Selberg sieve variational problem: Difference between revisions
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| 1.845 | | [http://terrytao.wordpress.com/2013/11/22/polymath8b-ii-optimising-the-variational-problem-and-the-sieve/#comment-253082 1.845] | ||
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| 2.001162 | | [http://arxiv.org/abs/1311.4600 2.001162] | ||
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| 2.53 | | [http://terrytao.wordpress.com/2013/11/19/polymath8b-bounded-intervals-with-many-primes-after-maynard/#comment-251896 2.53] | ||
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| 3.05 | | [http://terrytao.wordpress.com/2013/11/19/polymath8b-bounded-intervals-with-many-primes-after-maynard/#comment-251896 3.05] | ||
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| 3.34 | | [http://terrytao.wordpress.com/2013/11/19/polymath8b-bounded-intervals-with-many-primes-after-maynard/#comment-251896 3.34] | ||
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| 3.52 | | [http://terrytao.wordpress.com/2013/11/19/polymath8b-bounded-intervals-with-many-primes-after-maynard/#comment-251896 3.52] | ||
| 3.793466 | | 3.793466 | ||
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| 3.66 | | [http://terrytao.wordpress.com/2013/11/19/polymath8b-bounded-intervals-with-many-primes-after-maynard/#comment-251896 3.66] | ||
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| 3. | | [http://terrytao.wordpress.com/2013/11/22/polymath8b-ii-optimising-the-variational-problem-and-the-sieve/#comment-255681 3.96508] | ||
| 4.1479398 | | 4.1479398 | ||
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All upper bounds come from (1). |
Revision as of 16:36, 8 December 2013
Let [math]\displaystyle{ M_k }[/math] be the quantity
- [math]\displaystyle{ \displaystyle M_k := \sup_F \frac{\sum_{m=1}^k J_k^{(m)}(F)}{I_k(F)} }[/math]
where [math]\displaystyle{ F }[/math] ranges over square-integrable functions on the simplex
- [math]\displaystyle{ \displaystyle {\mathcal R}_k := \{ (t_1,\ldots,t_k) \in [0,+\infty)^k: t_1+\ldots+t_k \leq 1 \} }[/math]
with [math]\displaystyle{ I_k, J_k^{(m)} }[/math] being the quadratic forms
- [math]\displaystyle{ \displaystyle I_k(F) := \int_{{\mathcal R}_k} F(t_1,\ldots,t_k)^2\ dt_1 \ldots dt_k }[/math]
and
- [math]\displaystyle{ \displaystyle J_k^{(m)}(F) := \int_{{\mathcal R}_{k-1}} (\int_0^{1-\sum_{i \neq m} t_i} F(t_1,\ldots,t_k)\ dt_m)^2 dt_1 \ldots dt_{m-1} dt_{m+1} \ldots dt_k. }[/math]
It is known that [math]\displaystyle{ DHL[k,m+1] }[/math] holds whenever [math]\displaystyle{ EH[\theta] }[/math] holds and [math]\displaystyle{ M_k \gt \frac{2m}{\theta} }[/math]. Thus for instance, [math]\displaystyle{ M_k \gt 2 }[/math] implies [math]\displaystyle{ DHL[k,2] }[/math] on the Elliott-Halberstam conjecture, and [math]\displaystyle{ M_k\gt 4 }[/math] implies [math]\displaystyle{ DHL[k,2] }[/math] unconditionally.
Upper bounds
We have the upper bound
- [math]\displaystyle{ \displaystyle M_k \leq \frac{k}{k-1} \log k }[/math] (1)
that is proven as follows.
The key estimate is
- [math]\displaystyle{ \displaystyle \int_0^{1-t_2-\ldots-t_k} F(t_1,\ldots,t_k)\ dt_1)^2 \leq \frac{\log k}{k-1} \int_0^{1-t_2-\ldots-t_k} F(t_1,\ldots,t_k)^2 (1 - t_1-\ldots-t_k+ kt_1)\ dt_1. }[/math]. (2)
Assuming this estimate, we may integrate in [math]\displaystyle{ t_2,\ldots,t_k }[/math] to conclude that
- [math]\displaystyle{ \displaystyle J_k^{(1)}(F) \leq \frac{\log k}{k-1} \int F^2 (1-t_1-\ldots-t_k+kt_1)\ dt_1 \ldots dt_k }[/math]
which symmetrises to
- [math]\displaystyle{ \sum_{m=1}^k J_k^{(m)}(F) \leq k \frac{\log k}{k-1} \int F^2\ dt_1 \ldots dt_k }[/math]
giving the desired upper bound (1).
It remains to prove (2). By Cauchy-Schwarz, it suffices to show that
- [math]\displaystyle{ \displaystyle \int_0^{1-t_2-\ldots-t_k} \frac{dt_1}{1 - t_1-\ldots-t_k+ kt_1} \leq \frac{\log k}{k-1}. }[/math]
But writing [math]\displaystyle{ s = t_2+\ldots+t_k }[/math], the left-hand side evaluates to
- [math]\displaystyle{ \frac{1}{k-1} (\log k(1-s) - \log (1-s) ) = \frac{\log k}{k-1} }[/math]
as required.
Lower bounds
...
World records
[math]\displaystyle{ k }[/math] | Lower bound | Upper bound |
---|---|---|
4 | 1.845 | 1.848 |
5 | 2.001162 | 2.0011797 |
10 | 2.53 | 2.55842 |
20 | 3.05 | 3.1534 |
30 | 3.34 | 3.51848 |
40 | 3.52 | 3.793466 |
50 | 3.66 | 3.99186 |
59 | 3.96508 | 4.1479398 |
All upper bounds come from (1).