Equation 7 · Mathematics, Proof, and Scientific Computation in 2035: Scenarios, Signals, and Falsifiable Predictions
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If is large, small errors in the input data or in floating-point rounding can produce large errors in the computed solution, independent of which algorithm is used to solve the system — a property of the problem, not the solver. Bringing formal verification to numerical software means proving that an implementation’s actual rounding behavior stays within a bound consistent with this kind of analysis, not just testing it empirically on sample inputs. That is a much harder and more labor-intensive proof target than a compiler’s semantic-preservation proof, because floating-point arithmetic is non-associative and the correctness statement has to quantify over rounding error explicitly…
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If is large, small errors in the input data or in floating-point rounding can produce large errors in the computed solution, independent of which algorithm is used to solve the system — a property of the problem, not the solver. Bringing formal verification to numerical software means proving that an implementation’s actual rounding behavior stays within a bound consistent with this kind of analysis, not just testing it empirically on sample inputs. That is a much harder and more labor-intensive proof target than a compiler’s semantic-preservation proof, because floating-point arithmetic is non-associative and the correctness statement has to quantify over rounding error explicitly rather than treating arithmetic as exact — which is precisely why, as of 2026, formally verified numerical libraries remain rare compared to verified compilers and kernels.
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