Leveraging
linear programming for
pseudo-Boolean solving

 

Jo Devriendt †, Jan Elffers †, Ambros Gleixner ‡, Jakob Nordström *
† KTH Royal Institute of Technology, Sweden

‡ Zuse Institut Berlin, Germany

* University of Copenhagen, Denmark

jhmde@kth.se

Pseudo-Boolean background

  • Pseudo-Boolean (PB) constraint:
    • Bounded weighted sum of literals:

       
    • Note:
    • Equivalent to 0-1 integer linear programming constraint
  • PB formula: conjunction of PB constraints
  • PB solvers decide satisfiability of PB formula
  • PB formula can be rationally infeasible
    • no assignment to interval [0,1] satisfies the formula, e.g.:


       
  • Rationally infeasible implies UNSAT
x+y+z \geq 2 \\ \overline{x}+\overline{y}+\overline{z} \geq 2
x+2\overline{y}+3z+4\overline{w} \geq 5
\overline{x} = 1-x

SAT background

  • A clause is a special PB constraint:

     
  • CNF formula can be rationally infeasible
    • But only if unit propagation leads to conflict
  • Otherwise, trivial rational solution: assign 0.5 to all non-propagated variables
    • All clauses not satisfied by unit propagation have at least 2 unassigned literals
    • Such clauses are satisfied by 0.5-assignment

For CNF, deciding rational infeasibility is trivial

x+\overline{y}+z+\overline{w} \geq 1

Pseudo-Boolean background

  • Deciding rational infeasibility of PB formulas is easy in theory:
    • Algorithmic complexity class: P [K1979]
    • Proof-theoretic complexity: short cutting plane [CCT87] proofs exist [F1902]
  • Linear programming (LP) solvers efficiently decide rational feasibility
  • In practice, many PB solvers struggle on rationally infeasible formulas [EGNV18]
    • Even PB solvers that natively build cutting plane proofs, e.g.,  RoundingSat and Sat4J

Goal of our work:
use LP solver to check rational feasibility during PB search

Linear Programming (LP) solver

  • Input:
    • conjunction of linear constraints
    • variable bounds
    • objective function
  • Output: either
    • SAT: optimal rational solution
    • UNSAT: Farkas multipliers
      • defines violated positive linear combination of input constraints

PB search loop

Propagation

Conflict?

Learn PB constraint

Backjump

Decide unassigned variable

yes

no

PB search loop

with LP solver call

Propagation

Conflict?

Backjump

Decide unassigned variable

no

Rational infeasibility?

Query LP solver

yes

no

Learn PB constraint

Extract Farkas multipliers

yes

Learn Farkas constraint

  • LP solvers are slow compared to PB search loop
    • Limit calls to LP solver
    • Limit LP solver running time
    • Deterministic measure: compare #conflicts in PB solver to #pivots in LP solver
  • Learned constraint must be implied by input formula
    • LP solver uses inexact floating point arithmetic
    • Recalculate Farkas constraint with exact arithmetic
    • Verify Farkas constraint is still conflicting

Two technical hurdles

  • PB solver RoundingSat [EN18]
    • Native cutting plane proofs
    • Performed well in past PB competitions
  • LP solver SoPlex [ZIB]
    • SCIP's native LP solver
    • Fast
    • Open source

Working implementation

Experiments!

  • 5 solver configurations
    • RoundingSat
    • RoundingSat+SoPlex
    • SCIP
    • Sat4J
    • Sat4J-CP
  • 3000s on 16GiB machines
  • 4 benchmark families:
    • PB12
    • PB16
    • MIPLIB
    • PROOF

Performance experiment

Performance experiment

Performance experiment

Performance experiment

  • RoundingSat+SoPlex never really worse than RoundingSat
    • small LP overhead at worst, huge speedups at best
    • Not only more solved UNSAT instances (+16%), but also more solved SAT instances (+14%)
  • RoundingSat+SoPlex and SCIP trade places
  • SoPlex does not like PB12

Performance experiment

Conflict depth experiment

Conflict depth experiment

  • Conflict depth for rational infeasibility check and unit propagation are similar
  • Technique detects rational infeasibility also in deep search nodes

Learned constraint addition experiment

Learned constraint addition experiment

  • Adding learned constraints to LP solver does not lead to more solved instances
    • Also no improvement in number of conflicts needed
  • Hypothesis 1: no objective function to guide "tight" variant
  • Hypothesis 2: rational solution at deep search nodes is not useful
  • Other hypotheses?

Conclusion

  • Use LP solver to tackle rational infeasibility during search
  • Implemented sound integration of LP solver in PB solver
  • Experiments indicate small LP overhead at worst, huge speedups at best

Thanks for your attention! Questions?

Future Current work

  • Optimization
  • LP cut generation

[K1979] A polynomial algorithm for linear programming - 1979 - Khachiyan

[CCT87] On the Complexity of Cutting-Plane Proofs - Cook, Coullard, Turán

[F1902]  Über die Theorie der Einfachen Ungleichungen - 1902 - Farkas

[EGNV18] Using combinatorial benchmarks to probe the reasoning power of pseudo-Boolean solvers - 2018 - Elffers, Giráldez-Cru, Nordström, Vinyals

[EN18] Divide and conquer: Towards faster pseudo-boolean solving - 2018 - Elffers, Nordström

[ZIB] SoPlex - soplex.zib.de

References

Leveraging Linear Programming for pseudo-Boolean solving - PoS 19

By Jo Devriendt

Leveraging Linear Programming for pseudo-Boolean solving - PoS 19

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