Decision Procedures for Presburger Arithmetic

1. Decision Procedures forPresburger Arithmetic Presented by Constantinos Bartzis

2. Presburger formulas • numeral ::= 0 | 1 | 2… • var ::= x | y | z … • relop ::= < | ≤ | = |  | > • term ::= numeral | var | term + term | -term | numeral  term • formula ::= term relop term | formula formula | formula formula | formula | var. formula | var. formula numeral  term isn't really multiplication; it's short-hand for term + term + … + term

3. Decision Procedures • Will discuss algorithms for determining truth of formulas of Presburger arithmetic: • Fourier-Motzkin variable elimination (FMVE) • Omega Test • Cooper's algorithm • Automata based

4. Quantifier Elimination • All the methods we'll look at are quantifier eliminationprocedures. • If a formula with no free variables has no quantifiers, then it is easy to determine its truth value, e.g., 10 > 11  3+4 < 5  3 - 6 • Quantifier elimination works by taking input Pwith nquantifiers and turning it into equivalent formula P’ with mquantifiers, and where m < n. • So, eventually P P’ … Qand Qhas no quantifiers. • Qwill be trivially true or false

5. Normalization • Methods require input formulas to be normalized • e.g., collect coefficients, use only < and ≤ • Methods eliminate innermost existentialquantifiers. Universal quantifiers are normalized with (x. P(x))  (x. P(x)) • In FMVE, the sub-formula under the innermost existential quantifier must be a conjunction of relations. • This means the inner formula must be converted to disjunctive normal form(DNF): (c11c12 … c1n1)  ...  (cm1cm2 … cmnm)

6. Normalization (cont.) • The formula under  is in DNF. Next, the  must be moved inwards • First over disjuncts, using (x. P Q)  (x. P)  (x. Q) • Must then ensure every conjunct under the quantifier mentions the bound variable. Use (x. P(x) Q)  (x. P(x)) Q • For example: (x. 3 < x x +2y ≤ 6 y < 0)  (x. 3 < x x +2y ≤ 6) y < 0

7. Fourier-Motzkin theorems • The following simple facts are the basis for a very simple quantifier elimination procedure. • Over R (or Q), with a,b > 0: (x. c ≤ax bx ≤d) bc ≤ad (x. c < ax bx ≤d) bc < ad (x. c ≤ax bx < d) bc < ad (x. c < ax bx < d) bc < ad • In all four, the right hand side is implied by the left because of transitivity • e.g., (x < y y ≤z)  x < z

8. Fourier-Motzkin theorems (cont.) • For the other direction: (bc < ad)  (x.c < ax bx ≤d) take xto be d/b: c < a( d/b ), and b( d/b ) ≤d. • For (bc < ad)  (x.c < ax bx < d) take xto be (bc+ad)/2ab: c < a(bc+ad)/2ab  2bc < bc+ad bc < ad • Similarly for the other bound

9. Extending to a full procedure • So far: a quantifier elimination procedure for formulas where the scope of each quantifier is 1 upper bound and 1 lower bound. • We need to extend the method to cover cases with multiple constraints. • No lower bound, many upper bounds: (x: b1x < d1b2x < d2 … bnx < dn) True!(take min(di/bi) as a witness for x) • No upper bound, many lower bounds: obviously analogous.

10. Combining many constraints • Example: (x.c ≤ax b1x ≤d1b2x ≤d2) b1c ≤ad1b2c ≤ad2 • From left to right, the result just depends on transitivity. • From right to left, take xto be min(d1/b1, d2/b2). • In general, with many constraints, combine all possible lower-upper bound pairs. • Proof that this is possible is by induction on the number of constraints.

11. Combining many constraints • The core elimination formula is • With nconstraints initially, evenly divided between upper and lower bounds, this formula generates n2/4 new constraints.

12. FMVE example x. 20+x ≤ 0 y. 3y +x ≤ 10  20 ≤y - x (re-arrange) x. 20+x ≤ 0 y. 20+x ≤y  3y ≤ 10 - x (eliminate y) x. 20+x ≤ 0  60+3x ≤ 10 - x (re-arrange) x. 20+x ≤ 0  4x +50 ≤ 0 (normalize universal) x. 20+x ≤ 0  0 < 4x +50 (re-arrange)  x. -50 < 4x x ≤ -20 (eliminate x)  (-50 < -80)  T

13. Complexity • As before, when eliminating an existential over nconstraints we may introduce n2/4 new constraints. • With kquantifiers to eliminate, we might introduce as many as n2k/4kconstraints. • If dealing with alternating quantifiers, repeated conversions to DNF may become very costly.

14. Integers

15. Expressiveness over Integers • Can do divisibility by specific numerals: 2|e x. 2x = e for example: x. 0 < x < 30  (2|x  3|x  5|x) • Can do integer division and modulus, as long as divisor is constant. Use one of the following results (similar for division) P(x mod d)  q,r. (x = qd +r ) (0 ≤r < d d < r ≤ 0) P(r ) P(x mod d)  q,r. (x = qd +r )  (0 ≤r < d d < r ≤ 0) P(r ) • Any formula involving modulus or integer division by a constant can be translated to one without.

16. Expressivity over Integers • Any procedure for Z trivially can be extended to one for N (or any mixture of N and Z) too: Add extra constraints stating that variables are  0 • Relations < and ≤ can be converted into one another: x ≤y x < y +1 x < y x +1 ≤y • Decision procedures normalize to one of these relations.

17. Fourier-Motzkin for Integers? • Central theorem is false. E.g., (xZ. 3 ≤ 2x 2x ≤ 3)  6 ≤ 6 • But one direction still works (thanks to transitivity): (x. c ≤ax bx ≤d) bc ≤ad • We can compute consequences of existentially quantified formulas /

18. Fourier-Motzkin for Integers? • We know (x. c ≤ax bx ≤d) bc ≤ad • Thus an incomplete procedure for universal formulas over Z: • Compute negation: (x. P(x)) (x. P(x)) • Compute consequences: if (x. P(x)) then (x. P(x)) and (x. P(x))  T • Repeat for all quantified variables. • This is Phase 1 of the Omega Test

19. Omega Phase 1 - Example x,yZ. 0 < x y < x y +1 < 2x (normalize)  x,y. 1 ≤x y +1 ≤x  2x ≤y +1 x,y. 1 ≤x y +1 ≤x  2x ≤y +1 (eliminate y) x. 1 ≤x  2x ≤x (normalize) x. 1 ≤x x ≤ 0 (eliminate x)  1 ≤ 0 

20. Omega Phase 1 and the Interactive Theorem Provers • The Omega Test's Phase 1 is used by systems like Coq, HOL4, HOL Light and Isabelle to decide arithmetic problems. • Cons: • Incomplete • Conversion to DNF • Quadratic increase in numbers of constraints • Pros: • Easy to implement • Easy to adapt the procedures to create proofs that can be checked by other tools

21. Some Shadows • Given x. (i ci≤aix) (j bjx ≤dj) • The formula i,j bjci≤aidj is known as the real shadow. • If all of the aior all of the bjare equal to 1, then the real shadow is exact. • If the shadow is exact, then the two formulas are equivalent.

22. Exact Shadows • When a = 1 or b = 1, the core theorem (x. c ≤ax bx ≤d) bc ≤ad is valid because •  transitivity still holds •  take x = dif b = 1 or x = cif a = 1 • Omega Test's inventor, Bill Pugh, claims many problems in his domain (compiler optimization) have exact shadows. • Experience suggests the same is true in other domains too, such as hardware model checking. • When shadows are exact, we can pretend the problem is over R rather than Z and proceed as before.

23. Dark Shadows • The formula i,j (ai-1)(bj-1) ≤ aidj - bjci is known as the dark shadow. • If all aior all bjare one, then this is the same as the real shadow (or exact). • The real shadow provides a test for unsatisfiability. • The dark shadow tests for satisfiability, because (a-1)(b-1) ≤ad - bc  (x. c ≤ax bx ≤d) • This is the Phase 2 of the Omega Test

24. Omega Test Phases 1 & 2 • Problem is x. P(x) • If input is exact for one or more elements of x, then eliminate them x. P(x) x’. P’(x’) • Otherwise, calculate real shadow R: x. P(x) R so, if R , then input formula is . • Otherwise, calculate dark shadow D: D x. P(x) so, if D = T, then input formula is T.

25. Omega Phase 2 - Example (a-1)(b-1) ≤ad - bc  (x. c ≤ax bx ≤d) x,y. 3x +2y ≤ 18  3y ≤ 4x  3x ≤ 2y +1 3y ≤ 4x 3x ≤ 2y +1 3y ≤ 4x 3x ≤ 18 - 2y 6 ≤ 8y + 4 - 9y 6 ≤ 72 - 8y - 9y y ≤ -2 17y ≤ 66 y ≤ 3 • This gives a suitable value for y, and by back-substitution, finds x = -1, y = -2 as a possible solution.

26. Splinters • Purely existential formulas are often proved false by their real shadow; or proved true by their dark shadow • But in “rare” cases, the main theorem is needed. Let mbe the maximum of all the djs. Then splinter dark shadow

27. Splinters • A splinter doesrepresent a smaller problem than the original because the extra equality allows xto be eliminated immediately. • When quantifiers alternate, and there is no exact shadow, the main theorem is used as an equivalence, and splinters can't be avoided. • Splinters must also be checked if neither real nor dark shadows decide an input formula.

28. Eliminating Equalities • In an expression x. … cx = e  … the existential can be eliminated. • First, multiply all terms involving xso that they have a common coefficient. • Formula becomes x. …c’x … c’x = e’  …c’x… • This is equivalent to …e’… c’|e’ …e’…

29. Eliminating Divisibilities x. … c | dx + e  … • Note: d < c(otherwise, replace d with d mod c). • Introduce temporary new existential variable: x,y. … cy = dx + e  … • Rearrange: x,y. … dx = cy -e  … • Use equality elimination to derive y. … d | cy -e  … • Because d < c, this process must terminate with elimination of divisibility term.

30. Implementation - Normalization • Omega Test's main disadvantage is that it requires the matrix of the formula to be in DNF • Consider x. (x  10 x  11  9 < x ≤ 12) x = 12 • Negate, remove , <: x. (x ≤ 9  11 ≤x)  (x ≤ 10  12 ≤x)  10 ≤x x ≤ 12  (x ≤ 11  13 ≤x) • Evaluate 8 (= 23) DNF terms. • Clever preparation of input formulas can make orders of magnitude difference

31. Implementation - Normalization • The propositional tautology (p (q q’))  (p q p q’) justifies the following procedure: • If Pis an atomic formula, then when processing P Q, assume Pis true while processing Q: • If a sub-formula Q0 of Qis such that P Q0, then replace Q0 in Qby T. • If a sub-formula Q0 of Qis such that P Q0, then replace Q0 in Qby . • Similarly, (p (q q’))  (p q p q’) for disjunctions.

32. Example • Over  : 0 ≤x + y + 4  (0 ≤x + y + 6  0 ≤ 2x + 3y + 6) is equivalent to 0 ≤x + y + 4 • Whereas 0 ≤x + y + 4  0 ≤ -x -y -6  0 ≤ 2x + 3y + 6 is equivalent to  • Over  : 0 ≤x + y + 4  0 ≤x + y + 1  0 ≤ 2x + 3y + 6 is equivalent to 0 ≤x + y + 4  0 ≤ 2x + 3y + 6

33. Cooper’s Algorithm

34. Cooper's Algorithm • Cooper's algorithm is a decision procedure for Presburger arithmetic. • A non-Fourier-Motzkin alternative • It is also a quantifier elimination procedure, which also works from the inside out, eliminating existentials. • Its advantage is that it doesn't need to normalize input formulas to DNF. • Description is of simplest possible implementation; many tweaks are possible.

35. Preprocessing • To eliminate the quantifier in x. P(x): • Normalize so that only operators are <, and divisibility (c|e), and negations only occur around divisibility leaves. • Compute least common multiplec of all coefficients of x, and multiply all terms by appropriate numbers so that in every term the coefficient of x is c. • Now apply (x. P(cx))  (x. P(x) c|x).

36. Preprocessing Example x,y  Z. 0 < y x < y x +1 < 2y (normalize) x,y. 0 < y x < y  2y < x +2 (transform y to 2y everywhere)  x,y. 0 < 2y  2x < 2y  2y < x +2 (give y unit coefficient)  x,y. 0 < y  2x < y y < x +2  2|y

37. Two ways • How might x. P(x) be true? • Either: • there is a least xmaking Ptrue; or • there is no least x: however small you go, there will be a smaller xthat still makes Ptrue • Construct two formulas corresponding to both cases.

38. Case 1:Infinitely many small solutions • Look at the atomic formulas in P, and think about their values when xhas been made arbitrarily small: • x < e: if xbecomes small enough, this will be T • e < x: if xbecomes small enough, this will be • c | x+e: unchanged • This constructs P-, a formula where xonly occurs in divisibility terms. • Let  be the l.c.m.of the constants involved in divisibility terms. We just need to test P- on 1,…, .

39. P- example • For y. 0 < y  2x < y y < x +2  2|y • 0 < ywill become  as ygets small • 2x < yalso becomes  as ygets small • y < x +2 will be T as ygets small • 2|ydoesn't change (it tests if yis even or not) • So in this case, P- (y)  ( T  2|y) 

40. Case 2: Least solution exists • The case when there is a least xsatisfying P(x). • For there to be a least xsatisfying P(x), it must be the case that one of the terms e < xis T, and that if xwas any smaller the formula would become . • Let B = {b | b < x is a term of P(x)} • Need just consider P(b+j), where b Band 1 ≤j ≤. • Final elimination formula is:

41. Example continued • For y. 0 < y  2x < y y < x +2  2|y • least solutions, if they exist, will be at y = 1, y = 2, y = 2x +1, or y = 2x +2 • The divisibility constraint eliminates two of these. • Original formula is equivalent to: (2x < 2  0 < x)  (0 < 2x +2 x < 0) Which is unsatisfiable.

42. Automata based

43. 0 1 1 0,0,1 0 0 1 0,1,1 0 1 1 0 Symbolic Representation We use finite automata to represent the integer solutions (in binary) of atomic linear constraints. Example: The constraint x1x20 has solutions: (0,0), (1,0), (1,1), (2,0), (2,1), (2,2), (3,0), … The corresponding automaton

44. 0 1 2 FA Construction • Consider a finite state transducer that computes linear integer expressions 0 1 0 0 / / 0 1 0 1 / 1 1 0 / 0 0 1 1 1 / / 0 1 Example x + 2y 0 1 / 0 1 1 / 1 1 1 / 0 010 + 2  001 0 0 / 1 0 1 0 0 / / 0 1 1 0 0

45. 0 1 -1 Equality with 0 0 1 0 0 / / 0 1 0 1 / 1 1 0 / 0 0 1 1 1 / / 0 1 • Remove transitions that write 1. • Make state 0 accepting • states 0 1 / 0 1 1 / 1 1 1 / 0 0 0 / 0 0 1 0 0 / / 0 1

46. 0 1 -1 Inequality (<0) • All transitions remain • States with negative carries become accepting • Same size 0 1 0 0 / / 0 1 0 1 / 1 1 0 / 0 0 1 1 1 / / 0 1 0 1 / 0 1 1 / 1 1 1 / 0 0 0 / 0 0 1 0 0 / / 0 1

47. Non-zero Constant Term c • Same as before but now -c is the initial state • If there is no such state, create one (and possibly some intermediate states) • Size 0 1 0 0 / / 0 1 0 1 / 1 1 0 / 0 0 1 1 1 / / 0 1 0 1 / 0 1 1 / 1 1 1 / 0 0 1 -1 0 0 / 0 0 1 0 0 / / 0 1

48. Boolean Connectives • For  compute the intersection • For  compute the union • For compute the complement

49. Conjunction example 0 0 1 0,1,1 0 1 0,1 1 0 Automaton for x-y<1 1 0 1 0 -1 0 1 0 1 0 0 1 0,1,1 0 0 0,1 0 0 0,1 0,-1 0 0 1 1 1 0 1 1 1 0,1 0 1 Automaton for x-y<1  2x-y>0 1 0 0 1 0,1 Automaton for 2x-y>0 0 1 0,1 0 1 0,1 -1,-1 -1,0 -1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 1 1,1 0 1 1,1 1 0 0 1 1 1,0,1 -2,-1 -2,0 -2,1 -2 0 1 1 0 1 1

50. Existential Quantifier Elimination • To eliminate x, remove the track of x • The resulting FA is in general non-deterministic • Determinization may cause exponential blowup • Rarely occurs in practice 1 0 0 1 0,1 0 0 1 0,1,1 1 0 1 0 x . x-y<1 1 0 -1 0 0 1 0,1,1 0 1