math and physics play

Equation of a hyperplane, and shortest distance between two hyperplanes.

December 13, 2024 math and physics play , , , , , , ,

[Click here for a PDF version of this post]

Scalar equation for a hyperplane.

In our last post, we found, in a round about way, that

Theorem 1.1:

The equation of a \(\mathbb{R}^N\) hyperplane, with distance \( d \) from the origin, and normal \( \mathbf{\hat{n}} \) is
\begin{equation*}
\Bx \cdot \mathbf{\hat{n}} = d.
\end{equation*}

Start proof:

Let \( \beta = \setlr{ \mathbf{\hat{f}}_1, \cdots \mathbf{\hat{f}}_{N-1} } \) be an orthonormal basis for the hyperplane normal to \( \mathbf{\hat{n}} \), and \( \Bd = d \mathbf{\hat{n}} \) be the vector in that hyperplane, closest to the origin, as illustrated in fig. 1.

 

fig 1. R^3 plane with normal n-cap

The hyperplane \( d \) distant from the origin with normal \( \mathbf{\hat{n}} \) has the parametric representation
\begin{equation}\label{eqn:hyperplaneGeometry:40}
\Bx(a_1, \cdots, a_{N-1}) = d \mathbf{\hat{n}} + \sum_{i = 1}^{N-1} a_i \mathbf{\hat{f}}_i.
\end{equation}
Equivalently, suppressing the parameterization, with \( \Bx = \Bx(a_1, \cdots, a_{N-1}) \), representing any vector in that hyperplane, by dotting with \( \mathbf{\hat{n}} \), we have
\begin{equation}\label{eqn:hyperplaneGeometry:60}
\Bx \cdot \mathbf{\hat{n}} = d \mathbf{\hat{n}} \cdot \mathbf{\hat{n}},
\end{equation}
where all the \( \mathbf{\hat{f}}_i \cdot \mathbf{\hat{n}} \) dot products are zero by construction. Since \( \mathbf{\hat{n}} \cdot \mathbf{\hat{n}} = 0 \), the proof is complete.

End proof.

Incidentally, observe we can also write the hyperplane equation in dual form, as
\begin{equation}\label{eqn:hyperplaneGeometry:220}
\Bx \wedge (\mathbf{\hat{n}} I) = d I,
\end{equation}
where \( I \) is an \(\mathbb{R}^N\) pseudoscalar (such as \( I = \mathbf{\hat{n}} \mathbf{\hat{f}}_1 \cdots \mathbf{\hat{f}}_{N-1} \)).

Our previous parallel plane separation problem.

The standard \(\mathbb{R}^3\) scalar form for an equation of a plane is
\begin{equation}\label{eqn:hyperplaneGeometry:80}
a x + b y + c z = d,
\end{equation}
where \( d \) looses it’s geometrical meaning. If we form \( \Bn = (a,b,c) \), then we can rewrite this as
\begin{equation}\label{eqn:hyperplaneGeometry:100}
\Bx \cdot \Bn = d,
\end{equation}
for this representation of an equation of a plane, we see that \( d/\Norm{\Bn} \) is the shortest distance from the origin to the plane. This means that if we have a pair of parallel plane equations
\begin{equation}\label{eqn:hyperplaneGeometry:120}
\begin{aligned}
\Bx \cdot \Bn &= d_1 \\
\Bx \cdot \Bn &= d_2,
\end{aligned}
\end{equation}
then the distance between those planes, by inspection, is
\begin{equation}\label{eqn:hyperplaneGeometry:140}
\Abs{ \frac{d_2}{\Norm{\Bn}} – \frac{d_1}{\Norm{\Bn}} },
\end{equation}
which reduces to just \( \Abs{d_2 – d_1} \) if \( \Bn \) is a unit normal for the plane. In our previous post, the problem to solve was to find the shortest distance between the parallel planes given by
\begin{equation}\label{eqn:hyperplaneGeometry:160}
\begin{aligned}
x – y + 2 z &= -3 \\
3 x – 3 y + 6 z &= 1.
\end{aligned}
\end{equation}
The more natural geometrical form for these plane equations is
\begin{equation}\label{eqn:hyperplaneGeometry:180}
\begin{aligned}
\Bx \cdot \mathbf{\hat{n}} &= -\frac{3}{\sqrt{6}} \\
\Bx \cdot \mathbf{\hat{n}} &= \inv{3 \sqrt{6}},
\end{aligned}
\end{equation}
where \( \mathbf{\hat{n}} = (1,-1,2)/\sqrt{6} \), as illustrated in fig. 2.

fig. 2. The two planes.

 

Given that representation, we can find the distance between the planes just by taking the absolute difference of the respective distances to the origin
\begin{equation}\label{eqn:hyperplaneGeometry:200}
\begin{aligned}
\Abs{ -\frac{3}{\sqrt{6}} – \inv{3 \sqrt{6}} }
&= \frac{\sqrt{6}}{6} \lr{ 3 + \inv{3} } \\
&= \frac{10}{18} \sqrt{6} \\
&= \frac{5}{9} \sqrt{6}.
\end{aligned}
\end{equation}

Shortest distance between two parallel planes.

December 13, 2024 math and physics play , , ,

[Click here for a PDF version of this post]

The problem.

Helping Karl with his linear algebra exam prep, he asked me about this problem

Problem:

Find the shortest distance between the two parallel planes, \( P_1 \), and \( P_2 \), with respective equations:
\begin{equation*}
\begin{aligned}
x – y + 2 z &= -3 \\
3 x – 3 y + 6 z &= 1.
\end{aligned}
\end{equation*}

A numerical way to tackle the problem.

A fairly straightforward way to tackle this problem is illustrated in the sketch of fig. 1. If we can find a point in the first plane, we can follow the normal to the plane to the next, and compute the length of that connecting vector.

fig. 1. Distance between two planes.

fig. 1. Distance between two planes.

For this problem, let
\begin{equation}\label{eqn:distanceBetweenPlanes:20}
\Bn = (1,-1,2),
\end{equation}
and rescale the two plane equations to use the same normal. That is
\begin{equation}\label{eqn:distanceBetweenPlanes:40}
\begin{aligned}
\Bx_1 \cdot \Bn &= -3 \\
\Bx_2 \cdot \Bn &= \inv{3},
\end{aligned}
\end{equation}
where \( \Bx_1 \) are vectors in the first plane, and \( \Bx_2 \) are vectors in the second plane. Finding a vector in one of the planes isn’t hard. Suppose, for example, that \( \Bx_0 = (\alpha, \beta, \gamma) \) is a vector in the first plane, then
\begin{equation}\label{eqn:distanceBetweenPlanes:60}
\alpha – \beta + 2 \gamma = -3.
\end{equation}
One solution is \( \alpha = -3, \beta = 0, \gamma = 0 \), or \( \Bx_0 = (-3, 0, 0) \). We can follow the normal from that point to the closest point in the second plane by forming
\begin{equation}\label{eqn:distanceBetweenPlanes:80}
\By_0 = \Bx_0 + k \Bn,
\end{equation}
where \( k \) is to be determined. If \( \By_0 \) is a point in the second plane, we must have
\begin{equation}\label{eqn:distanceBetweenPlanes:100}
\begin{aligned}
\inv{3}
&=
\By_0 \cdot \Bn \\
&=
\lr{ \Bx_0 + k \Bn } \cdot \Bn \\
&=
(-3, 0, 0 ) \cdot (1,-1,2) + k (1,-1,2) \cdot (1,-1,2) \\
&=
-3 + 6 k,
\end{aligned}
\end{equation}
or
\begin{equation}\label{eqn:distanceBetweenPlanes:120}
k = \frac{10}{18} = \frac{5}{9}.
\end{equation}
This means the point in plane two closest to \( \Bx_0 = (-3,0,0) \) is
\begin{equation}\label{eqn:distanceBetweenPlanes:140}
\begin{aligned}
\By_0
&= (-3, 0, 0 ) + \frac{5}{9} (1,-1,2) \\
&= \inv{9} (-27 + 5, -5, 10) \\
&= \inv{9} (-22, -5, 10),
\end{aligned}
\end{equation}
and the vector distance between the planes is
\begin{equation}\label{eqn:distanceBetweenPlanes:160}
\begin{aligned}
\By_0 – \Bx_0
&= \inv{9} (-22, -5, 10) – (-3, 0, 0 ) \\
&= \inv{9} (-22 + 27, -5, 10) \\
&= \inv{9} (5, -5, 10).
\end{aligned}
\end{equation}
This vector’s length is \( \sqrt{150}/9 = (5/9) \sqrt{6} \), which is the shortest distance between the planes.

A symbolic approach.

Generally, we get more clarity if we avoid plugging in numbers until the very end, so let’s try a generalization of this problem.

Problem:

Find the shortest distance between the two parallel planes, \( P_1 \), and \( P_2 \), with respective equations:
\begin{equation*}
\begin{aligned}
\Bx_1 \cdot \Bn_1 &= d_1 \\
\Bx_2 \cdot \Bn_2 &= d_2.
\end{aligned}
\end{equation*}

We can use the same approach, but first, let’s rescale the two normals. Let
\begin{equation}\label{eqn:distanceBetweenPlanes:180}
\Bn_2 = t \Bn_1,
\end{equation}
or
\begin{equation}\label{eqn:distanceBetweenPlanes:200}
\Bn_1 \cdot \Bn_2 = t \Bn_1^2,
\end{equation}
so
\begin{equation}\label{eqn:distanceBetweenPlanes:220}
\Bn_2 = \frac{\Bn_1 \cdot \Bn_2}{\Bn_1^2} \Bn_1,
\end{equation}
which means that our plane equations are
\begin{equation}\label{eqn:distanceBetweenPlanes:240}
\begin{aligned}
\Bx_1 \cdot \Bn_1 &= d_1 \\
\Bx_2 \cdot \Bn_1 &= \frac{\Bn_1^2}{\Bn_1 \cdot \Bn_2} d_2,
\end{aligned}
\end{equation}
We can further streamline our plane equation representation, setting \( \ncap = \Bn_1/\Norm{\Bn_1} \), which gives us
\begin{equation}\label{eqn:distanceBetweenPlanes:260}
\begin{aligned}
\Bx_1 \cdot \ncap &= \frac{d_1}{\Norm{\Bn_1}} \\
\Bx_2 \cdot \ncap &= \frac{d_2}{\ncap \cdot \Bn_2}.
\end{aligned}
\end{equation}

This time, let’s assume that we can find a point \( \Bx_0 \) in the first plane, but not actually try to find it. We can still follow the normal to the second plane from that point
\begin{equation}\label{eqn:distanceBetweenPlanes:280}
\By_0 = \Bx_0 + k \ncap,
\end{equation}
but since we only care about the vector distance between the planes, we seek
\begin{equation}\label{eqn:distanceBetweenPlanes:300}
\By_0 -\Bx_0 = k \ncap.
\end{equation}
Now, the constant \( k \), once we find it, is exactly the distance between the planes that we seek. Plugging \( \By_0 \) into the \( P_2 \) equation, we find
\begin{equation}\label{eqn:distanceBetweenPlanes:320}
\begin{aligned}
\frac{d_2}{\ncap \cdot \Bn_2}
&=
\lr{ \Bx_0 + k \ncap } \cdot \ncap \\
&=
\Bx_0 \cdot \ncap + k \\
&=
\frac{d_1}{\Norm{\Bn_1}} + k,
\end{aligned}
\end{equation}
or
\begin{equation}\label{eqn:distanceBetweenPlanes:340}
\boxed{
\Abs{k} = \Norm{\By_0 – \Bx_0} = \Abs{ \frac{d_2}{\ncap \cdot \Bn_2} – \frac{d_1}{\ncap \cdot \Bn_1} }.
}
\end{equation}
If \( \Bn_2 = \Bn_1 = \Bn \), then we have
\begin{equation}\label{eqn:distanceBetweenPlanes:360}
\begin{aligned}
\Norm{\By_0 – \Bx_0} &=
\Abs{
\frac{d_2}{\Bn_1^2/\Norm{\Bn_1}} – \frac{d_1}{\Norm{\Bn_1}}
} \\
&=
\frac{\Abs{d_2 – d_1}}{\Norm{\Bn}},
\end{aligned}
\end{equation}
and if \( \Bn \) is a unit normal, this further reduces to just \( \Abs{d_2 – d_1} \).

Let’s try this for the specific problem originally given. We have \( \Bn_1 = \Bn_2 \), so the distance between the planes is
\begin{equation}\label{eqn:distanceBetweenPlanes:380}
\begin{aligned}
\Norm{\By_0 – \Bx_0}
&= \frac{\Abs{1/3 + 3}}{\sqrt{6}} \\
&= \frac{10}{3 \times 6} \sqrt{6} \\
&= \frac{5}{9} \sqrt{6},
\end{aligned}
\end{equation}
as previously calculated.

Eigenvalues of 2×2 matrix: another identity seen on twitter.

December 11, 2024 math and physics play , , , ,

[Click here for a PDF version of this post]

Here’s another interesting looking twitter math post, this time about 2×2 matrix eigenvalues:

Theorem 1.1: Eigenvalues of a 2×2 matrix.

Let \( m \) be the mean of the diagonal elements, and \( p \) be the determinant. The eigenvalues of the matrix are given by
\begin{equation*}
m \pm \sqrt{ m^2 – p }.
\end{equation*}

This is also not hard to verify.

Start proof:

Let
\begin{equation}\label{eqn:2x2eigen:20}
A =
\begin{bmatrix}
a & b \\
c & d
\end{bmatrix},
\end{equation}
where we are looking for \( \lambda \) that satisfies the usual zero determinant condition
\begin{equation}\label{eqn:2x2eigen:40}
\begin{aligned}
0
&= \Abs{ A – \lambda I } \\
&=
\begin{vmatrix}
a – \lambda & b \\
c & d – \lambda
\end{vmatrix} \\
&=
\lr{ a – \lambda } \lr{ d – \lambda } – b c \\
&=
a d – b c – \lambda \lr{ a + d } + \lambda^2 \\
&=
\mathrm{Det}{A} – \lambda \mathrm{Tr}{A} + \lambda^2 \\
&=
\lr{ \lambda – \frac{\mathrm{Tr}{A}}{2} }^2 + \mathrm{Det}{A} – \lr{ \frac{\mathrm{Tr}{A}}{2}}^2,
\end{aligned}
\end{equation}
so
\begin{equation}\label{eqn:2x2eigen:n}
\lambda = \frac{\mathrm{Tr}{A}}{2} \pm \sqrt{ \lr{ \frac{\mathrm{Tr}{A}}{2}}^2 – \mathrm{Det}{A} }.
\end{equation}
substitution of the variables in the problem statement finishes the proof.

End proof.

Clearly the higher dimensional characteristic equation will also have both a trace and determinant dependency as well, but the cross terms will be messier (and nobody wants to solve cubic or higher equations by hand anyways.)

A funny looking log identity

December 9, 2024 math and physics play

[Click here for a PDF version of this post]

On twitter, I saw a funny looking identity
\begin{equation}\label{eqn:logab:20}
\log_{ab} x = \frac{ \log_a x \log_b x}{\log_a x + \log_b x}.
\end{equation}

To verify this, let
\begin{equation}\label{eqn:logab:40}
\begin{aligned}
u &= \log_a x \\
v &= \log_b x.
\end{aligned}
\end{equation}

This means that
\begin{equation}\label{eqn:logab:60}
\log_{ab} x = \log_{ab} a^u = \log_{ab} b^v.
\end{equation}

We may rewrite either of these in terms of \( a b \), for example
\begin{equation}\label{eqn:logab:80}
\begin{aligned}
\log_{ab} x
&= \log_{ab} b^v \\
&= v \log_{ab} b \\
&= v \log_{ab} \frac{ab}{a} \\
&= v \lr{ 1 – \log_{ab} a },
\end{aligned}
\end{equation}
so
\begin{equation}\label{eqn:logab:100}
u \log_{ab} a = v \lr{ 1 – \log_{ab} a },
\end{equation}
or
\begin{equation}\label{eqn:logab:120}
\lr{ u + v } \log_{ab} a = v,
\end{equation}
or
\begin{equation}\label{eqn:logab:140}
u \log_{ab} a = \frac{u v}{u + v},
\end{equation}
and since \( x = a^u \), our proof is complete.

Area within closed boundary

August 11, 2024 math and physics play , , , , , ,

[Click here for a PDF version of this post]

Motivation.

On vacation I was reading some more of [1]. It was mentioned in passing that the area contained within a closed parameterized curve is given by
\begin{equation}\label{eqn:containedArea:20}
A = \inv{2} \int_{t_0}^{t_1} \lr{x y’ – y x’} dt,
\end{equation}
where \( x = x(t), y = y(t), t \in [t_0, t_1] \). This has the look of a Stokes theorem coordinate expansion (specifically, the Green’s theorem special case of Stokes’), but with somewhat mysterious looking factor of one half out in front. My aim in this post is to understand the origins of this area relationship, and play with it a bit.

Circular coordinates example.

The book suggests that the reader verify this for a circular parameterization, so we’ll do that here too.

Let
\begin{equation}\label{eqn:containedArea:40}
\begin{aligned}
x(t) &= r \cos t \\
y(t) &= r \sin t,
\end{aligned}
\end{equation}
where \( t \in [0, 2 \pi] \). Plugging in this, we have
\begin{equation}\label{eqn:containedArea:60}
\begin{aligned}
A
&= \inv{2} \int_0^{2 \pi} \lr{ r \cos t \lr{ r \cos t } – r \sin t \lr{ – r \sin t } } dt \\
&= \frac{r^2}{2} \int_0^{2 \pi} \lr{ \cos^2 t + \sin^2 t } dt \\
&= \frac{2 \pi r^2}{2} \\
&= \pi r^2.
\end{aligned}
\end{equation}
This simple example works out.

Piecewise linear parametrization example.

One parameterization of the unit parallelogram depicted in fig. 1 is

\begin{equation}\label{eqn:containedArea:340}
\begin{aligned}
(x,y) &= (t, 0),\quad t \in [0,1] \\
&= (t, t – 1),\quad t \in [1,2] \\
&= (4 – t, 1),\quad t \in [2,3] \\
&= (4 – t, 4 – t),\quad t \in [3,4]
\end{aligned}
\end{equation}

fig. 1. Parallelogram with unit area.

fig. 1. Parallelogram with unit area.

Respective evaluating of \( x y’ – y x’ \) in each of these regions gives
\begin{equation}\label{eqn:containedArea:360}
\begin{aligned}
(t) (0) – (0)(0) &= 0 \\
(t) (1) – (t-1)(1) &= 1 \\
(4-t)(0) – (1)(-1) &= 1 \\
(4-t)(-1) – (4-t)(-1) &= 0,
\end{aligned}
\end{equation}
and integrating
\begin{equation}\label{eqn:containedArea:380}
\inv{2} \int_0^4 \lr{ x y’ – y x’} dt = \frac{2}{2} = 1,
\end{equation}
as expected. In this example, the directional derivative is not continuous at the corners of the parallelogram, but that is not a requirement (as it should not be, as the area is well defined despite any corners.)

Can we discover this relationship using the Jacobian?

Graphically, I can imagine that we could find this area relationship, by considering a parameterization of a family of nested closed curves, as depicted in fig. 2.

fig. 2. Family of nested closed curves.

fig. 2. Family of nested closed curves.

For such a parameterization, calculating the area is just a Jacobian evaluation
\begin{equation}\label{eqn:containedArea:80}
\begin{aligned}
A
&= \iint \frac{\partial(x, y)}{\partial(u,t)} du dt \\
&= \iint \lr{ \PD{u}{x} \PD{t}{y} – \PD{u}{y} \PD{t}{x} } du dt \\
&= \iint \lr{ \PD{u}{x} y’ – \PD{u}{y} x’ } du dt.
\end{aligned}
\end{equation}
Let’s try to eliminate the \( u \) derivatives using integration by parts, and see what we get.
\begin{equation}\label{eqn:containedArea:100}
\begin{aligned}
A
&= \iint \lr{ \PD{u}{x} y’ – \PD{u}{y} x’ } du dt \\
&= \iint \frac{d}{du} \lr{ x y’ – y x’ } du dt – \iint \lr{ x \PD{u}{y’} – y \PD{u}{x’} } du dt \\
&= \int \lr{ x y’ – y x’ } dt – \iint \lr{ x \PD{u}{y’} – y \PD{u}{x’} } du dt.
\end{aligned}
\end{equation}
This is interesting, as we find the area equation that we are interested (times two), but we have a strange new area equation. Essentially, we have found, assuming we trust the claim in the book, that
\begin{equation}\label{eqn:containedArea:120}
A = 2 A – \iint \lr{ x \PD{u}{y’} – y \PD{u}{x’} } du dt,
\end{equation}
so it seems that the area can also be expressed as
\begin{equation}\label{eqn:containedArea:140}
A = \iint \lr{ x \frac{\partial^2 y}{\partial u \partial t} – y \frac{\partial^2 x}{\partial u \partial t} } du dt.
\end{equation}
Let’s again use the circular parameterization to verify that this works. I won’t try to prove this directly, but instead, we’ll use Stokes’ theorem to prove the stated result, from which we get this second derivative area formula as a side effect by virtue of our integration by parts expansion above.

For the circular parameterization, we have
\begin{equation}\label{eqn:containedArea:160}
\begin{aligned}
A
&= \int_{r = 0}^R dr \int_{t = 0}^{2 \pi} dt \lr{ x \frac{\partial^2 y}{\partial r \partial t} – y \frac{\partial^2 x}{\partial r \partial t} } \\
&= \int_{r = 0}^R dr \int_{t = 0}^{2 \pi} dt \lr{ r \cos t \frac{\partial \sin t}{\partial t} – r \sin t \frac{\partial \cos t}{\partial t} } \\
&= \int_{r = 0}^R r dr \int_{t = 0}^{2 \pi} dt \lr{ \cos^2 t + \sin^2 t } \\
&= \frac{R^2}{2} 2 \pi \\
&= \pi R^2.
\end{aligned}
\end{equation}
This checks out, at least for this one specific circular parameterization.

Area formula derivation using Stokes’ theorem.

Theorem 1.1: Green’s theorem.

\begin{equation}\label{eqn:containedArea:260}
\iint dx dy \lr{ \PD{x}{M} – \PD{y}{L} } = \oint L dx + M dy.
\end{equation}

Start proof:

We start with the general two parameter integration theorem
\begin{equation}\label{eqn:containedArea:180}
\iint F d^2 \Bx \lrpartial G = -\oint F d\Bx G,
\end{equation}
set \( F = 1, G = \Bf \), and apply scalar selection
\begin{equation}\label{eqn:containedArea:200}
\iint \gpgradezero{ d^2 \Bx \lrpartial \Bf } = -\oint d\Bx \cdot \Bf,
\end{equation}
to find the two parameter form of Stokes’ theorem
\begin{equation}\label{eqn:containedArea:220}
\iint d^2 \Bx \cdot \lr{ \spacegrad \wedge \Bf } = -\oint d\Bx \cdot \Bf,
\end{equation}

With a planar parameterization, say \( \Bf = L \Be_1 + M \Be_2 \), we have \( d\Bx \cdot \Bf = L dx + M dy \), and for the LHS
\begin{equation}\label{eqn:containedArea:240}
\begin{aligned}
\iint d^2 \Bx \cdot \lr{ \spacegrad \wedge \Bf }
&=
\iint dx dy \Be_{12}^2
\begin{vmatrix}
\partial_1 & \partial_2 \\
L & M
\end{vmatrix} \\
&=
-\iint dx dy \lr{ \PD{x}{M} – \PD{y}{L} }.
\end{aligned}
\end{equation}

End proof.

Parameterized area equation.

If we wish to evaluate an elementary area, we can pick \( L, M \) such that \( \PDi{x}{M} – \PDi{y}{L} = 1 \). One such selection is
\begin{equation}\label{eqn:containedArea:280}
\begin{aligned}
M &= \frac{x}{2} \\
L &= -\frac{y}{2},
\end{aligned}
\end{equation}
so
\begin{equation}\label{eqn:containedArea:300}
A = \inv{2} \oint -y dx + x dy = \inv{2} \int \lr{ x y’ – y x’ } dt.
\end{equation}
Clearly, there are other possible choices of \( L, M \) that we could use to find alternate area equations, but this choice seems to be independent of the shape of the region.

References

[1] F.W. Byron and R.W. Fuller. Mathematics of Classical and Quantum Physics. Dover Publications, 1992.