Definition 42
If \(\tilde A\) is a symmetric
\(N\times N\) matrix, \(\vec x\in\mathbb{R}^N\) than:
\(\tilde
A\) is positive definite if \(\vec x\cdot\tilde A\vec{x}\gt0\) for all \(\vec{x}\)
\(\tilde A\) is positive semi-definite if \(\vec x\cdot\tilde A\vec{x}\ge0\) for all \(\vec{x}\)
\(\tilde A\) is negative definite if \(\vec x\cdot\tilde
A\vec{x}\lt0\) for all \(\vec{x}\)
\(\tilde A\) is negative semi-definite
if \(\vec x\cdot\tilde A\vec{x}\le0\) for all \(\vec{x}\)
\(\tilde
A\) is called indefinite if \(\vec x,\vec y\) exist with \(\vec x\cdot\tilde{A}\vec{x}\gt0\) and
\(\vec y\cdot\tilde{A}\vec{y}\lt0\)
Criterion for extreme values in \(N\)-dimensions:
\(f:\mathbb{R}^N\to\mathbb{R}\;\;\vec{x}_0\in\mathbb{R}^N\) extreme value (local) than \(\vec\nabla f(\vec{x}_0)=0\)
if:
\(\tilde H(\vec{x}_0)\) positive
definite\(\to\) minimum
\(\tilde H(\vec{x}_0)\) negative definite\(\to\)
maximum
\(\tilde H(\vec{x}_0)\) indefinite\(\to\) no extremum (saddle point)
\(\tilde H(\vec{x}_0)\) positive semi-definite or neg.
semi-definite then no decision is possible
Note: 1D is special case.
Hurwitz criterion:
\(\tilde{A}\;\;N\times N\) matrix, symmetric
| is positive definite if and only if |
for all \(k=1,\ldots,N\) . |
© J. Carstensen (Math for MS)