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The 2nd Derivative test

The second derivative test looks at the eigenvalues of the Hessian matrix evaluated at critical points.

The idea for this came from Ben Woodruff, who implemented a similar thing in Mathematica.

var('x,y')
(x, y)
f(x,y)=-x^2+y^2 table, graph=illustrate_test(f) print table show(graph)
Critical PointEigenvalueEigenvector
(0.0, 0.0, 0.0)-2.0(1.0, 0.0)
(0.0, 0.0, 0.0)2.0(0.0, 1.0)
f(x,y)=x*y*exp((-x^2-y^2)/3) table, graph=illustrate_test(f) print table show(graph)
Critical PointEigenvalueEigenvector
(0.0, 0.0, 0.0)1.0(0.707106781187, 0.707106781187)
(0.0, 0.0, 0.0)-1.0(-0.707106781187, 0.707106781187)
(-1.22474487139, -1.22474487139, 0.551819161757)-0.735758882343(0.707106781187, 0.707106781187)
(-1.22474487139, -1.22474487139, 0.551819161757)-0.735758882343(-0.707106781187, 0.707106781187)
(1.22474487139, -1.22474487139, -0.551819161757)0.735758882343(0.707106781187, 0.707106781187)
(1.22474487139, -1.22474487139, -0.551819161757)0.735758882343(-0.707106781187, 0.707106781187)
(-1.22474487139, 1.22474487139, -0.551819161757)0.735758882343(0.707106781187, 0.707106781187)
(-1.22474487139, 1.22474487139, -0.551819161757)0.735758882343(-0.707106781187, 0.707106781187)
(1.22474487139, 1.22474487139, 0.551819161757)-0.735758882343(0.707106781187, 0.707106781187)
(1.22474487139, 1.22474487139, 0.551819161757)-0.735758882343(-0.707106781187, 0.707106781187)
illustrate_test?