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Certifying Bounds Propagation for Integer Multiplication Constraints

AAAI 2025 · Learning theory and algorithms › Combinatorial and search optimization · approach: Structure and complexity of constraint reasoning · 0 citations (OpenAlex)

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Extends pseudo-Boolean (VeriPB) proof logging to bounds-consistent propagation of integer multiplication and division constraints in constraint programming solvers, with a bespoke justification procedure.

Problem

Certifying (proof-logging) bounds-consistent propagation for ternary integer multiplication constraints in CP solvers, using a linear pseudo-Boolean proof system that only handles 0-1 variables.

Approach

Encodes CP integer variables as binary bit vectors with sign-bit/absolute-value channelling and derives bounds justifications for multiplication via cutting planes, RUP, and proof by contradiction, with step counts proportional to the product of bit-widths, instead of brute-force tabulation.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use bounds-consistent justifications instead of auto-tabulation proofsGCS multiplication constraint proof logging · Synthetic multiplication instancessolve time with proof loggingHelped
Use bounds-consistent justifications instead of auto-tabulation proofsGCS multiplication constraint proof logging · Synthetic multiplication instancesVeriPB verification timeHelped
Use bounds-consistent justifications instead of auto-tabulation proofsGCS multiplication constraint proof logging · n-fractions (n=2)proof size: 428MB; >2TBHelped

Surprising findings

Limitations

Experimental setup

Datasets: synthetic ternary multiplication instances (1000, domains within [-200..200]) n-fractions problem from CSPLib (n=2)

Benchmarks: synthetic single-multiplication CSP instances n-fractions (CSPLib, n=2)

Metrics: solve time with proof logging VeriPB verification time proof file size

Baselines: auto-tabulation proof logging

Builds on: cutting planes proof system VeriPB pseudo-Boolean proof logging for CP (Gocht, McCreesh, Nordström 2022) Demirović et al. 2024 (RUP-based reification of derivations)

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