Definiteness–Signature Relationship
Definiteness and signature are directly related: the definiteness of a symmetric matrix is completely determined by its signature . The matrix is PD iff (all positive, none zero or negative), PSD iff (no negative eigenvalues), ND iff and , NSD iff , and indefinite iff both and .
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
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
A symmetric matrix has signature . What is its definiteness?
A matrix with signature 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 , the last counts the number of *negative* eigenvalues, not the matrix dimension.