## Tuesday, July 29, 2014

### The average rank of elliptic curves

It's about time I should demonstrate the utility of the code I've written - the aim of the game for my GSoC project, after all, is to provide a new suite of tools with which to conduct mathematical research.

First some background. Given an elliptic curve $E$ specified by equation $y^2 = x^3 + Ax + B$ for integers $A$ and $B$, one of the questions we can ask is: what is the average rank of $E$ as $A$ and $B$ are allowed to vary? Because there are an infinite number of choices of $A$ and $B$, we need to formulate this question a bit more carefully. To this end, let us define the height of $E$ to be
$$h(E) = \max\{|A|^3,|B|^2\}$$
[Aside: The height essentially measures the size of the coefficients of $E$ and is thus a fairly decent measure of the arithmetic complexity of the curve. We need the 3rd and 2nd powers in order to make the height function scale appropriately with the curve's discriminant.]

We can then ask: what is the limiting value of the average rank of all curves up to height $X$, as $X$ gets bigger and bigger? Is it infinity? Is it 0? Is it some non-trivial positive value? Does the limit even exist? It's possible that the average rank, as a function of $X$ could oscillate about and never stabilize.

There are strong heuristic arguments suggesting that the answer should be exactly $\frac{1}{2}$. Specifically, as the height bound $X$ gets very large, half of all curves should have rank 0, half should have rank 1, and a negligible proportion should have rank 2 or greater.

Even as recently as five years ago this there we knew nothing concrete unconditionally about average curve rank. There are some results by BrumerHeath-Brown and Young providing successively better upper bounds on the average rank of curves ordered by height (2.3, 2 and $\frac{25}{14}$ respectively), but these results are contingent on the Riemann Hypothesis.

However, starting in 2011 Manjul Bhargava, together with Christopher Skinner and Arul Shankar, published a series of landmark papers (see here for a good expository slideshow, and here and here for two of the latest publications) proving unconditionally that average rank - that is, the limiting value of the average rank of all elliptic curves up to height $X$ - is bounded above by 0.885. A consequence of their work too is that at least 66% of all elliptic curves satisfy the rank part of the Birch and Swinnerton-Dyer Conjecture.

To a computationally-minded number theorist, the obvious question to ask is: Does the data support these results? I am by no means the first person to ask this question. Extensive databases of elliptic curves under various orderings have already been compiled, most notably those by Cremona (ordered by conductor) and Stein-Watkins (ordered essentially by discriminant). However, as yet no extensive tabulation of height-ordered elliptic curves has been carried out.

Here is a summarized table of elliptic curves with height at most 10000 - a total of 8638 curves, and the ranks thereof (all computations done in Sage, of course):

 Rank # Curves % 0 2988 34.59% 1 4307 49.86% 2 1286 14.89% 3 57 0.66% $\ge$4 0 0.00% Total: 8638

Thus the average rank of elliptic curves is 0.816 when the height bound is 10000. This is worrisome: the average is significantly different from the value of 0.5 we're hoping to see.

The situation gets even worse when we go up to height bound 100000:

 Rank # Curves % 0 19492 33.11% 1 28818 48.96% 2 9747 16.56% 3 801 1.36% $\ge$4 4 0.01% Total: 58862

This yields an average rank of 0.862 for height bound 100000. Bizarrely, the average rank is getting bigger, not smaller!

[Note: the fact that 0.862 is close to Bhargava's asymptotic bound of 0.885 is coincidental. Run the numbers for height 1 million, for example, and you get an average rank of 0.8854, which is bigger than the above asymptotic bound. Observationally, we see the average rank continue to increase as we push out to even larger height bounds beyond this.]

So what's the issue here? It turns out that a lot of the asymptotic statements we can make about elliptic curves are precisely that: asymptotic, and we don't yet have a good understanding of the associated rates of convergence. Elliptic curves, ornery beasts that they are, can seem quite different from their limiting behaviour when one only considers curves with small coefficients. We expect (hope?) that the average to eventually turn around and start to decrease back down to 0.5, but the exact point at which that happens is as yet unknown.

This is where I come in. One of the projects I've been working on (with Wei Ho, Jen Balakrishnan, Jamie Weigandt, Nathan Kaplan and William Stein) is to compute the average rank of elliptic curves for as large a height bound as possible, in the hope that we will get results a bit more reassuring than the above. The main steps of the project are thus:

1. Generate an ordered-by-height database of all elliptic curves up to some very large  height bound (currently 100 million; about 18.5 million curves);
2. Use every trick in the book to compute the ranks of said elliptic curves;
3. Compute the average of said ranks.
Steps 1 and 3 are easy. Step 2 is not. Determining the rank of an elliptic curve is a notoriously hard problem - no unconditional algorithm with known complexity currently exists - especially when you want to do it for millions of curves in a reasonable amount of time. Sage, for example, already has a rank() method attached to their EllipticCurve class; if you pass the right parameters to it, the method will utilize an array of approaches to get a value out that is (assuming standard conjectures) the curve's rank. However, its runtime for curves of height near 100 million is on the order of 20 seconds; set it loose on 18.5 million such curves and you're looking at a total computation time of about 10 CPU years.

Enter GSoC project stage left. At the expense of assuming the Generalized Riemann Hypothesis and the Birch and Swinnerton-Dyer Conjecture, we can use the zero sum rank bounding code I've been working on to quickly compute concrete upper bounds on an elliptic curve's rank. This approach has a couple of major advantages to it:
• It's fast. In the time it's taken me to write this post, I've computed rank bounds on 2.5 million curves.
• Runtime is essentially constant for any curve in the database; we don't have to worry about how the method scales with height or conductor. If we want to go up to larger height bounds at a later date, no problem.
As always, some Terms and Conditions apply. The rank bounding code only gives you an upper bound on the rank: if, for example, you run the code on a curve and get the number 4 back, there's no way to determine with this method if the curve's rank is 4, or if it is really some non-negative integer less than 4.

Note: there is an invariant attached to an elliptic curve called the root number which is efficiently computable, even for curves with large conductor (it took less than 20 minutes to compute the root number for all 18.5 million curves in our database). The root number is one of two values: -1 or +1; if it's -1 the curve has odd analytic rank, and if it's +1 the curve has even analytic rank. Assuming BSD we can therefore always easily tell if the curve's rank is even or odd. My GSoC rank estimation code takes the root number into account, so in the example above, a returned value of 4 tells us that the curve's true rank is one of three values: 0, 2 or 4.

Even better, if the returned value is 0 or 1, we know this must be the actual algebraic rank of the curve: firstly, there's no ambiguity as to what the analytic rank is - it has to the returned 0 or 1; secondly, the BSD conjecture has been proven in the rank 0 & 1 cases. Thus even though we are a priori only computing analytic rank upper bounds, for some proportion of curves we've found the actual algebraic rank.

[Note that the rank bounding code I've written is predicated on knowing that all nontrivial zeros of an elliptic curve $L$-function lie on the critical line, so we still have to assume the Generalized Riemann Hypothesis.]

Thus running the rank bound code on the entire database of curves is a very sensible first step, and it's what I'm currently doing. It's computationally cheap to do - on SageMathCloud, using a Delta value of 1.0, the runtime for a single curve is about 4 milliseconds. Moreover, for some non-negligible percentage of curves the bounds will be observably sharp - based on some trial runs I'm expecting about 20-30% of the computed bounds to be 0 or 1.

That's about 4 million curves for which we won't have to resort to much more expensive rank finding methods. Huge savings!

## Monday, July 21, 2014

### How to efficiently enumerate prime numbers

There's a part in my code that requires me to evaluate a certain sum
$$\sum_{p\text{ prime }< \,M} h_E(p)$$
where $h_E$ is a function related to specified elliptic curve that can be evaluated efficiently, and $M$ is a given bound that I know. That is, I need to evaluate the function h_E at all the prime numbers less than $t$, and then add all those values up.

The question I hope to address in this post is: how can we do this efficiently as $M$ gets bigger and bigger? Specifically, what is the best way to compute a sum over all prime numbers up to a given bound when that bound can be very large?

[For those who have read my previous posts (you can skip this paragraph if you haven't - it's not the main thrust of this post), what I want to compute is, for an elliptic curve $E/\mathbb{Q}$, the analytic rank bounding sum $\sum_{\gamma} \text{sinc}^2(\Delta \gamma)$ over the zeros of $L_E(s)$ for positive parameter $\Delta$; this requires us to evaluate the sum $\sum_{n < \exp(2\pi\Delta)} c_n\cdot(2\pi\Delta-\log n)$. Here the $c_n$  are the logarithmic derivative coefficients of the completed $L$-function of $E$. Crucially $c_n = 0$ whenever $n$ isn't a prime power, and we can lump together all the terms coming from the same prime; we can therefore express the sum in the form you see in the first paragraph.]

As with so many things in mathematical programming, there is a simple but inefficient way to do this, and then there are more complicated and ugly ways that will be much faster. And as has been the case with other aspects of my code, I've initially gone with the first option to make sure that my code is mathematically correct, and then gone back later and reworked the relevant methods in an attempt to speed things up.

### METHOD 1: SUCCINCT BUT STUPID

Here's a Python function that will evaluate the sum over primes. The function takes two inputs: a function $h_E$ and an integer $M$, and returns a value equal to the sum of $h_E(p)$ for all primes less than $M$. We're assuming here that the primality testing function is_prime() is predefined.

As you can see, we can achieve the desired outcome in a whopping six lines of code. Nothing mysterious going on here: we simply iterate over all integers less than our bound and test each one for primality; if that integer is prime, then we evaluate the function h_E at that integer and add the result to y. The variable y is then returned at the end.

Why is this a bad way to evaluate the sum? Because there are far more composite integers than there are primes. According to the prime number theorem, the proportion of integers up to $M$ that are prime is approximately $\frac{1}{\log M}$. For my code I want to compute with bounds in the order of $M = e^{8\pi} \sim 10^{11}$; the proportion of integers that are prime up to this bound value is correspondingly about $\frac{1}{8\pi} \sim 0.04$. That is, 96% of the integers we iterate over aren't prime, and we end up throwing that cycle away.

Just how inefficient this method is of course depends on how quickly we can evaluate the primality testing function is_prime(). The best known deterministic primality testing algorithm has running time that scales with (at most) the 6th power of $\log n$, where $n$ is the number being tested. This places primality testing in a class of algorithms called Polynomial Time Complexity Algorithms, which means the runtime of the function scales relatively well with the size of the input. However, what kills us here is the sheer number of times we have to call is_prime() - on all integers up to our bound $M$ - so even if it ran in constant time the prime_sum() function's running time is going to scale with the magnitude of $M$.

### METHOD 2: SKIP THOSE $n$ WE KNOW ARE COMPOSITE

We can speed things up considerably by noting that apart from 2, all prime numbers are odd. We are therefore wasting a huge amount of time running primality tests on integers that we know a priori are composite. Assuming is_prime() takes a similar time to execute than our coefficient function h_E(), we could therefore roughly halve the runtime of the prime sum function by skipping the even integers and just checking odd numbers for primality.

We can go further. Apart from 2 and 3, all primes yield a remainder of 1 or 5 when you divide them by 6 (because all primes except for 2 are 1 (modulo 2) and all primes except for 3 are 1 or 2 (modulo 3)). We can therefore skip all integers that are 0, 2, 3 or 4 modulo 6; this means we only have to check for primality on only one third of all the integers less than $M$.

Here's a second version of the prime_sum() function that does this:

Of course we could go even further with the technique by looking at remainders modulo $p$ for more primes $p$ and combining the results: for example, all primes outside of 2, 3 and 5 can only have a remainder of 7, 11, 13, 17, 19, 23 or 29 modulo 30. However, the further you go the more special cases you need to consider, and the uglier your code becomes; as you can see, just looking at cases modulo 6 requires us to write a function about three times as long as previously. This method therefore will only be able to take us so far before the code we'd need to write would become too unwieldy for practicality.

### METHOD 3: PRIME SIEVING...

This second prime_sum() version is a rudimentary example of a technique called prime sieving. The idea is to use quick computations to eliminate a large percentage of integers from consideration in a way that doesn't involve direct primality testing, since this is computationally expensive. Sieving techniques are an entire field of research in their own right, so I thought I'd just give as example one of the most famous methods: the Sieve of Eratosthenes (named after the ancient Greek mathematician who is thought to first come up with the idea). This takes as input a positive bound $M$ and returns a list of all prime numbers less than $M$. The method goes as follows:
1. Start with a list of boolean flags indexed by the numbers 2 through $M$, and set all of them to True.
2. Starting at the beginning of the list, let $i$ be the index of the first True entry. Set all entries at indices a multiples of $i$ to False.
3. Repeat step 2 until the first True entry is at index $> \sqrt{M}$.
4. Return a list of all integers $i$ such that the entry at index $i$ is True.
This is definitely a case where a (moving) picture is worth a thousand words:
 A good graphic representation of the Sieve of Eratosthenes being used to generate all primes less than 121. Courtesy Wikipedia: "Sieve of Eratosthenes animation". Licensed under CC BY-SA 3.0 via Wikimedia Commons.
How and why does this work? By mathematical induction, at each iteration the index of the first True entry will always be prime. However any multiple thereof is by definition composite, so we can walk along the list and flag them as not prime. Wash, rinse, repeat. We can stop at $\sqrt{M}$, since all composite numbers at most $M$ in magnitude must have at least one prime factor at most $\sqrt{M}$ in size.

Here is a third version of our prime_sum() function that utilizes the Sieve of Eratosthenes:

Let's see how the three versions stack up against each other time-wise in the Sage terminal. I've saved the three functions in a file called prime_sum_functions.py, which I then import up front (if you want to do the same yourself, you'll need to import or define appropriate is_prime() and sqrt() functions at the top of the file). I've also defined a sample toy function h_E() and bound M:

sage: from prime_sum_functions import *
sage: def h_E(n): return sin(float(n))/float(n)
sage: M = 10000
sage: prime_sum_v1(h_E,M)
0.19365326958140347
sage: prime_sum_v2(h_E,M)
0.19365326958140347
sage: prime_sum_v3(h_E,M)
0.19365326958140347
sage: %timeit prime_sum_v1(h_E,M)
1 loops, best of 3: 363 ms per loop
sage: %timeit prime_sum_v2(h_E,M)
1 loops, best of 3: 206 ms per loop
sage: %timeit prime_sum_v3(h_E,M)
10 loops, best of 3: 86.8 ms per loop

Good news! All three functions (thankfully) produce the same result. And we see version 2 is about 1.8 times faster than version 1, while version 3 is four times as fast. These ratios remained roughly the same when I timed the functions on larger bounds, which indicates that the three versions have the same or similar asymptotic scaling - this should be expected, since no matter what we do we will always have to check something at each integer up to the bound.

### METHOD 4: ...AND BEYOND

It should be noted, however, that the Sieve of Eratosthenes as implemented above would be a terrible choice for my GSoC code. This is because in order to enumerate the primes up to $M$ we need to create a list in memory of size $M$. This isn't an issue when $M$ is small, but for my code I need $M \sim 10^{11}$; an array of booleans that size would take up about 12 gigabytes in memory, and any speedups we get from not having to check for primality would be completely obliterated by read/write slowdowns due to working with an array that size. In other words, while the Sieve of Eratosthenes has great time complexity, it has abysmal space complexity.

Thankfully, more memory-efficient sieving methods exist that drastically cut down the space requirements. The best of these - for example, the Sieve of Atkin - need about $\sqrt{M}$ space. For $M \sim 10^{11}$ this translates to only about 40 kilobytes; much more manageable.

Of course, there's always a downside: bleeding edge prime enumeration methods are finicky and intricate, and there are a plethora of ways to get it wrong when implementing them. At some point squeezing an extra epsilon of speedup from your code is no longer worth it in terms of the time and effort it will take to get there. For now, I've implemented a more optimized version of the second prime_sum() function in my code (where we skip over all integers that are obviously not prime), since for now that is my happy middle ground.  If I have time at the end of the project I will revisit the issue of efficient prime enumeration and try implement a more optimized sieving method, but that is a tomorrow problem.

## Monday, July 14, 2014

### Cythonize!

I'm at the stage where my code essentially works: it does everthing I initially set out to have it do, including computing the aforementioned zero sums for elliptic curve $L$-functions. However, the code is written in pure Python, and it is therefore not as fast as it could be.

This is often not a problem; Python is designed to be easy to read and maintain, and I'm hoping that the Python code I wrote is both of those. If we were just planning to run it on elliptic curves with small coefficients - for example, the curve represented by the equation $y^2=x^3-16x+16$ - this wouldn't be an issue. Curves with small coefficients have small conductors and thus few low-lying zeros near the central point, which allows us to run the zero sum code on them with small Delta parameter values. A small Delta value means the computation will finish very quickly regardless of how efficiently it's implemented, so it probably wouldn't be worth my while trying to optimize the code in that case.

To illustrate this point, here is the first most high-level, generic version of the method that computes the sum $\sum_{\gamma} \text{sinc}^2(\Delta \gamma)$ over the zeros of a given elliptic curve $L$-function (minus documentation):

[Of course, there's plenty going on in the background here. I have a separate method, self.cn() which computes the logarithmic derivative coefficients, and I call the SciPy function spence() to compute the part of the sum that comes from the Fourier transform of the digamma function $\frac{\Gamma^{\prime}}{\Gamma}$. Nevertheless, the code is simple and straightforward, and (hopefully) it's easy to follow the logic therein.]

However, the whole point of my GSoC project is to produce code that can be used for mathematical research; ultimately we want to push computations as far as we can and run the code on elliptic curves with large conductor, since curves with small conductor are already well-understood. Ergo, it's time I thought about going back over what I've written and seeing how I can speed it up.

There are two distinct ways to achieve speedups. The first is to rewrite the code more cleverly to eliminates unnecessary loops, coercions, function calls etc. Here is a second version I have written of the same function (still in Python):

The major change I've made between the two versions is improving how the sum involving the logarithmic derivative coefficients is computed - captured in the variable y. In the first version, I simply iterated over all integers $n$ up to the bound $t$, calling the method self.cn() each time. However, the logarithmic derivative coefficient $c_n$ is zero whenever $n$ is not a prime power, and knowing its value for $n=p$ a prime allows us to efficiently compute its value for $p^k$ any power of that prime. It therefore makes sense to do everything "in-house": eliminate the method call to self.cn(), iterate only over primes, and compute the logarithmic derivative coefficients for all powers of a given prime together.

Let's see how the methods match up in terms of speed. Below we run the two versions of the zero sum method on the elliptic curve $E: y^2=x^3-16x+16$, which is a rank 1 curve of conductor 37:

sage: import sage.lfunctions.zero_sums as zero_sums
sage: ZS = zero_sums.LFunctionZeroSum(EllipticCurve([-16,16]))
sage: ZS._zerosum_sincsquared(Delta=1)
1.01038406984
sage: ZS._zerosum_sincsquared_fast(Delta=1)
1.01038406984
sage: %timeit ZS._zerosum_sincsquared(Delta=1)
10 loops, best of 3: 20.5 ms per loop
sage: %timeit ZS._zerosum_sincsquared_fast(Delta=1)
100 loops, best of 3: 3.46 ms per loop

That's about a sixfold speedup we've achieved, just by reworking the section of the code that computes the $c_n$ sum.

The downside, of course, is that the code in method version 2 is more complicated - and thus less readable - than that in version 1. This is often the case in software development: you can write code that is elegant and easy to read but slow, or you can write code that is fast but horribly complicated and difficult to maintain. And when it comes to mathematical programming, unfortunately, sometimes the necessity for speed trumps readability.

The second major way to achieve speedups is to abandon pure Python and switch to a more low-level language. I could theoretically take my code and rewrite it in C, for example; if done relatively intelligently I'm sure the resulting code would blow what I have here out the water in terms of speed. However, I have no experience writing C code, and even if I did getting the code to interface with the rest of the Sage codebase would be a major headache.

Thankfully there is a happy middle ground: Cython. Cython is a programming language - technically a superset of Python - that allows direct interfacing with C and C++ data types and structures. Code written in Cython can be as fast as C code and nearly as readable as pure Python. Crucially, because it's so similar to Python it doesn't require rewriting all my code from scratch. And Sage already knows how to deal with Cython, so there aren't any compatibility issues there.

I am therefore in the process of doing exactly that: rewriting my code in Cython. Mostly this is just a cut-and-paste job and is pleasingly hassle-free; however, in order to achieve the desired speedups, the bottleneck methods - such as our $\text{sinc}^2$ zero sum method above - must be modified to make use of C data types.

Here is the third, most recent version of the _zerosum_sincsquared() method for our zero sum class, this time written in Cython:

Definitely longer and uglier. I now must declare my (C) variables up front; previously Python just created them on the fly, which is nice but slower than allocating memory space for the variables a priori. I've eliminated the use of complex arithmetic, so that everything can be done using C integer and float types. I still iterate over all primes up to the bound $t$; however now I deal with those primes that divide the conductor of $E$ (for which the associated summand is calculated slightly differently) beforehand, so that in the main loop I don't have to check at each point if my prime $p$ divides the conductor or not [This last check is expensive: the conductor $N$ can be very large - too large to cast as a $C$ long long even - so we would have to use slower Python or Sage data types to represent it. Better to get rid of the check altogether].

Let's see how this version holds up in a speed test. The Cython code has already been built into Sage and the class loaded into the global namespace, so I can just call it without having to attach or load any file:

sage: ZS = LFunctionZeroSum(EllipticCurve([-16,16]))
sage: ZS._zerosum_sincsquared_fast(Delta=1)
1.01038406984
sage: %timeit ZS._zerosum_sincsquared_fast(Delta=1)
1000 loops, best of 3: 1.72 ms per loop

The good news: the Cythonized version of the method produces the same output as the Python versions, and it's definitely faster. The bad news: the speedup is only about a factor of 2, which isn't hugely impressive given how much uglier the code is.

Why is this? Crucially, we still iterate over all integers up to the bound $t$, testing for primality at each step. This is very inefficient: most integers are not prime (in fact, asymptotically 0 percent of all positive integers are prime); we should be using sieving methods to eliminate primality testing at those integers which we know before checking are composite. For example, we should at the very least only ever iterate over the odd numbers beyond 3. That immediately halves the number of primality tests we have to do, and we should therefore get a comparable speedup if primality testing is what is dominating the runtime in this method.

This is therefore what I hope to implement next: rework the zero sum code yet again to incorporate prime sieving. This has applicability beyond just the $\text{sinc}^2$ method: all explicit formula-type sums at some point invoke summing over primes or prime powers, so having access to code that can do this quickly would be a huge boon.

## Wednesday, July 9, 2014

### Hat Operators!

Clearly I am working in the right field: a lot of what I'm currently doing involves taking Fourier transforms - typically denoted mathematically by a hat operator!
$$f \longmapsto \hat{f}$$

## Tuesday, July 8, 2014

### The Birch and Swinnerton-Dyer Conjecture and computing analytic rank

Let $E$ be an elliptic curve with $L$-function $L_E(s)$. Recall that Google Summer of Code project is to implement in Sage a method that allows us to compute $\sum_{\gamma} f(\Delta \gamma)$, where $\gamma$ ranges over the imaginary parts of the nontrivial zeros of $L_E$, $\Delta$ is a given positive parameter, and $f(x)$ is a specified symmetric continuous integrable function that is 1 at the origin. The value of this sum then bounds the analytic rank of $E$ - the number of zeros at the central point - from above, since we are summing $1$ with multipliticy $r_{an}$ in the sum, along with some other nonzero positive terms (that are hopefully small). See this post for more info on the method.

One immediate glaring issue here is that zeros that are close to the critical point will contribute values that are close to 1 in the sum, so the curve will then appear to have larger analytic rank than it actually does. An obvious question, then, is to ask: how close can the noncentral zeros get to the central point? Is there some way to show that they cannot be too close? If so, then we could figure out just how large of a $\Delta$ we would need to use in order to get a rank bound that is actually tight.

 The rank zero curve $y^2 = x^3 - x^3 - 7460362000712 x - 7842981500851012704$ has an extremely low-lying zero at $\gamma = 0.0256$ (and thus another one at $-0.0256$; as a result the zero sum looks like it's converging towards a value of 2 instead of the actual analytic rank of zero. In order to actually get a sum value out that's less than one we would have to use a $\Delta$ value of about 20; this is far beyond what's feasible due to the exponential dependence of the zero sum method on $\Delta$.

The good news is that there is hope in this regard; the nature of low-lying zeros for elliptic curve $L$-functions is actually the topic of my PhD dissertation (which I'm still working on, so I can't provide a link thereto just yet!). In order to understand how close the lowest zero can get to the central point we will need to talk a bit about the BSD Conjecture.

The Birch and Swinnerton-Dyer Conjecture is one of the two Clay Math Millenium Problems related to $L$-functions. The conjecture is comprised of two parts; the first part I mentioned briefly in this previous post. However, we can use the second part to gain insight into how good our zero sum based estimate for analytic rank will be.

Even though I've stated the first part of the BSD Conjecture before, for completeness I'll reproduce the full conjecture here. Let $E$ be an elliptic curve defined over the rational numbers, e.g. a curve represented by the equation $y^2 = x^3 + Ax + B$ for some integers $A$ and $B$ such that $4A^3+27B^2 \ne 0$. Let $E(\mathbb{Q})$ be the group of rational points on the elliptic curve, and let $r_{al}$ be the algebraic rank of $E(\mathbb{Q})$. Let $L_E(s)$ be the $L$-function attached to $E$, and let $L_E(1+s) = s^{r_{an}}\left[a_0 + a_1 s + \ldots\right]$ be the Taylor expansion of $L_E(s)$ about $s=1$ such that the leading coefficient $a_0$ is nonzero; $r_{an}$ is called the analytic rank of $E$ (see here for more details on all of the above objects). The first part of the BSD conjecture asserts that $r_{al}=r_{an}$; that is, the order of vanishing of the $L$-function about the central point is exactly equal to the number of free generators in the group of rational points on $E$.

The second part of the conjecture asserts that we actually know the exact value of that leading coefficient $a_0$ in terms of other invariants of $E$. Specifically:
$$a_0 = \frac{\Omega_E\cdot\text{Reg}_E\cdot\prod_p c_p\cdot\#\text{Ш}(E/\mathbb{Q})}{(\#E_{\text{Tor}}(\mathbb{Q}))^2}.$$

Fear not if you have no idea what any of these quantities are. They are all things that we know how to compute - or at least estimate in size. I provide below brief descriptions of each of these quantities; however, feel free to skip this part. It suffices to know that we have a formula for computing the exact value of that leading coefficient $a_0$.
1. $\#E_{\text{Tor}}(\mathbb{Q})$ is the number of rational torsion points on $E$. Remember that the solutions $(x,y)$ to the elliptic curve equation $y^2 = x^3 + Ax+B$, where $x$ and $y$ are both rational numbers, form a group. Recall also that that the group of rational points $E(\mathbb{Q})$ may be finite or infinite, depending on whether the group has algebraic rank zero, or greater than zero. However, it turns out that there are only ever finitely many torsion points - those which can be added to themselves some finite number of times to get the group identity element. These points of finite order form a subgroup, denoted $E_{\text{Tor}}(\mathbb{Q})$, and the quantity in question is just the size of this finite group (squared in the formula). In fact, it's been proven that the size of $E_{\text{Tor}}(\mathbb{Q})$ is at most 16.
2. $\Omega_E$ is the real period of $E$. This is perhaps a bit more tricky to define, but it essentially is a number that measures the size of the set of real points of $E$. If you plot the graph of the equation representing $E: y^2 = x^3 + Ax + B$ on the cartesian plane, you get something that looks like one of the following:

 The plots of the real solutions to four elliptic curves, and their associated real periods.

There is a way to assign an intrinsic "size" to these graphs, which we denote the real period $\Omega_E$. The technical definition is that $\Omega_E$ is equal to the integral of the absolute value of the differential $\omega = \frac{dx}{2y}$ along the part of the real graph of $E$ that is connected to infinity (that or twice that depending on whether the cubic equation $x^3 + Ax + B$ has one or three real roots respectively).
3. $\text{Reg}_E$ is the regulator of $E$. This is a number that measures the "density" of rational points on $E$. Recall that $E(\mathbb{Q}) \approx T \times \mathbb{Z}^{r_{an}}$, i.e there free part of $E(\mathbb{Q})$ is isomorphic to $r_{an}$ copies of the integers. There is a canonical way to embed the free part of $E(\mathbb{Q})$ in $\mathbb{R}^{r_{an}}$ as a lattice; the regulator $\text{Reg}_E$ is the volume of the fundamental domain of this lattice. The thing to take away here is that elliptic curves with small regulators have lots of rational points whose coefficients have small numerators and denominators, while curves with large regulators have few such points.
4. $\prod_p c_p$ is the Tamagawa product for $E$. For each prime $p$, one can consider the points on $E$ over the $p$-adic numbers $\mathbb{Q}_p$. The Tamagawa number $c_p$ is the ratio of the size of the full group of $p$-adic points on $E$ to the subgroup of $p$-adic points that are connected to the identity. This is always a positive integer, and crucially, in all but a finite number of cases the ratio is 1. Thus we can consider the product of the $c_p$ as we range over all prime numbers, and this is precisely the definition of the Tamagawa product.
5. $\#\text{Ш}(E/\mathbb{Q})$ is the order of the Tate-Shafarevich group of $E$ over the rational numbers. The Tate-Shafarevich group of $E$ is probably the most mysterious part of the BSD formula; it is defined as the subgroup of the Weil–Châtelet group $H^1(G_{\mathbb{Q}},E)$ that becomes trivial when passing to any completion of $\mathbb{Q}$. If you're like me then this definition will be completely opaque; however, we can think of $\text{Ш}(E/\mathbb{Q})$ as measuring how much $E$ violates the local-global principle: that one should be able to find rational solutions to an algebraic equation by finding solutions modulo a prime number $p$ for each $p$, and then piecing this information together with the Chinese Remainder Theorem to get a rational solution. Curves with nontrivial $\text{Ш}$ have homogeneous spaces that have solutions modulo $p$ for all $p$, but no rational points. The main thing here is that $\text{Ш}$ is conjectured to be finite, in which case $\#\text{Ш}(E/\mathbb{Q})$ is just a positive integer (in fact, it can be shown for elliptic curves that if $\text{Ш}$ is indeed finite, then its size is a perfect square).
Why is the BSD Conjecture relevant to rank estimation? Because it helps us overcome the crucial obstacle to computing analytic rank exactly: without extra knowledge, it's impossible to decide using numerical techniques whether the $n$th derivative of the $L$-function at the central point is exactly zero, or just so small that it looks like it is zero to the precision that we're using. If we can use the BSD formula to show a priori that $a_0$ must be at least $M_E$ in magnitude, where $M_E$ is some number that depends only on some easily computable data attached to the elliptic curve $E$, then all we need to do is evaluate successive derivatives of $L_E$ at the central point to enough precision to decide if that derivative is less than $M_E$ or not; this is readily doable on a finite precision machine. Keep going until we hit a derivative which is then definitely greater than $M_E$ in magnitude, at which we can halt and declare that the analytic rank is precisely the order of that derivative.

In the context of the explicit formula-based zero sum rank estimation method implemented in our GSoC project, the size of the leading coefficient also controls how far close the lowest noncentral zero can be from the central point. Specifically, we have the folling result: Let $\gamma_0$ be the lowest-lying noncentral zero for $L_E$ (i.e. the zero closest to the central point that is not actually at the central point); let $\Lambda_E(s) = N^{s/2}(2\pi)^s\Gamma(s)L_E(s)$ is the completed $L$-function for $E$, and let $\Lambda_E(1+s) = s^{r_{an}}\left[b_0 + b_1 s + b_2 s^2 \ldots\right]$ be the Taylor expansion of $\Lambda_E$ about the central point. Then:
$$\gamma_0 > \sqrt{\frac{b_0}{b_2}}.$$
Thankfully, $b_0$ is easily relatable back to the constant $a_0$, and we have known computable upper bounds on the magnitude of $b_2$, so knowing how small $a_0$ is tells us how close the lowest zero can be to the central point. Thus bounding $a_0$ from below in terms of some function of $E$ tells us how large of a $\Delta$ value we need to use in our zero sum, and thus how long the computation will take as a function of $E$.

If this perhaps sounds a bit handwavy at this point it's because this is all still a work in progress, to be published as part of my dissertation. Nevertheless, the bottom line is that bounding the leading $L$-series coefficient from below gives us a way to directly analyze the computational complexity of analytic rank methods.

I hope to go into more detail in a future post about what we can do to bound from below the leading coefficient of $L_E$ at the central point, and why this is a far from straightforward endeavour.

### Bristol Summer School Recap

For the past week I've been in Bristol, England, attending a summer school on computing with L-functions and automorphic forms. The official name of the summer school was the Building Bridges Summer School on Automorphic Forms and Related Topics.

The school comprised of three intense 2-day long mini-courses, each hosted by a pair of mathematicians in the field:

The reason I attended the summer school is that the subjects all tie directly to the work that I am currently doing - both for my Google Summer of Code project and work towards my dissertation. The central objects of all three mini-courses are motivic $L$-functions - of which elliptic curve $L$-functions are an example. It would take me quite a long time to go through all the math we covered at the summer school - over the course of last week I took 114 pages of lecture notes! This is unfeasible - if you're interested please follow the links above, at which you should find typed up notes of all of the lectures.

Instead, my next post will focus on one of the aspects that ties in with both my GSoC project and my dissertation work: The full Birch and Swinnerton-Dyer conjecture, and how we can use it to obtain results about the complexity of computing analytic rank.

## Tuesday, July 1, 2014

### Bristol

This week finds me in Bristol, in southwest England. I'm here for a weeklong summer school officially titled the Building Bridges: 2nd EU/US Summer School on Automorphic Forms and Related Topics (catchy title!). The summer school comprises of three mini-courses run by pairs of mathematicians in the area of mathematics that I'm in; it's also being co-organized by of of the mentors of my Google Summer of Code project, who is resident here in England. Thus attending will be a great way to kill multiple birds with one stone: learn some new and cool math, see a new place, and get to chat to people relating to my GSoC project as well.

As such, posts on analytic rank estimation of elliptic curves and $L$-functions might be a bit thin on the ground this week. I'll try do a little bit when I can, but they're keeping us fairly busy here.

Bristol, from what I've seen of it so far, is quite beautiful. The sun has been shining this week (which always helps), but the part of town that I'm staying in - the recently revitalized Docklands area - is clean, picturesque, and buzzing with activity. The city has pushed for sustainability and pedestrian-friendliness in recent years, and it shows.

All in all, can't complain. I think it will be a wonderful week.