Differentiate each coordinate with respect to the parameter and use the chain rule:
Both coordinates are defined where and . As a check, there, so the same derivative is .
The seven requested parabolas are for ; the case is the horizontal axis. Positive opens upward, negative downward, and larger gives a narrower parabola. All pass through the origin with a horizontal tangent line.
To find orthogonal trajectories, use the continuous family containing these seven representative members. Its slope is away from . Two finite slopes of perpendicular tangents multiply to , so the orthogonal slope is
This separable differential equation gives , hence
These ellipses are the regular real trajectories; is a single point, not a regular curve, and has no real points. One example is , with semiaxes horizontally and vertically.
The implicit tangent relation also describes points with vertical tangents, where the slope quotient is unavailable. At intersections with the member , the ellipse has a vertical tangent line and is perpendicular to that horizontal axis. The common origin of the parabolas is not on any regular trajectory with . The sketch shows the requested seven members and the example ellipse.
Figure 1.
Seven parabolic family members and an elliptical orthogonal trajectory
. Details.
The original differential equation is defined only where and . Put , so the chain rule gives . Multiplying the equation by yields a first-order linear ordinary differential equation:
An integrating factor on either half-line is , up to an irrelevant nonzero constant. Equivalently, apply the quotient rule directly:
Integrating gives . Thus the real general solution is
on any connected interval where and . These restrictions preserve both the original denominator and differentiability of the real cube root. In particular includes on either half-line; this solution has not been lost through division by an expression involving .
For verification, differentiating gives , while . The two sides agree wherever the original equation is defined.
The supplied bracket gives . Part (b) ensures , so each error satisfies
Writing gives and . After three steps the exponent has doubled three times:
This rigorous error bound uses only the given bracket, not advance knowledge of the digits of .
The actual rational iterates are , , and . They illustrate the bound, but the proof comes from the exact error identity and the positive lower bound on the denominators.
Write and subtract it from the iteration:
This is an Exact Newton error identity for a quadratic. If , then and , so . Induction from proves for every . Also , so the iterates decrease while remaining above the root. Their limit must satisfy , hence . The squared error numerator explains the ensuing quadratic convergence.
The Newton root-finding iteration follows the current tangent line to its horizontal-axis intersection. Since , it gives
A positive initial guess is appropriate for the positive square root; negative starting values instead target the negative root.
Using the signs chosen above, explicit multiplication gives
Thus the nonzero cases obey . This relates the right singular vectors and left singular vectors in a singular value decomposition. For example, if and , then with .
In , every row sums to one, so is an eigenvector with eigenvalue . The antisymmetric direction gives eigenvalue . A direction orthogonal to both is , and direct multiplication shows that it lies in the kernel of a linear map. Normalizing all three gives the complete system
They form an orthonormal basis of . The extra zero eigenvalue reflects the one-dimensional kernel of a linear map for this rank-two rectangular matrix. The question's symbols denote the eigenvalues of ; the nonzero singular values of are their square roots, namely and .
For a matrix with equal diagonal entries, the symmetric and antisymmetric directions and are eigenvectors. Direct multiplication of gives eigenvalues and . Dividing each direction by its Euclidean norm gives
The two normalized eigenvectors are orthogonal; either may independently be replaced by its negative.
A row's squared Euclidean norm is , and the dot product of the two rows is . Thus the row Gram matrix is
For the column Gram matrix, the first and third squared column norms are , the middle one is , and the respective cross-products are . Hence
The two products are square matrices of different sizes; their unequal dimensions are consistent with being rectangular.

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