Solver-admitted metamorphic testing of learned network optimization surrogates; arXiv study examines equivalence-encoding differences, numerical error, and constraint checks for audit eligibility
Read the original at arxiv.org→arXiv:2611.08837v1 Announce Type: new Abstract: Equivalent encodings of an optimization problem can produce different learned predictions. Interpreting these differences requires accounting for numerical error and...
Original headline: "Solver-Admitted Metamorphic Testing of Learned Network Optimization Surrogates"
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- Nov 9, 05:00 UTC arXiv cs.AI lead source Solver-Admitted Metamorphic Testing of Learned Network Optimization Surrogates