For , the product rule for the curl gives
Since , this is not identically zero on . A gradient field has zero curl, so no scalar potential exists for on the whole space.
For the radial vector field , observe that
Thus all vector-field potentials are
These are smooth everywhere, including the origin. Any two differ by a constant because their gradient difference vanishes on the connected whole space.
Near a nonzero stationary point , Taylor expansion of the gradient gives . Since the Hessian matrix is positive definite, for sufficiently small nonzero . Thus the gradient arrows point away from each of the four local minima, with different strengths in the two eigenvector directions.
At the origin, on the -axis the gradient is and on the -axis it is , so arrows point outward. On the gradient is , and on it is , so arrows point toward the origin for small . Away from the axes the leading terms are , pointing inward in both coordinate signs. The narrow outward sectors close to the axes are visible in the exact vector field.
Figure 1.
Gradient directions near all five stationary points
. Arrows show normalized gradient directions, not the direction of descent. They point outward near the four minima, while the degenerate origin has outward axis directions and inward diagonal directions. Details.
Along either coordinate axis, the restriction is , which is positive for . Along either diagonal it is
which is negative for sufficiently small nonzero . Therefore every neighbourhood of the origin contains both positive and negative values, whereas . Consequently
The Hessian matrix vanishes there, so the ordinary second-derivative test is inconclusive; the restrictions settle the question.
Figure 1.
Opposite signs along axes and diagonals establish the saddle at the origin
. The two axes give , while the two diagonals give near zero. The panels use different vertical scales to make both signs visible. Details.
The Hessian matrix is
At each nonzero stationary point, its diagonal entries are and the off-diagonal entries are . Its eigenvalues are and , both positive. Thus the Hessian matrix is positive definite, and the second-derivative test proves that
Their common value is . In fact they are global minima: with , the arithmetic-geometric mean inequality gives , so
The last one-variable polynomial attains its minimum at , and equality in the first bound requires , precisely the four points above.
A stationary point must satisfy both factored equations from part (a). If either coordinate is zero, the other equation forces the other coordinate to vanish. This gives .
Otherwise and . With and , these become and . Dividing gives , so . Then forces . Hence all five stationary points are
The gradient is horizontal when and vertical when . Factoring those partial derivatives gives the two loci:
Thus each locus consists of an axis and two parabolas. Their common points are the five stationary points found below. At those points the gradient is zero and has no direction, so they are included in both algebraic zero-component conditions but excluded if “parallel” is required to mean a nonzero directed vector.
Figure 1.
Loci of horizontal and vertical gradients
. Blue curves have , orange curves have , and black points mark their intersections. At the black points the gradient itself vanishes. Details.
A necessary compatibility condition is , because a cross product with is perpendicular to it. If this condition fails, there is no solution.
If it holds, take the equation and use the vector triple product identity:
Thus the perpendicular orthogonal projection is fixed and the parallel component is free:
Conversely, under the compatibility condition, so every displayed vector solves the equation. The solution set is a straight line, including the line through the origin when .
The straight line consists of , with . Since the endpoints are distinct, the direction vector can be normalized. Choose
Putting gives the required parametric equation of a straight line . Other choices of on the same straight line, or the opposite direction for , give equivalent descriptions.
The scalar is the signed scalar projection along the unit vector . Multiplying by that unit vector makes the orthogonal projection of onto its span.
For the other expression, the vector triple product identity gives
This is the orthogonal projection onto a plane perpendicular to through the origin. Its dot product with is zero, while the difference from is parallel to .
From the factorisation in part (a), or . Substitution into the parabola equation gives respectively or . Thus the two intersections of the parabola and the unit circle are
Substitution for reduces the two simultaneous equations to
The remaining quadratic factor is positive for all real , since . Thus there are exactly .

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