1. Phase Transitions of PP-Complete Satisfiability Problems D. Bailey, V. Dalmau, Ph.G. Kolaitis Computer Science Department UC Santa Cruz

2. Phase Transitions A phase transitionis an abrupt change in the behavior of a property of a “system”. A phase transition is an abrupt change in the behavior of a property of a “system”. Extensive study of phase transitions in physics (statistical mechanics). Extensive study of phase transitions in physics (statistical mechanics). Extensive study of phase transitions in the behavior of asymptotic probabilities of NP- complete problems during the past decade. Extensive study of phase transitions in the behavior of asymptotic probabilities of NP- complete problems during the past decade.

3. Motivation and Goals Understand the “structure” of NP-complete complete problems. Understand the “structure” of NP-complete complete problems. Relate phase transitions to the average-case performance of particular algorithms for NP-complete problems. Relate phase transitions to the average-case performance of particular algorithms for NP-complete problems. Ultimately, understand the average-case complexity of NP-complete problems. Ultimately, understand the average-case complexity of NP-complete problems.

4. Boolean Formulas Literal: x,  x, with x a Boolean variable Literal: x,  x, with x a Boolean variable Clause: disjunction of literals Clause: disjunction of literals k-Clause: disjunction of k literals k-Clause: disjunction of k literals k-CNF formula: conjunction of k-clauses k-CNF formula: conjunction of k-clauses Example: 3-CNF formula Example: 3-CNF formula (x   y  z )  (  z  w   u)  (x  y  z)  (  x   y   u)  (  x   y   u)

5. NP-Completeness of kSAT kSAT is the following decision problem: given a k-CNF formula , is it satisfiable? (i.e., is there an assignment of values 0/1 to the variables, so that  evaluates to 1). kSAT is the following decision problem: given a k-CNF formula , is it satisfiable? (i.e., is there an assignment of values 0/1 to the variables, so that  evaluates to 1). Theorem: (S. Cook – 1971) Theorem: (S. Cook – 1971) kSAT is NP-complete, for every k  3. kSAT is NP-complete, for every k  3. In particular, 3SAT is NP-complete. In particular, 3SAT is NP-complete.

6. Random Boolean Formulas F k (n,r): space of all k-CNF formulas with n variables and r  n clauses. F k (n,r): space of all k-CNF formulas with n variables and r  n clauses. Random k-CNF formula: r  n clauses of length k are picked independently & with replacement. Random k-CNF formula: r  n clauses of length k are picked independently & with replacement. pr(k,n,r): probability that a random k-CNF formula is satisfiable. pr(k,n,r): probability that a random k-CNF formula is satisfiable. Fixed clauses-to-variables ratio model introduced and studied first by Franco et al. in the 1980s. Fixed clauses-to-variables ratio model introduced and studied first by Franco et al. in the 1980s.

7. Phase Transitions for k-SAT Ratio r of clauses-to-variables measures the “constrainedness” of a k-CNF formula. Ratio r of clauses-to-variables measures the “constrainedness” of a k-CNF formula. (Chvatal and Reed – 1992): For every k > 1, there is a ratio r*(k) s.t. Conjecture (Chvatal and Reed – 1992): For every k > 1, there is a ratio r*(k) s.t. –If r < r*(k), then pr(k,n,r)  1; –If r > r*(k), then pr(k,n,r)  0.

8. Phase Transitions of kSAT Theorem: 2SAT has a phase transition at r*(2)=1. Theorem: 2SAT has a phase transition at r*(2)=1. Chvatal and Reed (1992), Goerdt (1996) Chvatal and Reed (1992), Goerdt (1996) Open Problem: Phase transition of kSAT, k > 2. Open Problem: Phase transition of kSAT, k > 2. Current state of affairs: Current state of affairs: –Analytical upper and lower bounds for the value of r*(k), if it exists. the value of r*(k), if it exists. –Experimental results providing evidence that r*(k) exists and estimates for its value. that r*(k) exists and estimates for its value.

9. Phase Transition of 3SAT Lower and Upper Bounds for r*(3) Lower and Upper Bounds for r*(3) 3.26 < r*(3) < 4.571 3.26 < r*(3) < 4.571 Lower: Achlioptas & Sorkin (2000); Lower: Achlioptas & Sorkin (2000); Upper: Kaporis, Kirousis, Stamatiou, Vamvakari, Upper: Kaporis, Kirousis, Stamatiou, Vamvakari, Zito (2001). Zito (2001). Experimental estimation of r*(3) Experimental estimation of r*(3) r*(3)  4.3 r*(3)  4.3 Mitchell, Selman and Levesque (1992). Mitchell, Selman and Levesque (1992).

10. Phase Transition and Hardness Davis-Putnam (DP) Procedure for SAT Davis-Putnam (DP) Procedure for SAT Unit Propagation + Splitting Rule Unit Propagation + Splitting Rule Mitchell, Levesque and Selman (1992): Mitchell, Levesque and Selman (1992): The average number of recursive calls The average number of recursive calls in the DP-Procedure for 3SAT peaks in the DP-Procedure for 3SAT peaks near the critical ratio 4.3 near the critical ratio 4.3

15. Phase Transition Phenomena Observed in other NP-complete problems Observed in other NP-complete problems Experimental Results for Experimental Results for -- Graph Coloring Problems -- Graph Coloring Problems -- Constraint Satisfaction Problems -- Constraint Satisfaction Problems -- Number Partitioning -- Number Partitioning Provable Phase Transition at 2/k(k-1) for Provable Phase Transition at 2/k(k-1) for -- 1-in-kSAT Achlioptas et al. (2001) -- 1-in-kSAT Achlioptas et al. (2001)

16. Phase Transition Phenomena Observed experimentally in problems that are complete for higher complexity classes. Observed experimentally in problems that are complete for higher complexity classes. In particular, PSPACE-complete problems: In particular, PSPACE-complete problems: -- QBF: Cadoli et al. (1997), Gent and Walsh (1999) Gent and Walsh (1999) -- SSAT: Littman (1999), Littman, Majercik, Pitassi (2000) Littman, Majercik, Pitassi (2000)

17. Hardness of Random #3SAT Birnbaum and Lozinskii (1999) Birnbaum and Lozinskii (1999) Counting Davis-Putnam Procedure (CDP) Counting Davis-Putnam Procedure (CDP) The average number of recursive calls The average number of recursive calls for #3SAT peaks near the ratio 1.2 for #3SAT peaks near the ratio 1.2 Bayardo and Pehoushek (2000) Bayardo and Pehoushek (2000) CDP with connected components CDP with connected components The average number of recursive calls The average number of recursive calls for #3SAT peaks near the ratio 1.5 for #3SAT peaks near the ratio 1.5

18. Counting vs. Decision Problems #3SAT is a function problem, not a decision problem. #3SAT is a function problem, not a decision problem. Thus, is not meaningful to relate the peak in average running time of algorithms for #3SAT Thus, is not meaningful to relate the peak in average running time of algorithms for #3SAT to a structural phase transition of the asymptotic probability of #3SAT. to a structural phase transition of the asymptotic probability of #3SAT. However, there is a class of decision problems that “captures” the complexity of #P. However, there is a class of decision problems that “captures” the complexity of #P.

19. PP: Probabilistic NP PP: there is a polynomial time NTM such that an input is accepted iff at least half of the computations are accepting. Simon (1975), Gill (1977). PP: there is a polynomial time NTM such that an input is accepted iff at least half of the computations are accepting. Simon (1975), Gill (1977). Prototypical PP-complete problem: MAJSAT: given a CNF formula , is it satisfied by at least half of the possible truth assignments? Prototypical PP-complete problem: MAJSAT: given a CNF formula , is it satisfied by at least half of the possible truth assignments?

20. PP vs. Other Complexity Classes PP contains both NP and coNP. PP contains both NP and coNP. PP is contained in PSPACE. PP is contained in PSPACE. P PP = P # P (Angluin -- 1980) P PP = P # P (Angluin -- 1980) PP “captures” the complexity of counting PP “captures” the complexity of counting PH  P #P (Toda -- 1990) PH  P #P (Toda -- 1990) PP is considered to be highly intractable. PP is considered to be highly intractable.

21. Phase Transitions in PP Problems Study PP-complete satisfiability problems under the fixed clauses-to-variables model. Study PP-complete satisfiability problems under the fixed clauses-to-variables model. First natural choice to study: MAJ 3SAT First natural choice to study: MAJ 3SAT

22. Phase Transitions in PP Problems However, However, MAJ 3SAT is not known to be PP- complete. MAJ 3SAT is not known to be PP- complete. MAJ 3SAT has no phase transition: MAJ 3SAT has no phase transition: for every r, almost all random 3CNF formulas are satisfied by less than half for every r, almost all random 3CNF formulas are satisfied by less than half of all possible truth assignments. of all possible truth assignments.

23. Square Root 3SAT Square Root 3SAT - #3SAT(  2 n/2 ): given a 3-CNF formula, is it satisfied by at least 2 n/2 truth assignments? Square Root 3SAT - #3SAT(  2 n/2 ): given a 3-CNF formula , is it satisfied by at least 2 n/2 truth assignments? Intuitively, #3SAT(  2 n/2 ) asks whether at least one of the first n/2 bits of the number of satisfying truth assignments is equal to 1. Intuitively, #3SAT(  2 n/2 ) asks whether at least one of the first n/2 bits of the number of satisfying truth assignments is equal to 1.

24. Square Root 3SAT Square Root 3SAT - #3SAT(  2 n/2 ): given a 3-CNF formula , is it satisfied by at least 2 n/2 truth assignments? Square Root 3SAT - #3SAT(  2 n/2 ): given a 3-CNF formula , is it satisfied by at least 2 n/2 truth assignments? Intuitively, #3SAT(  2 n/2 ) asks whether at least one of the first n/2 bits of the number of satisfying truth assignments is equal to 1. Intuitively, #3SAT(  2 n/2 ) asks whether at least one of the first n/2 bits of the number of satisfying truth assignments is equal to 1. Theorem: #3SAT(  2 n/2 ) is PP-complete. Theorem: #3SAT(  2 n/2 ) is PP-complete.

25. Phase Transition Conjecture F 3 (n,r): space of all 3-CNF formulas  with n variables and r  n clauses. F 3 (n,r): space of all 3-CNF formulas  with n variables and r  n clauses. X: random variable on F 3 (n,r) such that X  = number of satisfying assignments of . X: random variable on F 3 (n,r) such that X  = number of satisfying assignments of . Conjecture: There is a ratio r* such that Conjecture: There is a ratio r* such that – If r < r*, then Pr[ X  2 n/2 ]  1; – If r > r*, then Pr[ X  2 n/2 ]  0.

26. Evidence for the Phase Transition Analytical results that yield upper and lower bounds for r*. Analytical results that yield upper and lower bounds for r*. Experimental results suggesting that r*  2.5 Experimental results suggesting that r*  2.5

27. Upper and Lower Bounds for r* Theorem: 0.9227  r*  2.595 Theorem: 0.9227  r*  2.595 Hint of Proof: Hint of Proof: –Upper Bound: Markov’s inequality. –Lower Bound: -- Covering partial assignments. -- Covering partial assignments. -- Achlioptas’ differential equations -- Achlioptas’ differential equations technique. technique.

28. Upper Bound for r* From Markov’s inequality, From Markov’s inequality, Pr[ X  2 n/2 ]  E(X)/ 2 n/2 Pr[ X  2 n/2 ]  E(X)/ 2 n/2 E(X) = 2 n (7/8) r  n E(X) = 2 n (7/8) r  n Pr[ X  2 n/2 ]  2 n/2 (7/8) r  n Pr[ X  2 n/2 ]  2 n/2 (7/8) r  n If 2 1/2 (7/8) r  1, then Pr [ X  2 n/2 ]  0. If 2 1/2 (7/8) r  1, then Pr [ X  2 n/2 ]  0. So, if r  2.595, then Pr [ X  2 n/2 ]  0. So, if r  2.595, then Pr [ X  2 n/2 ]  0.

29. An Approach to Lower Bounds Show that, if r is small enough, then a random formula  (x 1,…,x n ) has  2 n/2 satisfying assignments by finding a partial assignment with n/2 variables Show that, if r is small enough, then a random formula  (x 1,…,x n ) has  2 n/2 satisfying assignments by finding a partial assignment with  n/2 variables covering . covering . Covering means: the partial assignment makes the formula true, regardless of the truth value of the un-assigned variables. Covering means: the partial assignment makes the formula true, regardless of the truth value of the un-assigned variables.

30. Finding Covering Partial Assignments To show r* , use a known lower bound for r*(3) to derive the existence of a To show r* , use a known lower bound for r*(3) to derive the existence of a covering assignment for a random . covering assignment for a random . To show r*  use Achlioptas’ technique to analyze a simple randomized algorithm, called the Extended Unit Clause. To show r*  use Achlioptas’ technique to analyze a simple randomized algorithm, called the Extended Unit Clause.

31. 1/2 Lower Bound for r* Random 3-CNF  with m clauses, n variables, and such that m/n  1/2. Random 3-CNF  with m clauses, n variables, and such that m/n  1/2.  has at least one satisfying assignment  by lower bound for r*(3).  has at least one satisfying assignment  by lower bound for r*(3). Build partial assignment  with  n/2 variables that covers   for each of Build partial assignment  with  n/2 variables that covers   for each of the  n/2 clauses in , choose some literal in the clause that is true under  and set  to also make that literal true. the  n/2 clauses in , choose some literal in the clause that is true under  and set  to also make that literal true.

32. Experimental Results Implemented a threshold version of Birnbaum & Lozinskii’s CDP algorithm. Implemented a threshold version of Birnbaum & Lozinskii’s CDP algorithm. Experiments on random 3CNF-formulas with n = 10, 20, 30, 40, and 50 variables. Experiments on random 3CNF-formulas with n = 10, 20, 30, 40, and 50 variables. Probability curves cross at r  2.5 Probability curves cross at r  2.5 The average number of recursive calls peaks near 2.5 The average number of recursive calls peaks near 2.5

33. Birnbaum & Lozinskii’s CDP recursive function CDP( ,  n) recursive function CDP( ,  n) if  is empty, return 2 n if  is empty, return 2 n if  contains an empty clause, return 0 if  contains an empty clause, return 0 if  contains unit clause {t}, return CDP(  ,n-1), where if  contains unit clause {t}, return CDP(  ,n-1), where –   contains all clauses in  that do not contain t ; –the literal  t is removed if present.

34. CDP (continued) otherwise choose any variable x in  return CDP(  ,n-1) + CDP(  ,n-1), where otherwise choose any variable x in  return CDP(  ,n-1) + CDP(  ,n-1), where –   contains all clauses in  that do not contain x, with the literal  x removed if present. –   contains all clauses in  that do not contain  x, with the literal x removed if present.

35. Threshold CDP Accumulate partial counts in recursive calls of CDP. Accumulate partial counts in recursive calls of CDP. Return “yes”, when accumulated count equals or exceeds threshold. Return “yes”, when accumulated count equals or exceeds threshold. Return “no”, otherwise. Return “no”, otherwise. Can also use upper bound tracking to terminate and return “no” earlier. Can also use upper bound tracking to terminate and return “no” earlier.

38. Summary Evidence for a phase transition in a natural PP-complete satisfiability problem. Evidence for a phase transition in a natural PP-complete satisfiability problem. Analytical upper bound 2.595 obtained via Markov’s inequality is quite close to the value 2.5 of the critical ratio suggested by the experiments. Analytical upper bound 2.595 obtained via Markov’s inequality is quite close to the value 2.5 of the critical ratio suggested by the experiments. Open Problem: Obtain tighter upper and Open Problem: Obtain tighter upper and lower bounds. lower bounds.

39. Work in Progress Investigation of phase transitions of Investigation of phase transitions of #3SAT(  2  n ), 0 <  < 1. #3SAT(  2  n ), 0 <  < 1. Experimental Findings: Experimental Findings: -- Each such problem has a phase transition. -- Each such problem has a phase transition. However, However, -- The peak in the average number of recursive calls does not always occur at the critical ratio. -- The peak in the average number of recursive calls does not always occur at the critical ratio.

44. The Bigger Challenge Develop a theory of phase transitions Develop a theory of phase transitions of algorithmic problems. of algorithmic problems. Are there structural properties that imply Are there structural properties that imply the existence of phase transitions? the existence of phase transitions? Is there a descriptive complexity theory Is there a descriptive complexity theory of phase transitions? of phase transitions?