Linear Algebra
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Definiteness–Signature Relationship

Definiteness and signature are directly related: the definiteness of a symmetric matrix is completely determined by its signature (p,z,n)(p, z, n). The matrix is PD iff p=np = n (all positive, none zero or negative), PSD iff n=0n = 0 (no negative eigenvalues), ND iff p=0p = 0 and z=0z = 0, NSD iff p=0p = 0, and indefinite iff both p>0p > 0 and n>0n > 0.

This gives you a quick checklist: look at the sign count of eigenvalues. If there is even one negative eigenvalue and one positive eigenvalue, the matrix is indefinite. If there are only non-negatives with at least one zero, it is PSD but not PD. This connection between signature and definiteness makes signature a fundamental classification tool.

Formal View

Theorem 8.6 — Definiteness from Signature
For a symmetric matrix with signature (p,z,n)(p, z, n):\n- PD     (p,z,n)=(ntotal,0,0)\iff (p, z, n) = (n_{\text{total}}, 0, 0)\n- PSD     n=0\iff n = 0\n- ND     (p,z,n)=(0,0,ntotal)\iff (p, z, n) = (0, 0, n_{\text{total}})\n- NSD     p=0\iff p = 0\n- Indefinite     p>0 and n>0\iff p > 0 \text{ and } n > 0

Why This Matters

Knowing the signature lets you immediately classify the optimization landscape without computing exact eigenvalue values.

  • Second-order optimality conditions: the Hessian's signature at a critical point determines if it is a local min (PD), local max (ND), or saddle (indefinite).
  • Matrix nearness problems: how close is an indefinite matrix to the nearest PSD matrix?
  • Stability of equilibria in dynamical systems determined by signature of the linearization.

Quiz

Question 1

A 3×33 \times 3 symmetric matrix has signature (2,1,0)(2, 1, 0). What is its definiteness?

Question 2

A matrix with signature (1,0,2)(1, 0, 2) is indefinite.

Common Mistakes

  • Thinking PSD requires all eigenvalues to be strictly positive — PSD allows zero eigenvalues; strictly positive is PD.
  • Getting confused by the notation: in signature (p,z,n)(p, z, n), the last nn counts the number of *negative* eigenvalues, not the matrix dimension.