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CounterexampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedprecheck passaudited 2026-08-29
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A tiny residual does not force a small forward error

Statement refuted

If the residual bAx^ of an approximate solution is tiny compared with b, then the forward error xx^ is tiny compared with x.

Facts & Assumptions

Given: A parameter ε with 0<ε<1, the matrix A=diag(1,ε), the right-hand side b=(1,ε), the exact solution x=A1b=(1,1), the approximation x^=(1,0), the residual r=bAx^, and the error e=xx^.

[L1]

For r=Ae and e=A1r the residual and the error satisfy e2A12r2 and r2A2e2 (Residual bounds on forward error for a nonsingular linear system).

[L2]

κ2(A)=A2A12=1/ε (The condition number kappa_p(A) = ||A||_p ||A^{-1}||_p of a nonsingular linear system).

Counterexample

technique · direct
1.1

The residual is r=bAx^=(1,ε)(1,0)=(0,ε), so r2=ε, while b2=1+ε2; the relative residual is ε/1+ε2, which tends to 0 as ε0+.

algebra
1.2

The error is e=xx^=(0,1), so e2=1 and the relative forward error is 1/2, independent of ε.

algebra
2.1

Steps 1.1 and 1.2 contradict the claimed implication: the relative residual can be made arbitrarily small while the relative forward error stays at 1/2.

step 1.1step 1.2
2.2

The bounds of [L1] are consistent with the witness and explain it: e2=1=(1/ε)ε=A12r2 attains the first bound exactly, and the condition number 1/ε of [L2] is what converts the tiny residual ε into order-one forward error.

L1L2step 1.1step 1.2algebra
3.1

Steps 2.1 and 2.2 refute the false statement: a tiny residual guarantees only a tiny forward error up to the condition number, not by itself.

step 2.1step 2.2

Depends on

Used by

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Dependency tree · two levels

6 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources