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TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck passaudited 2026-08-29
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Right-hand-side perturbations in Ax = b are amplified by the condition number

Statement

Let n≥1, let p∈Q with p≥1, let A∈GL⁡n(R), let b,δb∈Rn with b≠0, let x=A−1b, and let δx∈Rn satisfy A(x+δx)=b+δb. Then

∥δx∥p∥x∥p  ≤  κp(A) ∥δb∥p∥b∥p.

In words: the relative perturbation of the solution is at most the condition number κp(A) of The condition number kappa_p(A) = ||A||_p ||A^{-1}||_p of a nonsingular linear system times the relative perturbation of the right-hand side.

Facts & Assumptions

Given: An invertible matrix A∈GL⁡n(R) with n≥1, vectors b,δb∈Rn with b≠0, x=A−1b and δx=A−1δb.

[L2]

Compatibility of the induced norm: ∥Ay∥p≤∥A∥p∥y∥p for every y (Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1).

[L3]

κp(A)=∥A∥p∥A−1∥p (The condition number kappa_p(A) = ||A||_p ||A^{-1}||_p of a nonsingular linear system).

Proof

technique · direct
1.1givenL1algebra

Subtracting the two equations gives Aδx=A(x+δx)−Ax=(b+δb)−b=δb, and multiplying on the left by A−1 with [L1] gives δx=A−1δb.

1.2givenL2algebra

Since b=Ax, [L2] gives ∥b∥p=∥Ax∥p≤∥A∥p∥x∥p, and b≠0 forces ∥b∥p>0 and ∥x∥p>0, so 1/∥x∥p≤∥A∥p/∥b∥p.

2.1step 1.1L2algebra

By [L2] applied to A−1 at the vector δb, step 1.1 gives ∥δx∥p=∥A−1δb∥p≤∥A−1∥p∥δb∥p.

3.1step 2.1step 1.2L3algebra

Multiplying the bound of step 2.1 by the bound of step 1.2 gives ∥δx∥p/∥x∥p≤∥A−1∥p∥A∥p∥δb∥p/∥b∥p, and [L3] identifies the coefficient product as κp(A), which is the stated bound.

4.1step 3.1∎

Step 3.1 proves the stated bound.

Remarks

  • The bound is sharp at p=2. The constant κ2(A) is optimal, but the two equalities in the proof are attained on different singular directions: choose x along a right singular vector for the largest singular value of A, so b=Ax has norm σmax⁡∥x∥2, and choose δb along a left singular vector for the smallest singular value, so ∥A−1δb∥2=σmin⁡−1∥δb∥2. Then the bound is attained with coefficient σmax⁡/σmin⁡=κ2(A).

Depends on

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Sources