Put and . The gradient has components and , so a stationary point requires . In the closed square, and , and the compatible possibilities are precisely the four corners and .
The Hessian matrix isIts eigenvectors and have eigenvalues and . The second-derivative test gives the following classification:
| Point || || Hessian eigenvalues || Classification | | | | Minima | | | | Maxima | | | | Saddle point of a scalar function |
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