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As readers of this blog are no doubt aware, I (in conjunction with Tim Gowers and many others) have been working collaboratively on a mathematical project.  To do this, we have been jury-rigging together a wide variety of online tools for this, including at least two blogs, a wiki, some online spreadsheets, and good old-fashioned email, together with offline tools such as Maple, LaTeX, C, and other programming languages and packages.  (To a lesser extent, I also rely this sort of mish-mash of semi-compatible online and offline software packages in my more normal mathematical collaborations, though polymath1 has been particularly chaotic in this regard.)

While this has been working reasonably well so far, the mix of all the various tools has been somewhat clunky, to put it charitably, and it would be good to have a more integrated online framework to do all of these things seamlessly; currently there seem to be software that achieves various subsets of what one would need for this, but not all.  (This point has also recently been made at the Secret Blogging Seminar.)

Yesterday, though, Google Australia unveiled a new collaborative software platform called “Google Wave” which incorporates many of these features already, and looks flexible enough to incorporate them all eventually.  (Full disclosure: my brother is one of the software engineers for this project.)  It’s nowhere near ready for release yet – it’s still in the development phase – but with the right type of support for things like LaTeX, this could be an extremely useful platform for mathematical collaboration (including the more traditional type of collaboration with just a handful of authors).

There is a demo for the product below.  It’s 80 minutes long, and aimed more at software developers than at end users, but I found it quite interesting, and worth watching through to the end:

[Update, May 30: Apparently a LaTeX renderer is already being developed as an API extension to Google Wave; here is a very preliminary screenshot.  Also, a shorter explanation of what Google Wave is and does can be found here. ]

[Update, Jun 7: Another review can be found here.]

[This post should have appeared several months ago, but I didn't have a link to the newsletter at the time, and I subsequently forgot about it until now.  -T.]

Last year, Emmanuel Candés and I were two of the recipients of the 2008 IEEE Information Theory Society Paper Award, for our paper “Near-optimal signal recovery from random projections: universal encoding strategies?” published in IEEE Inf. Thy..  (The other recipient is David Donoho, for the closely related paper “Compressed sensing” in the same journal.)  These papers helped initiate the modern subject of compressed sensing, which I have talked about earlier on this blog, although of course they also built upon a number of important precursor results in signal recovery, high-dimensional geometry, Fourier analysis, linear programming, and probability.  As part of our response to this award, Emmanuel and I wrote a short piece commenting on these developments, entitled “Reflections on compressed sensing“, which appears in the Dec 2008 issue of the IEEE Information Theory newsletter.  In it we place our results in the context of these precursor results, and also mention some of the many active directions (theoretical, numerical, and applied) that compressed sensing is now developing in.

Now that the quarter is nearing an end, I’m returning to the half of the polymath1 project hosted here, which focussed on computing density Hales-Jewett numbers and related quantities.  The purpose of this thread is to try to organise the task of actually writing up the results that we already have; as this is a metathread, I don’t think we need to number the comments as in the research threads.

To start the ball rolling, I have put up a proposed outline of the paper on the wiki.   At present, everything in there is negotiable: title, abstract, introduction, and choice and ordering of sections.  I suppose we could start by trying to get some consensus as to what should or should not go into this paper, how to organise it, what notational conventions to use, whether the paper is too big or too small, and so forth.  Once there is some reasonable consensus, I will try creating some TeX files for the individual sections (much as is already being done with the first polymath1 paper) and get different contributors working on different sections (presumably we will be able to coordinate all this through this thread).   This, like everything else in the polymath1 project, will be an experiment, with the rules made up as we go along; presumably once we get started it will become clearer what kind of collaborative writing frameworks work well, and which ones do not.

A fundamental characteristic of many mathematical spaces (e.g. vector spaces, metric spaces, topological spaces, etc.) is their dimension, which measures the “complexity” or “degrees of freedom” inherent in the space. There is no single notion of dimension; instead, there are a variety of different versions of this concept, with different versions being suitable for different classes of mathematical spaces. Typically, a single mathematical object may have several subtly different notions of dimension that one can place on it, which will be related to each other, and which will often agree with each other in “non-pathological” cases, but can also deviate from each other in many other situations. For instance:

  • One can define the dimension of a space {X} by seeing how it compares to some standard reference spaces, such as {{\bf R}^n} or {{\bf C}^n}; one may view a space as having dimension {n} if it can be (locally or globally) identified with a standard {n}-dimensional space. The dimension of a vector space or a manifold can be defined in this fashion.
  • Another way to define dimension of a space {X} is as the largest number of “independent” objects one can place inside that space; this can be used to give an alternate notion of dimension for a vector space, or of an algebraic variety, as well as the closely related notion of the transcendence degree of a field. The concept of VC dimension in machine learning also broadly falls into this category.
  • One can also try to define dimension inductively, for instance declaring a space {X} to be {n}-dimensional if it can be “separated” somehow by an {n-1}-dimensional object; thus an {n}-dimensional object will tend to have “maximal chains” of sub-objects of length {n} (or {n+1}, depending on how one initialises the chain and how one defines length). This can give a notion of dimension for a topological space or a commutative ring.

The notions of dimension as defined above tend to necessarily take values in the natural numbers (or the cardinal numbers); there is no such space as {{\bf R}^{\sqrt{2}}}, for instance, nor can one talk about a basis consisting of {\pi} linearly independent elements, or a chain of maximal ideals of length {e}. There is however a somewhat different approach to the concept of dimension which makes no distinction between integer and non-integer dimensions, and is suitable for studying “rough” sets such as fractals. The starting point is to observe that in the {d}-dimensional space {{\bf R}^d}, the volume {V} of a ball of radius {R} grows like {R^d}, thus giving the following heuristic relationship

\displaystyle  \frac{\log V}{\log R} \approx d \ \ \ \ \ (1)

between volume, scale, and dimension. Formalising this heuristic leads to a number of useful notions of dimension for subsets of {{\bf R}^n} (or more generally, for metric spaces), including (upper and lower) Minkowski dimension (also known as box-packing dimension or Minkowski-Bougliand dimension), and Hausdorff dimension.

[In {K}-theory, it is also convenient to work with ``virtual" vector spaces or vector bundles, such as formal differences of such spaces, and which may therefore have a negative dimension; but as far as I am aware there is no connection between this notion of dimension and the metric ones given here.]

Minkowski dimension can either be defined externally (relating the external volume of {\delta}-neighbourhoods of a set {E} to the scale {\delta}) or internally (relating the internal {\delta}-entropy of {E} to the scale). Hausdorff dimension is defined internally by first introducing the {d}-dimensional Hausdorff measure of a set {E} for any parameter {0 \leq d < \infty}, which generalises the familiar notions of length, area, and volume to non-integer dimensions, or to rough sets, and is of interest in its own right. Hausdorff dimension has a lengthier definition than its Minkowski counterpart, but is more robust with respect to operations such as countable unions, and is generally accepted as the “standard” notion of dimension in metric spaces. We will compare these concepts against each other later in these notes.

One use of the notion of dimension is to create finer distinctions between various types of “small” subsets of spaces such as {{\bf R}^n}, beyond what can be achieved by the usual Lebesgue measure (or Baire category). For instance, a point, line, and plane in {{\bf R}^3} all have zero measure with respect to three-dimensional Lebesgue measure (and are nowhere dense), but of course have different dimensions ({0}, {1}, and {2} respectively). (The Kakeya set conjecture, discussed recently on this blog, offers another good example.) This can be used to clarify the nature of various singularities, such as that arising from non-smooth solutions to PDE; a function which is non-smooth on a set of large Hausdorff dimension can be considered less smooth than one which is non-smooth on a set of small Hausdorff dimension, even if both are smooth almost everywhere. While many properties of the singular set of such a function are worth studying (e.g. their rectifiability), understanding their dimension is often an important starting point. The interplay between these types of concepts is the subject of geometric measure theory.

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In this post I would like to make some technical notes on a standard reduction used in the (Euclidean, maximal) Kakeya problem, known as the two ends reduction. This reduction (which takes advantage of the approximate scale-invariance of the Kakeya problem) was introduced by Wolff, and has since been used many times, both for the Kakeya problem and in other similar problems (e.g. by Jim Wright and myself to study curved Radon-like transforms). I was asked about it recently, so I thought I would describe the trick here. As an application I give a proof of the {d=\frac{n+1}{2}} case of the Kakeya maximal conjecture.

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Below the fold is a version of my talk “Recent progress on the Kakeya conjecture” that I gave at the Fefferman conference.

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In a previous post, we discussed the Szemerédi regularity lemma, and how a given graph could be regularised by partitioning the vertex set into random neighbourhoods. More precisely, we gave a proof of

Lemma 1 (Regularity lemma via random neighbourhoods) Let {\varepsilon > 0}. Then there exists integers {M_1,\ldots,M_m} with the following property: whenever {G = (V,E)} be a graph on finitely many vertices, if one selects one of the integers {M_r} at random from {M_1,\ldots,M_m}, then selects {M_r} vertices {v_1,\ldots,v_{M_r} \in V} uniformly from {V} at random, then the {2^{M_r}} vertex cells {V^{M_r}_1,\ldots,V^{M_r}_{2^{M_r}}} (some of which can be empty) generated by the vertex neighbourhoods {A_t := \{ v \in V: (v,v_t) \in E \}} for {1 \leq t \leq M_r}, will obey the regularity property

\displaystyle  \sum_{(V_i,V_j) \hbox{ not } \varepsilon-\hbox{regular}} |V_i| |V_j| \leq \varepsilon |V|^2 \ \ \ \ \ (1)

with probability at least {1-O(\varepsilon)}, where the sum is over all pairs {1 \leq i \leq j \leq k} for which {G} is not {\varepsilon}-regular between {V_i} and {V_j}. [Recall that a pair {(V_i,V_j)} is {\varepsilon}-regular for {G} if one has

\displaystyle  |d( A, B ) - d( V_i, V_j )| \leq \varepsilon

for any {A \subset V_i} and {B \subset V_j} with {|A| \geq \varepsilon |V_i|, |B| \geq \varepsilon |V_j|}, where {d(A,B) := |E \cap (A \times B)|/|A| |B|} is the density of edges between {A} and {B}.]

The proof was a combinatorial one, based on the standard energy increment argument.

In this post I would like to discuss an alternate approach to the regularity lemma, which is an infinitary approach passing through a graph-theoretic version of the Furstenberg correspondence principle (mentioned briefly in this earlier post of mine). While this approach superficially looks quite different from the combinatorial approach, it in fact uses many of the same ingredients, most notably a reliance on random neighbourhoods to regularise the graph. This approach was introduced by myself back in 2006, and used by Austin and by Austin and myself to establish some property testing results for hypergraphs; more recently, a closely related infinitary hypergraph removal lemma developed in the 2006 paper was also used by Austin to give new proofs of the multidimensional Szemeredi theorem and of the density Hales-Jewett theorem (the latter being a spinoff of the polymath1 project).

For various technical reasons we will not be able to use the correspondence principle to recover Lemma 1 in its full strength; instead, we will establish the following slightly weaker variant.

Lemma 2 (Regularity lemma via random neighbourhoods, weak version) Let {\varepsilon > 0}. Then there exist an integer {M_*} with the following property: whenever {G = (V,E)} be a graph on finitely many vertices, there exists {1 \leq M \leq M_*} such that if one selects {M} vertices {v_1,\ldots,v_{M} \in V} uniformly from {V} at random, then the {2^{M}} vertex cells {V^{M}_1,\ldots,V^{M}_{2^{M}}} generated by the vertex neighbourhoods {A_t := \{ v \in V: (v,v_t) \in E \}} for {1 \leq t \leq M}, will obey the regularity property (1) with probability at least {1-\varepsilon}.

Roughly speaking, Lemma 1 asserts that one can regularise a large graph {G} with high probability by using {M_r} random neighbourhoods, where {M_r} is chosen at random from one of a number of choices {M_1,\ldots,M_m}; in contrast, the weaker Lemma 2 asserts that one can regularise a large graph {G} with high probability by using some integer {M} from {1,\ldots,M_*}, but the exact choice of {M} depends on {G}, and it is not guaranteed that a randomly chosen {M} will be likely to work. While Lemma 2 is strictly weaker than Lemma 1, it still implies the (weighted) Szemerédi regularity lemma (Lemma 2 from the previous post).

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I am currently at Princeton for the conference “The power of Analysis” honouring Charlie Fefferman‘s 60th birthday. I myself gave a talk at this conference entitled “Recent progress on the Kakeya conjecture”; I plan to post a version of this talk on this blog shortly.

But one nice thing about attending these sorts of conferences is that one can also learn some neat mathematical facts, and I wanted to show two such small gems here; neither is particularly deep, but I found both of them cute. The first one, which I learned from my former student Soonsik Kwon, is a unified way to view the mean, median, and mode of a probability distribution {\mu} on the real line. If one assumes that this is a continuous distribution {\mu = f(x)\ dx} for some smooth, rapidly decreasing function {f: {\mathbb R} \rightarrow {\mathbb R}^+} with {\int_{\mathbb R} f(x)\ dx = 1}, then the mean is the value of {x_0} that minimises the second moment

\displaystyle  \int_{\mathbb R} |x-x_0|^2 f(x)\ dx,

the median is the value of {x_0} that minimises the first moment

\displaystyle  \int_{\mathbb R} |x-x_0| f(x)\ dx,

and the mode is the value of {x_0} that maximises the “pseudo-negative first moment”

\displaystyle  \int_{\mathbb R} \delta(x-x_0) f(x)\ dx.

(Note that the Dirac delta function {\delta(x-x_0)} has the same scaling as {|x-x_0|^{-1}}, hence my terminology “pseudo-negative first moment”.)

The other fact, which I learned from my former classmate Diego Córdoba (and used in a joint paper of Diego with Antonio Córdoba), is a pointwise inequality

\displaystyle  |\nabla|^\alpha ( f^2 )(x) \leq 2 f(x) |\nabla|^\alpha f(x)

for the fractional differentiation operators {|\nabla|^\alpha} applied to a sufficiently nice real-valued function {f: {\mathbb R}^d \rightarrow {\mathbb R}} (e.g. Schwartz class will do), in any dimension {d} and for any {0 \leq \alpha \leq 1}; this should be compared with the product rule {\nabla (f^2 ) = 2 f \nabla f}.

The proof is as follows. By a limiting argument we may assume that {0 < \alpha < 1}. In this case, there is a formula

\displaystyle  |\nabla|^\alpha f(x) = c(\alpha) \int_{{\mathbb R}^d} \frac{f(x)-f(y)}{|x-y|^{d+\alpha}}\ dy

for some explicit constant {c(\alpha) > 0} (this can be seen by computations similar to those in my recent lecture notes on distributions, or by analytically continuing such computations; see also Stein’s “Singular integrals and differentiability properties of functions”). Using this formula, one soon sees that

\displaystyle  2 f(x) |\nabla|^\alpha f(x) - |\nabla|^\alpha ( f^2 )(x) = c(\alpha) \int_{{\mathbb R}^d} \frac{|f(x)-f(y)|^2}{|x-y|^{d+\alpha}}\ dy

and the claim follows.

In a recent Cabinet meeting, President Obama called for a $100 million spending cut in 90 days from the various federal departments as a sign of budget discipline.  While this is nominally quite a large number, it was pointed out correctly by many people that this was in fact a negligible fraction of the total federal budget; for instance, Greg Mankiw noted that the cut was comparable to a family with an annual spending of $100,000 and a deficit of $34,000 deciding on a spending cut of $3.  (Of course, this is by no means the only budgetary initiative being proposed by the administration; just today, for instance, a change in the taxation law for offshore income was proposed which could potentially raise about $210 billion over the next ten years, or about $630 a year with the above scaling, though it is not clear yet how feasible or effective this change would be.)

I thought that this sort of rescaling (converting $100 million to $3) was actually a rather good way of comprehending the vast amounts of money in the federal budget: we are not so adept at distinguishing easily between $1 million, $1 billion, and $1 trillion, but we are fairly good at grasping the distinction between $0.03, $30, and $30,000.  So I decided to rescale (selected items in) the federal budget, together with some related numbers for comparison, by this ratio 100 million:3, to put various figures in perspective.

This is certainly not an advanced application of mathematics by any means, but I still found the results to be instructive.  The same rescaling puts the entire population of the US at about nine – the size of a (large) family – which is of course consistent with the goal of putting the federal budget roughly on the scale of a family budget (bearing in mind, of course, that the federal government is only about a fifth of the entire US economy, so one might perhaps view the government as being roughly analogous to the “heads of household” of this large family).  The implied (horizontal) length rescaling of \sqrt{100 \hbox{million}:3} \approx 5770 is roughly comparable to the scaling used in Dubai’s “The World” (which is not a coincidence, if you think about it; the purpose of both rescalings is to map global scales to human scales).  Perhaps readers may wish to contribute additional rescaled statistics of interest to those given below.

One caveat: the rescaling used here does create some noticeable distortions in other dimensional quantities.  For example, if one rescales length by the implied factor of \approx 5770, but leaves time unrescaled (so that a fiscal year remains a fiscal year), then this will rescale all speeds by a factor of \approx 5770 also.  For instance, the circumference of the Earth has been rescaled to a manageable-looking 6.9 kilometres (4.3 miles), but the speed of, say, a commercial airliner (typically about 900 km/hr, or 550 mi/hr) is rescaled also, to a mere 150 metres (or 160 yards) or so per hour, i.e. two or three meters or yards per minute – about the speed of a tortoise.

All amounts here are rounded to three significant figures (and in some cases, the precision may be worse than this).   I am using here the FY2008 budget instead of the 2009 or 2010 one, as the data is more finalised; as such, the numbers here are slightly different from those of Mankiw.  (For instance, the 2010 budget includes the expenditures for Iraq & Afghanistan, whereas in previous budgets these were appropriated separately.)  I have tried to locate the most official and accurate statistics whenever possible, but corrections and better links are of course welcome.

FY 2008 budget:

  • Total revenue: $75,700
    • Individual income taxes: $34,400
    • Social security & other payroll taxes: $27,000
  • Total spending: $89,500
    • Net mandatory spending: $48,000
      • Medicare, Medicaid, and SCHIP: $20,500
      • Social Security: $18,400
    • Net interest: $7,470
    • Net discretionary spending: $34,000
      • Department of Defense: $14,300
        • DARPA: $89
      • Global War on Terror: $4,350
      • Department of Education: $1,680
      • Department of Energy: $729
      • NASA: $519
      • Net earmarks: $495
      • NSF: $180
        • Maths & Physical Sciences: $37.50
  • Budget deficit: $13,800
  • Additional appropriations (not included in regular budget)
    • Iraq & Afghanistan: $5,640
  • Spending cuts within 90 days of Apr 20, 2009: $3
  • Air force NY flyover “photo shoot”, Apr 27, 2009: $0.01
  • Additional spending cuts for FY2010, proposed May 7, 2009: $510
  • Projected annual revenue from proposed offshore tax code change: $630

Other figures (for comparison)

  • National debt held by public 2008: $174,000
    • Held by foreign/international owners 2008: $85,900
  • National debt held by government agencies, 2008: $126,000
    • Held by OASDI (aka (cumulative) “Social Security Surplus”): $64,500
  • National GDP 2008: $427,000
    • National population 2008: 9
    • GDP per capita 2008: $47,000
    • Land mass: 0.27 sq km (0.1 sq mi, or 68 acres)
  • World GDP 2008: $1,680,000
    • World population 2008: 204
    • GDP per capita 2008 (PPP): $10,400
    • Land mass: 4.47 sq km (1.73 sq mi)
  • World’s richest (non-rescaled) person: Bill Gates (net worth $1,200, March 2009)
  • 2008/2009 Bailout package (TARP): $21,000 (maximum)
    • Amount spent by Dec 2008: $7,410
    • AIG bailout from TARP: $1,200
      • AIG Federal Reserve credit line: $4,320
      • AIG bonuses in 2009 Q1: $4.95
    • GM & Chrysler loans: $552
  • 2009/2010 Stimulus package (ARRA):  $23,600
    • Tax cuts (spread out over 10 years): $8,640
    • State and local fiscal relief: $4,320
    • Education: $3,000
      • “Race to the top” education fund: $150
    • Investments in scientific research: $645
    • Pandemic flu preparedness: $1.50 (was initially $27, after being dropped from FY2008 and FY2009 budgets)
      • Additional request after A(H1N1) (“swine flu”) outbreak, Apr 28: $45
    • Volcano monitoring: $0.46 (erroneously reported as $5.20)
    • Salt marsh mouse preservation (aka “Pelosi’s mouse“): $0.00 (erroneously reported as $0.90)
  • Market capitalization of NYSE
    • May 2008 (peak): $506,000
    • March 2009: $258,000
    • Largest company by market cap: Exxon Mobil (approx $10,000, Apr 2009)
  • Value of US housing stock (2007): $545,760
    • Total value of outstanding mortgages (2008): $330,000
      • Total value of sub-prime mortgages outstanding (2007 est): $39,000
      • Total value of mortgage-backed securities (2008): $267,000
  • Credit default swap contracts, total notional value:
  • US trade balance (2007)

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