Hi all! I finally decided to have some blog with mathematical notes (I do not want to publish anything not concerning mathematics here, in particular this is why everything is assumed to be in “English”). I would continue on rus4.livejournal.com, but unfortunately livejournal does not support formulae and the easiest way to fix it seems to crosspost from here.
Approximation of a symmetric convex body by polytopes
Let be an origin-symmetric symmetric convex body in
,
What is the optimal upper bound for the number of vertices of the convex polytope
satisfying
? Here we may consider both large
and small
. As far as I understand, in full generality the 2-parametric asymptotic is still open, see some recent results and a survey in the paper of Alexander Barvinok. UPDATE: Sasha Polyanskii immediately informed me about the result by Fedja Nazarov, Dima Ryabogin and Márton Naszódi where the sharp estimate is obtained.
Consider the fixed regime. Recently I asked the participants of Online IMC 2020 to prove the
bound and I found out from the discussions after the contest that the sharp order is
. The goal of this post is to share a nice argument for that borrowed from the paper by Bronstein and Ivanov (Siberian Math. Journal 1975), and a bit simplified. The lower bound is realised already for the Euclidean sphere, it is easy (each vertex
of
can see only
part of
).
First of all, we make a linear transform so that the John ellipsoid of is a unit ball
, now
.
is contained in
and thus it suffices to find
points in
whose convex hull contains
. The idea is to use the parallel surface.
Namely, consider the boundary of the set
and choose a minimal (by cardinality)
-net on
, denote it by
. We have
(since
is bi-Lipschitz equivalent to the surface of the unit cube, for example). For
denote by
the outer normal to
at
(it is unique, but we do not use this), that is, such a unit vector that
It means that , where
is an outer normal at
for the set
(there may exist other outer normals at
). The point
belongs to
. Define
It suffices to prove that
if we assume
. By convex duality,
is equivalent to
for any unit vector . For fixed
let
be a point on
such that
is maximal. We have
, thus there exists
such that
We have and
, thus
in particular since
. Thus for
we have
and (1) for is proved.
Euler’s proof of pentagonal theorem
tetrahedron
Hi there!
Today we prove that
in an arbitrary tetrahedron .
Draw three planes: through orthogonal to the external bisector of
, through
orthogonal to the external bisector of
, through
orthogonal to the external bisector of
.

Let them meet at point , and let
be projections of
onto lines
respectively (see the picture).
We have and also
,
,
by construction (the segments are directed in the natural sense: either
both belong to rays
respectively, or both do not, and so on). Also the right triangles
are equal by hypotenuse and cathetus. Thus
and
Well, possibly our three planes do not have a common point, that is, the external bisectors of angles are complanar? Then start with arbitrary point
, choose
,
so that
,
,
. The lines
are parallel to aforementioned three external bisectors, thus the points
are complanar and again
, now due to Menelaus theorem for this plane
:
.
UPDATE: the real tetrahedron stuff is discussed here
e⩽π
Mathematicians do like giving proofs of known results from some specific point of view.
Here goes the combinatorial proof of the inequality
To be precise, I define our heroes. is a length of a half-circle of the unit radius, and
is the common point of the segments
The idea is that for a positive integer and a complex
,
, we have
The latter inequality is obtained by multiplying the definitive inequalities for
We apply it for , the primitive root of unity of degree
.
Note that since the chord of the unit circle joining 1 and
is shorter than the arc between these two points (which equals
, since the whole circle is a union of
congruent arcs.)
We have
On the other hand, since and
is the complex conjugate to
for
, we get
Thus that yields (1) as
would grow exponentially fast if
.
Well, if you really prefer combinatorial proofs, here is a variant of the above argument for you. Although I do not provide any combinatorial quantity counted by (that what we expect from a combinatorial proof of inequality (1)), I instead do this for something like
.
Count the number of permutations of
for which the number of inversions is divisible by
. Since
is a generating polynomial for the number of permutations with the given number of inversions, by the polarization formula
for we get
If , we see that the summands corresponding to
are negative reals provided that
(this follows from evaluating the argument of
, which equals
, and summing over
: we get
that is
modulo
for
). By absolute value they are exponentially greater both than
(which corresponds to
, see the explanation above) and all other summands (since say
for other roots, and
is either 0 or the same as
. So we get
that is absurd.
Low-level variant of Huang’s argument for sensitivity conjecture
Consider the set of all
words
, over the alphabet
(the vertices of Boolean cube). We naturally multiply the words by concatenation: if
, then
. For
and
denote by
the word which differs from
only in
-th position. Clearly the operators
are involutive and mutually commute. We further use the same notations
for different values of
(namely, both for
and
.) We say that
are joined by an edge if
for some
. This graph (the skeleton of the Boolean cube) is bipartite by the chessboard coloring. In particular, it contains an independent subset of size
.
The recent sensational result by Hao Huang is that if we take a subset , the induced subgraph on this subset already contains a vertex of degree at least
. That is sharp by the earlier example of F. R. K. Chung, Z. Füredi, R. L. Graham and P. Seymour.
Everybody is very excited since this estimate settles the so called sensitivity conjecture, and this is not what I am going to tell about (cause of complete ignorance.)
The aim of this post is to give an explicit exposition of Huang’s argument without using any theorems but the following: the linear system of equations and
variables has a non-trivial solution.
Following Huang, we take a not-everywhere-zero function which vanishes outside
and satisfies some (other)
linear relations. Such a function exists by the aforementioned theorem. For describing these relations, we define
for
. Clearly
and the straightforward but crucial property is
Now we are ready to formulate the relations on
, they are the following:
You may say that the number of relations is , but actually the relations (1) for
starting from 0, say
, which read as:
imply relations (1) for :
This reduces to () after we express
via the values
. Indeed, Right Hand Side of (3) reads as
the coefficient of in this expression equals
. The coefficient of
is
for all
. Finally for
the coefficient of
equals
by
.
Now if is chosen so that
is maximal, we have
and looking at the right hand side we conclude that at least
words of the form
must belong to the support of
, thus to
. The proof is complete.
Of course the reason why equations (2) imply the other
equations (3) is that the corresponding to (1) operator
has an eigensubspace of dimension
for the eigenvalue
. Or viceversa, if you prefer. I cannot say that I understand well where does this operator come from.