Skip to content
Linear Algebra, Optimisation and Numerical Methods / Floating-Point Arithmetic, Conditioning, and Stability

Condition Numbers

A unique hedge can still react strongly to small target changes. The condition number measures worst-case relative sensitivity for a linear solve. This note derives the bound from singular values and explains why determinant magnitude alone is unreliable. We keep the matrix fixed when perturbing the target, so the assumptions of the bound remain explicit.
Learning objectives. By the end of this note you should be able to:
  • define the condition number via the SVD and compute it for a small matrix;
  • state the perturbation bound the condition number controls;
  • explain why κ(A)\kappa(A), not det⁡A\det A, is the right diagnostic for near-singularity;
  • distinguish a well-conditioned from an ill-conditioned problem in a financial example.

In this lesson

  1. Definition and the perturbation bound
  2. Condition number versus determinant

Before you start

The full lesson, with worked examples and exercise solutions, is for members.

Read the full lesson

New to SelfMadeQuant.com? See membership options or browse the Linear Algebra, Optimisation and Numerical Methods syllabus.