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......@@ -13,7 +13,7 @@ a different algorithm will be presented making use of theorems from Section
with the goal of cutting down the run time.
\subsubsection{Finding possible \texorpdfstring{$r$}{r} and
\texorpdfstring{$c$}{c}}
\texorpdfstring{$c$}{c}}
To do this, first calculate the upper bound $r_{\mathrm{max}}$ on the ranks of tilt
semistabilisers, as given by Theorem \ref{thm:loose-bound-on-r}.
......@@ -36,7 +36,7 @@ the Bogomolov inequalities and Consequence 3 of Lemma
($\chern_2^{\beta_{-}}(u)>0$).
\subsubsection{Finding \texorpdfstring{$d$}{d} for fixed \texorpdfstring{$r$}{r}
and \texorpdfstring{$c$}{c}}
and \texorpdfstring{$c$}{c}}
$\Delta(u) \geq 0$ induces an upper bound $\frac{c^2}{2r}$ on $d$, and the
$\chern_2^{\beta_{-}}(u)>0$ condition induces a lower bound on $d$.
......@@ -51,8 +51,8 @@ end up not yielding any solutions for the problem.
In fact, solutions $u$ to our problem with high rank must have $\mu(u)$ close to
$\beta_{-}(v)$:
\begin{align*}
0 &\leq \chern_1^{\beta_{-}}(u) \leq \chern_1^{\beta_{-}}(u) \\
0 &\leq \mu(u) - \beta_{-} \leq \frac{\chern_1^{\beta_{-}}(v)}{r}
0 & \leq \chern_1^{\beta_{-}}(u) \leq \chern_1^{\beta_{-}}(u) \\
0 & \leq \mu(u) - \beta_{-} \leq \frac{\chern_1^{\beta_{-}}(v)}{r}
\end{align*}
In particular, it is the $\chern_1^{\beta_{-}}(v-u) \geq 0$ condition which
fails for $r$,$c$ pairs with large $r$ and $\frac{c}{r}$ too far from $\beta_{-}$.
......@@ -66,17 +66,17 @@ alternative algorithm which will later be described in Section
\ref{sect:prob2-algorithm}.
\begin{center}
\label{table:bench-schmidt-vs-nay}
\begin{tabular}{ |r|l|l| }
\hline
Choice of $v$ on $\mathbb{P}^2$
& $(3, 2\ell, -2)$
& $(3, 2\ell, -\frac{15}{2})$ \\
\hline
\cite[\texttt{tilt.walls_left}]{SchmidtGithub2020} exec time & \sim 20s & >1hr \\
\cite{NaylorRust2023} exec time & \sim 50ms & \sim 50ms \\
\hline
\end{tabular}
\label{table:bench-schmidt-vs-nay}
\begin{tabular}{ |r|l|l| }
\hline
Choice of $v$ on $\mathbb{P}^2$
& $(3, 2\ell, -2)$
& $(3, 2\ell, -\frac{15}{2})$ \\
\hline
\cite[\texttt{tilt.walls_left}]{SchmidtGithub2020} exec time & \sim 20s & >1hr \\
\cite{NaylorRust2023} exec time & \sim 50ms & \sim 50ms \\
\hline
\end{tabular}
\end{center}
\section{Computing Solutions to Problem \ref{problem:problem-statement-2}}
......@@ -94,7 +94,7 @@ The algorithm yields solutions
$u=(r,c\ell,d\ell^2)$ to the problem as follows.
\subsubsection{Iterating Over Possible
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}}
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}}
Given a Chern character $v$, the domain of the problem are first verified: that
$v$ has positive rank, that it satisfies $\Delta(v) \geq 0$, and that
......@@ -102,6 +102,7 @@ $\beta_{-}(v)$ is rational.
Take $\beta_{-}(v)=\frac{a_v}{n}$ in simplest terms.
Iterate over $q = \frac{b_q}{n} \in (0,\chern_1^{\beta_{-}}(v))\cap\frac{1}{n}\ZZ$.
The code used to generate the corresponding values for $b_q$ is shown in Listing
% texlab: ignore
\ref{fig:code:consideredb}.
\lstinputlisting[
......@@ -118,6 +119,7 @@ We can therefore reduce the problem of finding solutions to the more specialised
problem of finding the solutions $u$ with each fixed possible $q=\chern_1^\beta(u)$
(i.e. choice of $b$).
The code representing this appears in Listing
% texlab: ignore
\ref{fig:code:reducingtoeachb}.
Line 16 refers to creating an objects representing the context the specialised
problem for the fixed $q$ value, with the next line `solving' the specialised
......@@ -133,9 +135,9 @@ and collect up the results.
]{../tilt.rs/src/tilt_stability/find_all.git-untrack.rs.tex.git-untrack}
\subsubsection{Iterating Over Possible
\texorpdfstring{$r=\chern_0(u)$}{r}
for Fixed
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}
\texorpdfstring{$r=\chern_0(u)$}{r}
for Fixed
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}
}
Let $q=\frac{b_q}{n}$, for which we are now solving the more specialised problem of finding
......@@ -159,6 +161,7 @@ Fixing $r$ and $q$ also determines $c\coloneqq\chern_1(u)$, and so we can genera
the corresponding values of $c$, as we generate the $r$ values.
It now remains to solve the problem for each of the combinations of fixed values
for $q$ and $r$ (and consequently $c$) considered.
% texlab: ignore
This is shown in Listing \ref{fig:code:reducingtoeachr}.
\lstinputlisting[
......@@ -170,11 +173,11 @@ This is shown in Listing \ref{fig:code:reducingtoeachr}.
\subsubsection{Iterating Over Possible
\texorpdfstring{$d=\chern_2(u)/\ell^2$}{d}
for Fixed
\texorpdfstring{$r=\chern_0(u)$}{r}
and
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}
\texorpdfstring{$d=\chern_2(u)/\ell^2$}{d}
for Fixed
\texorpdfstring{$r=\chern_0(u)$}{r}
and
\texorpdfstring{$q=\chern^{\beta_{-}}(u)$}{q}
}
At this point we are considering a specialisation of the problem
......@@ -198,8 +201,10 @@ equivalent to bounds on $d$ given by the equations
in Subsubsection \ref{subsubsect:all-bounds-on-d-prob2}
It therefore remains to just pick values
$d\in\frac{1}{\lcm(m,2n^2)}\ZZ$ within the bounds.
% texlab: ignore
Listing \ref{fig:code:solveforfixedr} is the code for solving this
specialisation of the problem, where the possible $d$ values are computed in
% texlab: ignore
Listing \ref{fig:code:possible_chern2}.
The explicit code for the bounds can be found in Appendix
\ref{appendix:subsubsec:fixed-r}.
......@@ -232,11 +237,11 @@ decrease in computational time to find the solutions to the problem.
This could be due to a range of potential reasons:
\begin{itemize}
\item Unexpected optimisations from the compiler for a certain form of the
program.
program.
\item Increased complexity to computing the formulae for the tighter bounds.
\item Modern CPU architecture such as branch predictors
\cite{BranchPredictor2024} may offset the overhead of considering ranks that
turn out to be too large to have any solutions.
\cite{BranchPredictor2024} may offset the overhead of considering ranks that
turn out to be too large to have any solutions.
\end{itemize}
For relatively small Chern characters (as those appearing in examples so far),
......