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In the tradition of “Polymath projects“, the problem posed in the previous two blog posts has now been solved, thanks to the cumulative effect of many small contributions by many participants (including, but not limited to, Sean Eberhard, Tobias Fritz, Siddharta Gadgil, Tobias Hartnick, Chris Jerdonek, Apoorva Khare, Antonio Machiavelo, Pace Nielsen, Andy Putman, Will Sawin, Alexander Shamov, Lior Silberman, and David Speyer). In this post I’ll write down a streamlined resolution, eliding a number of important but ultimately removable partial steps and insights made by the above contributors en route to the solution.

Theorem 1 Let {G = (G,\cdot)} be a group. Suppose one has a “seminorm” function {\| \|: G \rightarrow [0,+\infty)} which obeys the triangle inequality

\displaystyle \|xy \| \leq \|x\| + \|y\|

for all {x,y \in G}, with equality whenever {x=y}. Then the seminorm factors through the abelianisation map {G \mapsto G/[G,G]}.

Proof: By the triangle inequality, it suffices to show that {\| [x,y]\| = 0} for all {x,y \in G}, where {[x,y] := xyx^{-1}y^{-1}} is the commutator.

We first establish some basic facts. Firstly, by hypothesis we have {\|x^2\| = 2 \|x\|}, and hence {\|x^n \| = n \|x\|} whenever {n} is a power of two. On the other hand, by the triangle inequality we have {\|x^n \| \leq n\|x\|} for all positive {n}, and hence by the triangle inequality again we also have the matching lower bound, thus

\displaystyle \|x^n \| = n \|x\|

for all {n > 0}. The claim is also true for {n=0} (apply the preceding bound with {x=1} and {n=2}). By replacing {\|x\|} with {\max(\|x\|, \|x^{-1}\|)} if necessary we may now also assume without loss of generality that {\|x^{-1} \| = \|x\|}, thus

\displaystyle \|x^n \| = |n| \|x\| \ \ \ \ \ (1)

 

for all integers {n}.

Next, for any {x,y \in G}, and any natural number {n}, we have

\displaystyle \|yxy^{-1} \| = \frac{1}{n} \| (yxy^{-1})^n \|

\displaystyle = \frac{1}{n} \| y x^n y^{-1} \|

\displaystyle \leq \frac{1}{n} ( \|y\| + n \|x\| + \|y\|^{-1} )

so on taking limits as {n \rightarrow \infty} we have {\|yxy^{-1} \| \leq \|x\|}. Replacing {x,y} by {yxy^{-1},y^{-1}} gives the matching lower bound, thus we have the conjugation invariance

\displaystyle \|yxy^{-1} \| = \|x\|. \ \ \ \ \ (2)

 

Next, we observe that if {x,y,z,w} are such that {x} is conjugate to both {wy} and {zw^{-1}}, then one has the inequality

\displaystyle \|x\| \leq \frac{1}{2} ( \|y \| + \| z \| ). \ \ \ \ \ (3)

 

Indeed, if we write {x = swys^{-1} = t zw^{-1} t^{-1}} for some {s,t \in G}, then for any natural number {n} one has

\displaystyle \|x\| = \frac{1}{2n} \| x^n x^n \|

\displaystyle = \frac{1}{2n} \| swy \dots wy s^{-1}t zw^{-1} \dots zw^{-1} t^{-1} \|

where the {wy} and {zw^{-1}} terms each appear {n} times. From (2) we see that conjugation by {w} does not affect the norm. Using this and the triangle inequality several times, we conclude that

\displaystyle \|x\| \leq \frac{1}{2n} ( \|s\| + n \|y\| + \| s^{-1} t\| + n \|z\| + \|t^{-1} \| ),

and the claim (3) follows by sending {n \rightarrow \infty}.

The following special case of (3) will be of particular interest. Let {x,y \in G}, and for any integers {m,k}, define the quantity

\displaystyle f(m,k) := \| x^m [x,y]^k \|.

Observe that {x^m [x,y]^k} is conjugate to both {x (x^{m-1} [x,y]^k)} and to {(y^{-1} x^m [x,y]^{k-1} xy) x^{-1}}, hence by (3) one has

\displaystyle \| x^m [x,y]^k \| \leq \frac{1}{2} ( \| x^{m-1} [x,y]^k \| + \| y^{-1} x^{m} [x,y]^{k-1} xy \|)

which by (2) leads to the recursive inequality

\displaystyle f(m,k) \leq \frac{1}{2} (f(m-1,k) + f(m+1,k-1)).

We can write this in probabilistic notation as

\displaystyle f(m,k) \leq {\bf E} f( (m,k) + X )

where {X} is a random vector that takes the values {(-1,0)} and {(1,-1)} with probability {1/2} each. Iterating this, we conclude in particular that for any large natural number {n}, one has

\displaystyle f(0,n) \leq {\bf E} f( Z )

where {Z := (0,n) + X_1 + \dots + X_{2n}} and {X_1,\dots,X_{2n}} are iid copies of {X}. We can write {Z = (1,-1/2) (Y_1 + \dots + Y_{2n})} where Y_1,\dots,Y_{2n} = \pm 1 are iid signs.  By the triangle inequality, we thus have

\displaystyle f( Z ) \leq |Y_1+\dots+Y_{2n}| (\|x\| + \frac{1}{2} \| [x,y] \|),

noting that Y_1+\dots+Y_{2n} is an even integer.  On the other hand, Y_1+\dots+Y_{2n} has mean zero and variance 2n, hence by Cauchy-Schwarz

\displaystyle f(0,n) \leq \sqrt{2n}( \|x\| + \frac{1}{2} \| [x,y] \|).

But by (1), the left-hand side is equal to {n \| [x,y]\|}. Dividing by {n} and then sending {n \rightarrow \infty}, we obtain the claim. \Box

The above theorem reduces such seminorms to abelian groups. It is easy to see from (1) that any torsion element of such groups has zero seminorm, so we can in fact restrict to torsion-free groups, which we now write using additive notation {G = (G,+)}, thus for instance {\| nx \| = |n| \|x\|} for {n \in {\bf Z}}. We think of {G} as a {{\bf Z}}-module. One can then extend the seminorm to the associated {{\bf Q}}-vector space {G \otimes_{\bf Z} {\bf Q}} by the formula {\|\frac{a}{b} x\| := \frac{a}{b} \|x\|}, and then to the associated {{\bf R}}-vector space {G \otimes_{\bf Z} {\bf R}} by continuity, at which point it becomes a genuine seminorm (provided we have ensured the symmetry condition {\|x\| = \|x^{-1}\|}). Conversely, any seminorm on {G \otimes_{\bf Z} {\bf R}} induces a seminorm on {G}. (These arguments also appear in this paper of Khare and Rajaratnam.)

 

From Tim Gowers, I hear the good news that the editing process of the Princeton Companion to Mathematics is finally nearing completion. It therefore seems like a good time to resume my own series of Companion articles, while there is still time to correct any errors.

I’ll start today with my article on “Function spaces“. Just as the analysis of numerical quantities relies heavily on the concept of magnitude or absolute value to measure the size of such quantities, or the extent to which two such quantities are close to each other, the analysis of functions relies on the concept of a norm to measure various “sizes” of such functions, as well as the extent to which two functions resemble to each other. But while numbers mainly have just one notion of magnitude (not counting the p-adic valuations, which are of importance in number theory), functions have a wide variety of such magnitudes, such as “height” (L^\infty or C^0 norm), “mass” (L^1 norm), “mean square” or “energy” (L^2 or H^1 norms), “slope” (Lipschitz or C^1 norms), and so forth. In modern mathematics, we use the framework of function spaces to understand the properties of functions and their magnitudes; they provide a precise and rigorous way to formalise such “fuzzy” notions as a function being tall, thin, flat, smooth, oscillating, etc. In this article I focus primarily on the analytic aspects of these function spaces (inequalities, interpolation, etc.), leaving aside the algebraic aspects or the connections with mathematical physics.

The Companion has several short articles describing specific landmark achievements in mathematics. For instance, here is Peter Cameron‘s short article on “Gödel’s theorem“, on what is arguably one of the most popularised (and most misunderstood) theorems in all of mathematics.

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