pith:6RWM4CRE
Polynomial Invariant Generation for Floating-Point Programs
A framework combines round-off error analysis with polynomial constraint solving to generate tight invariants for floating-point programs.
arxiv:2507.15017 v4 · 2025-07-20 · cs.PL
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\usepackage{pith}
\pithnumber{6RWM4CREIAGOOO32TBWUTJ2Z2V}
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Record completeness
Claims
Our contribution is a novel framework for applying polynomial constraint solving to address the invariant generation problem, which is also the first polynomial constraint solving based approach that handles floating-point errors to our best knowledge.
The combination of round-off error analysis and polynomial constraint solving can circumvent the cost of handling a large number of error variables in the floating-point model without losing tightness of the generated invariants.
A framework merges round-off error analysis and polynomial constraint solving to generate tight invariants for floating-point programs and outperforms prior methods on benchmarks.
Receipt and verification
| First computed | 2026-06-03T01:05:05.163142Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
f46cce0a24400ce73b7a986d49a759d5459ff2fe1bb6e57c99a453beaf183f17
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/6RWM4CREIAGOOO32TBWUTJ2Z2V \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f46cce0a24400ce73b7a986d49a759d5459ff2fe1bb6e57c99a453beaf183f17
Canonical record JSON
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