The evaluated position vectors are and . Their difference is , so the Euclidean distance is
The cross product of the direction vectors is . It is nonzero, so a common perpendicular direction exists. Projecting onto its unit vector gives the distance between skew lines:
Thus
The numerator is a scalar triple product; movement along either line does not change this perpendicular projection.
Use only the dot product to minimize the squared Euclidean distance from the origin:
Completing the square gives
The square is minimized at , yielding
Geometrically this subtracts the squared length of the orthogonal projection parallel to the line. At the minimizing point, , as required for perpendicular distance.
Here , and . Therefore
The closest point has parameter and position , whose norm gives the same result.
For a point , the magnitude of is the area of a parallelogram. Dividing by its base length gives the perpendicular Euclidean distance to the first line. Explicitly,
Hence
This use of the cross product removes any component parallel to the first line automatically.
The graph is the upper branch of the hyperbola . It is symmetric about , positive everywhere, and has its lowest point at . Its derivatives are
so it is strictly convex. Since
its two oblique asymptotes are
Figure 1.
Distance from a moving point to a skew line
. Original plot of , its minimum and its two asymptotes. Details.
The square is minimized when its first term vanishes. Therefore
This agrees with the distance between skew lines found in part (b). The corresponding parameter on the first line is its orthogonal projection value
The closest pair is and ; their connecting vector is perpendicular to both direction vectors.

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