## Using a paraboloid to cover points with a disk

Find the equation of tangent line to parabola ${y=x^2}$borrring calculus drill.

Okay. Draw two tangent lines to the parabola, then. Where do they intersect?

If the points of tangency are ${a}$ and ${b}$, then the tangent lines are
${y=2a(x-a)+a^2}$ and ${y=2b(x-b)+b^2}$. Equate and solve:

$\displaystyle 2a(x-a)+a^2 = 2b(x-b)+b^2 \implies x = \frac{a+b}{2}$

Neat! The ${x}$-coordinate of the intersection point is midway between ${a}$ and ${b}$.

What does the ${y}$-coordinate of the intersection tell us? It simplifies to

$\displaystyle 2a(b-a)/2+a^2 = ab$

the geometric meaning of which is not immediately clear. But maybe we should look at the vertical distance from intersection to the parabola itself. That would be

$\displaystyle x^2 - y = \left(\frac{a+b}{2}\right)^2 -ab = \left(\frac{a-b}{2}\right)^2$

This is the square of the distance from the midpoint to ${a}$ and ${b}$. In other words, the squared radius of the smallest “disk” covering the set ${\{a,b\}}$.

Same happens in higher dimensions, where parabola is replaced with the paraboloid ${z=|\mathbf x|^2}$, ${\mathbf x = (x_1,\dots x_n)}$.

Indeed, the tangent planes at ${\mathbf a}$ and ${\mathbf b}$ are
${z=2\mathbf a\cdot (\mathbf x-\mathbf a)+|\mathbf a|^2}$ and ${z=2\mathbf b\cdot (\mathbf x-\mathbf b)+|\mathbf b|^2}$. Equate and solve:

$\displaystyle 2(\mathbf a-\mathbf b)\cdot \mathbf x = |\mathbf a|^2-|\mathbf b|^2 \implies \left(\mathbf x-\frac{\mathbf a+\mathbf b}{2}\right)\cdot (\mathbf a-\mathbf b) =0$

So, ${\mathbf x}$ lies on the equidistant plane from ${\mathbf a}$ and ${\mathbf b}$. And, as above,

$\displaystyle |\mathbf x|^2 -z = \left|\frac{\mathbf a-\mathbf b}{2}\right|^2$

is the square of the radius of smallest disk covering both ${\mathbf a}$ and ${\mathbf b}$.

The above observations are useful for finding the smallest disk (or ball) covering given points. For simplicity, I stick to two dimensions: covering points on a plane with the smallest disk possible. The algorithm is:

1. Given points ${(x_i,y_i)}$, ${i=1,\dots,n}$, write down the equations of tangent planes to paraboloid ${z=x^2+y^2}$. These are ${z=2(x_i x+y_i y)-(x_i^2+y_i^2)}$.
2. Find the point ${(x,y,z)}$ that minimizes the vertical distance to paraboloid, that is ${x^2+y^2-z}$, and lies (non-strictly) below all of these tangent planes.
3. The ${x,y}$ coordinates of this point is the center of the smallest disk covering the points. (Known as the Chebyshev center of the set). Also, ${\sqrt{x^2+y^2-z}}$ is the radius of this disk; known as the Chebyshev radius.

The advantage conferred by the paraboloid model is that at step 2 we are minimizing a quadratic function subject to linear constraints. Implementation in Sage:

 points = [[1,3], [1.5,2], [3,2], [2,-1], [-1,0.5], [-1,1]] constraints = [lambda x, p=q: 2*x[0]*p[0]+2*x[1]*p[1]-p[0]^2-p[1]^2-x[2] for q in points] target = lambda x: x[0]^2+x[1]^2-x[2] m = minimize_constrained(target,constraints,[0,0,0]) circle((m[0],m[1]),sqrt(m[0]^2+m[1]^2-m[2]),color='red') + point(points) 

Credit: this post is an expanded version of a comment by David Speyer on last year’s post Covering points with caps, where I considered the same problem on a sphere.

## Continuity of circumcenter and circumradius

For a bounded set ${A}$ on the plane (or in any Euclidean space) one can define the circumcenter ${c(A)}$ and circumradius ${r(A)}$ as follows: ${r(A)}$ is the smallest radius of a closed disk containing ${A}$, and ${c(A)}$ is the center of such a disk. (Other terms in use: Chebyshev center and Chebyshev radius.)

The fact that ${c(A)}$ is well-defined may not be obvious: what if there are multiple disks of radius ${r(A)}$ that contain ${A}$? To investigate, introduce the farthest distance function ${f_A(x) = \sup_{a\in A} |x-a|}$. By definition, ${c(A)}$ is where ${f_A}$ attains its minimum. The function ${f_A}$ is convex, being the supremum of a family of convex functions. However, that does not guarantee the uniqueness of its minimum. We have two issues here:

• ${x\mapsto |x-a|}$ is not strictly convex
• the supremum of an infinite family of strictly convex functions can fail to be strictly convex (like ${\sup_{1 on the interval ${[0,1]}$).

The first issue is resolved by squaring ${f_A}$. Indeed, ${f_A^2}$ attains its minimum at the same place where ${f_A}$ does, and ${f_A(x)^2 = \sup_{a\in A} |x-a|^2}$ where each term ${|x-a|^2}$ is strictly convex.

Also, we don’t want to lose strict convexity when taking the supremum over ${a\in A}$. For this purpose, we must replace strict inequality by something more robust. The appropriate substitute is strong convexity: a function ${f}$ is strongly convex if there is ${\lambda>0}$ such that ${f(x)-\lambda |x|^2 }$ is convex. Let’s say that ${f}$ is ${\lambda}$-convex in this case.

Since ${|x-a|^2-|x|^2 = -2\langle x,a\rangle + |a|^2}$ is a convex (in fact linear) function of ${x}$, we see that ${|x-a|^2}$ is ${1}$-convex. This property passes to supremum: subtracting ${|x|^2}$ from the supremum is the same as subtracting it from each term. Strong convexity implies strict convexity and with it, the uniqueness of the minimum point. So, ${c(A)}$, the minimum of ${f_A^2}$, is uniquely defined. (Finding it in practice may be difficult. The spherical version of this problem is considered in Covering points with caps).

Having established uniqueness, it is natural to ask about stability, or more precisely, the continuity of ${c(A)}$ and ${r(A)}$ with respect to ${A}$. Introduce the Hausdorff distance ${d_{\mathcal H}}$ on the set of bounded subsets. By definition, ${d_{\mathcal H}(A,B)\le \delta}$ if ${A}$ is contained in ${\delta}$-neighborhood of ${B}$, and ${B}$ is contained in ${\delta}$-neighborhood of ${A}$. It is easy to see that ${r(B)\le r(A) + d_{\mathcal H}(A,B)}$, and therefore

$\displaystyle |r(A)-r(B)|\le d_{\mathcal H}(A,B)$

In words, the circumradius is a ${1}$-Lipschitz function of the set.

What about the circumcenter? If the set ${A}$ is shifted by ${d}$ units in some direction, the circumcenter moves by the same amount. So it may appear that it should also be a ${1}$-Lipschitz function of ${A}$. But this is false.

Observe (or recall from middle-school geometry) that the circumcenter of a right triangle is the midpoint of its hypotenuse:

Consider two right triangles:

• Vertices ${(-1,0), (1,0), (1,\epsilon)}$. The right angle is at ${(1,0)}$, and the circumvcenter is the midpoint of opposite side: ${(0,\epsilon/2)}$.
• Vertices ${(-1,0), (1,0), (\sqrt{1-\epsilon^2},\epsilon)}$. The right angle is at
${(\sqrt{1-\epsilon^2},\epsilon)}$ and the circumcenter is at ${(0,0)}$.

The Hausdorff distance between these two triangles is merely ${1-\sqrt{1-\epsilon^2} < \epsilon^2}$, yet the distance between their circumcenters is ${\epsilon/2}$. So, Lipschitz continuity fails, and the most we can hope for is Hölder continuity with exponent ${1/2}$.

And indeed, the circumcenter is locally ${1/2}$-Hölder continuous. To prove this, suppose ${|c(A)-c(B)|\ge \epsilon}$. The ${1}$-convexity of ${f_A^2}$ implies that

$\displaystyle f_A(c(B))^2 \ge f_A(c(A))^2+|c(A)-c(B)|^2 \ge r(A)^2 + \epsilon^2$

On the other hand, since ${|f_A-f_B|\le d_{\mathcal H}(A,B)}$ everywhere,

$\displaystyle f_A(c(B))\le f_B(c(B)) + d_{\mathcal H}(A,B) = r(B) + d_{\mathcal H}(A,B) \le r(A) + 2 d_{\mathcal H}(A,B)$

Putting things together,

$\displaystyle d_{\mathcal H}(A,B) \ge \frac12 (\sqrt{r(A)^2 + \epsilon^2} - r(A)) = \frac{\epsilon^2}{2( \sqrt{r(A)^2 + \epsilon^2} + r(A) )}$

Thus, as long as ${r(A)}$ remains bounded above, we have an inequality of the form ${d_{\mathcal H}(A,B) \ge c\, \epsilon^2}$, which is exactly ${1/2}$-Hölder continuity.

Remark. The proof uses no information about ${\mathbb R^n}$ other than the ${1}$-convexity of the squared distance function. As such, it applies to every CAT(0) space.

## Covering points with caps

Sometimes easy problems should be solved in a hard way.

Problem 1. Given a finite subset of a circle, find a shortest arc containing the set.

Easy way. Sort the points by polar angle; find a maximal gap between points; return its complement as an answer.

Hard way. Not only is this approach harder, it also requires an additional assumption: the set is contained in some open semi-circle.

Draw the tangent line at every point ${x_k}$ of the set. It divides the plane into two half-planes. Focus on the closed half-plane ${P_k}$ that does not contain the circle. Find the point ${x^*\in \bigcap P_k}$ that is closest to the center of circle. The line ${L}$ from the center to ${x^*}$ is the line of symmetry of the required arc. Its size is determined by the maximal distance of given points to ${L}$.

Finding ${x^*}$ is a quadratic minimization problem ${\|x\|^2\rightarrow \min}$ with linear constraints, which is not so bad. Still, this is obviously the harder way.

Problem 2. Given a finite subset of a sphere, find the smallest spherical cap containing the set.

A moment’s reflection is enough to see that the easy way is no longer available. What was previously the hard way now becomes a reasonable solution which works in every dimension (provided that the set is contained in an open hemisphere). Assuming the sphere is a unit sphere, all you do is minimize ${\|x\|^2}$ subject to linear constraints ${\langle x,x_k\rangle\ge 1 }$ for every ${k}$.

Why this works: ${\{y:\langle x,y\rangle\ge 1 \}}$ is a half-space that intersects the sphere along a spherical cap. The smaller ${\|x\|}$ is, the greater is the distance from this half-space to the origin, and the smaller spherical cap is cut off. The constraints ensure that the cap contains the given points.

One might expect the “flat” version of Problem 2, with plane instead of sphere, to be easier. Yet, I don’t see a way to reduce the flat problem to quadratic minimization with linear constraints.

Problem 3. Given a finite subset of the plane, find the smallest disk containing the set.

This is an instance of the problem of finding the Chebyshev center of a set.