally dependent positive formula. Notice that #UNSAT (F d (n, n 1 )) can be computed from the value #UNSAT (F d (n 1, n 2 )) according to the following recurrence: #UNSAT (F d (n, n 1 )) = #UNSAT (F d (n 1, n 2 )) 2 1, and developing such recurrence until the base case #UNSAT (F d (2, 1 )) = 1, we obtain that #UNSAT (F d (n, n 1 )) = 2 n 1 1. Fig. 1. x2 x1... x3 xn A totally dependent positive formula Lemma 5. For any 2-CF F n with n variables which is not a totally dependent formula, it holds that #UNSAT (F n ) #SAT (F n ). Proof: As only the totally dependent formula holds that #UNSAT (F )=2 n 1 1. For any other formula F n with n variables, we have that #UNSAT (F n ) 2 n 1, so the lemma is derived from the fact that #UNSAT (F n )#SAT (F n )=2 n. Then, according to this last lemma, an upper bound for the value #SAT (F n ) is the same value of #UNSAT (F n ). IV. AN INCREMENTAL COMPUTATION FOR #2UNSAT Let F d (n, n 1 ) be a totally dependent formula and let C = {x i, x j } be a clause such that (C ) (F d ), and (x 1 ) /2 (C ). This means that (F d ^ C ) has a triangle (see Figure 2)(the cycle: x 1 x i x j x 1 ). It is not hard to compute #UNSAT (F d ^ C ) because with just one application of the independence reduction on C and C i 2 F d, the clause C 0 = { x 1, x i, x j } is independent with each clause in F d, and as the independence reduction preserves the number of unsatisfied assignments, #UNSAT (F d ^ C )=#UNSAT (F d ^ C 0 )= #UNSAT (F d )#UNSAT (C 0 )=2 n n 3. Fig. 2. C1 x1 C2 x2 x3 C... Cn-1 A triangle in a totally dependent formula In fact, the above procedure gives us an easy way to compute #UNSAT (F ^ C ) when the value #UNSAT (F ) has already been computed. The procedure consists of applying the independence reduction on C and the clauses in F involving the variables (C ) until xn TABLE I THE FALSIFYING STRINGS FOR THE CLAUSES IN F 1 x 1 x 2 x 3 x 4 x 5 x 6 C * * * * C 2 * 0 1 * * * C 3 * * 0 1 * * C 4 * * * 0 0 * C 5 * * * * 0 0 C C * 0 * 0 * * a new clause C 0 is built which will be independent with all clauses C i 2 F where (C ) \ (C i ) 6= ; and then #UNSAT (F ^ C )=#UNSAT (F ^ C 0 )= #UNSAT (F )#UNSAT (C 0 ). In [2], it was proved that if the constrained graph G F of the formula is a path, then #UNSAT (F ) is computed applying the following recurrence ( i, i) = 8 (µ i 1 (2 i 2 i 1 ),µ i 1 ) if ( i, i )=(0, 0) >< (µ i 1,µ i 1 (2 i 2 i 1 )) if ( i, i )=(0, 1) (µ i 1 (2 i 2 i 1 >: ),µ i 1 ) if ( i, i )=(1, 0) (µ i 1,µ i 1 (2 i 2 i 1 )) if ( i, i )=(1, 1) Let P = {C i } 5 i=1 be the path in Figure 3, then F 1 = P [{C C }. #UNSAT (P) =53 and if we want to compute (P ^ C C ), C C has to be made independent with each clause from P which is contained by the endpoints of the clause C C. In Table 1, we analyze the falsifying strings of each clause in F 1 [3]. Fig. 3. Cc G F x1 x2 x3 x4 C1 C2 C3 C4 (0,0) (1,0) (3,1) (7,4) (11,15) Computing #UNSAT for the path in the graph. - x5 C5 x6 (26,27) By one application of the independence reduction between C C and C 2, the new string for CC 0 is formed as: 000 and CC 0 is independent with C 3. And as only C 2 and C 3 are the clauses embraced by the back edge in the cycle formed by x 2 x 3 x 4 x 2, then #UNSAT (F 1 )=#UNSAT (P ^ C C )= #UNSAT (P ^ CC 0 )=#UNSAT (P)#UNSAT (C C 0 )= 53 2 n C 0 C = 53 8 = 61. By duality, we have that #SAT (F 1 )=2 n #UNSAT (F 1 )= = 3. In the case that (C ) \ (F )=;, C must be tranformed, via independence reductions until a new clause C 0 is formed from C and F such that C 0 is independent with any other clause C i 2 F. Definition IV.1. The constrained graph G F =( (F ), E) of a 2-CF formula F = {C 1,...,C m } is called a tree if the following holds: 1) There exist j clauses 1 < j < n such that T C j 6= ;. 2) For any F 0 F whose constrained graph is a path, there are no transitive clauses from F 0 in F, and F has no cycles. Lemma 6. If G F is a path, then F = {C 1, C 2,...,C m } = {{x 1 1, x }, {x2, x 3 2 },..., (3)

5 {xm m, xm1 m }}, where i, i 2{0, 1 }, i 2 [m ]. Let f i be a family of clauses of the formula F built as follows: f 1 = ;; f i = {C j } j<i, i 2 [m]. Notice that n = (F ) = m 1, f i f i1, i 2 [m 1]]. Let SAT (f i ) = {s : s satisfies f i }, A i = {s 2 SAT (f i ) : x i 2 s}, B i = {s 2 SAT (f i ) : x i 2 s}. Let i = A i ; i = B i and µ i = SAT (f i ) = i i. For every node x 2 G F, a pair ( x, x) is computed, where x indicates how many times the variable x is true and x indicates the number of times that the variable x is false in the set of models of F. The first pair is ( 1, 1) =(1, 1) since x 1 can be true or false in order to satisfy f 1. The pairs ( x, x) associated to each node x i,i =2,...,m are computed according to the signs ( i, i) of the literals in the clause c i. Let F = {C 1,...,C m } be a 2-CF where G F is a tree. For each father node x p with two branches, one of size i (it has i clauses), and the other of size j (j clauses are involved), the recurrence for updating the charge associated to the node x p : ( p, p), is given as: p = i (2 j j ) j (2 i i ) i j p = i (2 j j) j (2 i (4) i) i j Proof: If two different branches, one of size i and the other of size j meet in the same father node, then there are two charges ( i, i) and ( j, j ) associated at the same variable x p (the father node). ( i, i) initially denotes the number of falsifying assignments for the branch i, and then it updates considering all the assignments for the other branches minus the corresponding number of falsifying assignments (2 j j or 2 j j assignments). A similar argument is given when ( j, j ) is considered. Respectively we have to add the common falsifying assignments for the variable x p ; i j for the value p and i j for p. It is obvious that acyclic graphs can be represented by trees. Let G T be a tree, Figure 4 shows the result after applying equations (4) and (3) on G T. In this instance, #2UNSAT (G T )= = 219. Then, we obtain from the duality principle that #2SAT (G T )=2 7 #2UNSAT (G T )= = 37. Thus, we have the following theorem. a polynomial p(n) where n = (F ). Discarding the minimum dependent trees, we have that 2 n = #UNSAT (F n )#SAT (F n ) 2 n 1 k then the value k is upper bounded by 2 n 2 n 1 = 2 n 1. So, the hard cases for answering whether #SAT (F )=k, are those where p(n) < k < 2 n 1. Thus, we can work over the computation of #UNSAT (F ) as an upper bound for the value of #SAT (F ). V. CONCLUSION We have determined the kind of 2-CF formulas with a minimum number of falsifying assignments from the set of all 2-CF formulas with the same number of variables and at least the same number of clauses. As a result of the duality property between #2SAT and #2UNSAT, we determine the formulas with a maximum number of models from the set of all 2-CF formulas with the same number of variables and at least the same number of clauses. We have shown that for any 2-CF F n with n variables, #UNSAT (F n ) #SAT (F n ) with the exception of the totally dependent formulas. If #UNSAT (F n ) apple p(n) for a polynomial on n, then #SAT (F n ) apple p(n) too, and then #SAT (F n ) can be computed in polynomial time on the size of the formula. REFERENCES [1] V. Dahllöf,P. Jonsson and M. Wahlström, Counting models for 2SAT and 3SAT formulae, Theoretical Computer Sciences, vol. 332, no. 3, pp , Feb [2] G De Ita, R. Marcial Romero and J.A. Hernández, A threshold for a Polynomial Solution of #2SAT, Fundamenta Informaticae, vol. 113, no.1, pp , Jan [3] G. De Ita, P. Bello and M. Contreras, New Polynomial Classes for #2SAT Established Via Graph-Topological Structure, Engineering Letters, vol. 15, pp ,Nov [4] G. De Ita and J.R. Marcial Romero, Computing #2SAT and #2UNSAT by Binary Patterns, Lecture Notes in Computer Sciences, Vol. 7329, pp , Jun [5] O. Dubois, Counting the number of solutions for instances of satisfiability, Theoretical Computer Sciences., vol. 81, pp , Apr [6] N. Linial and N. Nisan, Approximate inclusion-exclusion. Combinatorica, vol. 10, no. 4, pp , Dec [7] E. Lozinskii E, Counting propositional models, Information Processing Letters, vol. 41, no. 4, pp , Apr [8] D. Roth, On the hardness of approximate reasoning, Artificial Intelligence, vol 82, pp , Apr [9] S.P. Vadhan, The Complexity of Counting in Sparse, Regular, and Planar Graphs, SIAM Journal on Computing, vol. 31, no.2, pp , Sep [10] M. Wahlström, A Tighter Bound for Counting Max-Weight Solutions to 2SAT Instances, Lecture Notes in Computer Sciences, vol. 5018, pp , May [11] J. Zhou, M. Yin and C. Zhou, New Worst-Case Upper Bound for #2-SAT and #3-SAT with the Number of Clauses as the Parameter, in Proceedings of the AAAI 2010, pp Fig. 4. Applying the formulas for #UNSAT on a Tree Theorem 3. Let F be a 2-CF where G F is an acyclic graph. #2UNSAT (F ) is computed in linear time on the number of nodes in G F. Proof: A postorder traversal of the tree computes #UNSAT (F ) while #2UNSAT (F ) is calculated by applying the recurrences (3) and (4). Remember that the hard cases to answer whether #SAT (F )=k, is when k > p(n) for

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