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luke naylor latex documents
research
Max Destabilizer Rank
Commits
cb98c26a
Commit
cb98c26a
authored
3 months ago
by
Luke Naylor
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Complete corrections of part II
parent
fbbe61f2
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#49102
failed
3 months ago
Stage: test
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tex/bounds-on-semistabilisers.tex
+7
-8
7 additions, 8 deletions
tex/bounds-on-semistabilisers.tex
tex/computing-solutions.tex
+4
-4
4 additions, 4 deletions
tex/computing-solutions.tex
with
11 additions
and
12 deletions
tex/bounds-on-semistabilisers.tex
+
7
−
8
View file @
cb98c26a
...
...
@@ -81,8 +81,8 @@ pseudo-semistabilisers for tilt stability.
currently considered restrictions, is unbounded.
This is where the rationality of
$
\beta
_{
-
}$
comes in.
If
$
\beta
_{
-
}
=
\frac
{
a
_
v
}{
n
}$
for some
$
a
_
v,n
\in
\ZZ
$
,
then
$
\chern
^{
\beta
_
-
}_
2
(
u
)
\in
\frac
{
1
}{
\lcm
(
m,
2
n
^
2
)
}
\ZZ
$
.
In particular, since
$
\chern
_
2
^{
\beta
_
-
}
(
u
)
>
0
$
we
must also
have
then
we must have
$
\chern
^{
\beta
_
-
}_
2
(
u
)
\in
\frac
{
1
}{
\lcm
(
m,
2
n
^
2
)
}
\ZZ
$
.
In particular, since
$
\chern
_
2
^{
\beta
_
-
}
(
u
)
>
0
$
we have
$
\chern
^{
\beta
_
-
}_
2
(
u
)
\geq
\frac
{
1
}{
\lcm
(
m,
2
n
^
2
)
}$
, which then in turn gives a
bound for the rank of
$
u
$
:
...
...
@@ -90,11 +90,11 @@ pseudo-semistabilisers for tilt stability.
\chern
_
0(u)
&
\leq
\frac
{
\chern
^{
\beta
_
-
}_
1(u)
^
2
}{
2
\chern
^{
\beta
_{
-
}}_
2(u)
}
\nonumber
\\
&
\leq
\frac
{
\lcm
(m,2n
^
2)
\chern
^{
\beta
_
-
}_
1(u)
^
2
}{
2
}
\nonumber
\\
&
=
\frac
{
mn
^
2
\chern
^{
\beta
_
-
}_
1(u)
^
2
}{
\gcd
(m,2n
^
2)
}
&
=
\frac
{
mn
^
2
\chern
^{
\beta
_
-
}_
1(u)
^
2
}{
\gcd
(m,2n
^
2)
}
.
\label
{
proof:first-bound-on-r
}
\end{align}
\noindent
W
hi
ch we
can then immediately bound using Equation
\eqref
{
eqn-tilt-cat-cond
}
.
T
hi
s
can then immediately
be
bound using Equation
\eqref
{
eqn-tilt-cat-cond
}
.
Alternatively, given that
$
\chern
_
1
^{
\beta
_{
-
}}
(
u
)
$
,
$
\chern
_
1
^{
\beta
_{
-
}}
(
v
)
\in\frac
{
1
}{
n
}
\ZZ
$
,
we can tighten this bound on
$
\chern
_
1
^{
\beta
_{
-
}}
(
u
)
$
given by that equation to:
...
...
@@ -142,7 +142,7 @@ pseudo-semistabilisers for tilt stability.
Using the above Theorem
\ref
{
thm:loose-bound-on-r
}
, we get that the ranks of
tilt semistabilisers for
$
v
$
are bounded above by
$
\sage
{
extravagant.loose
_
bound
}$
.
However, when computing all tilt semistabilisers for
$
v
$
on
$
\PP
^
2
$
, the maximum
rank that appears turns out to be
$
\sage
{
round
(
extravagant.actual
_
rmax
,
1
)
}$
.
rank that appears turns out to be
$
\sage
{
round
(
extravagant.actual
_
rmax
)
}$
.
\end{example}
...
...
@@ -180,8 +180,7 @@ of travel.
\qquad
\text
{
and
}
\qquad
\chern
_
0(u) >
\frac
{
q
}{
\mu
(v) -
\beta
_
0
}
.
\nonumber
\chern
_
0(u) >
\frac
{
q
}{
\mu
(v) -
\beta
_
0
}
\cdot
\end{equation}
\noindent
...
...
@@ -212,7 +211,7 @@ of travel.
$
u
=(
r,c
\ell
,d
\ell
^
2
)
$
with
$
r,c
\in
\ZZ
$
and
$
d
\in
\frac
{
1
}{
\lcm
(
m,
2
)
}
\ZZ
$
satisfying Equation
\eqref
{
lem:eqn:cond-for-fixed-q
}
is in fact a solution provided the remaining numerical conditions
1, 2, 3 and 6 are satisfied.
This is in essence the second part of the
L
emma.
This is in essence the second part of the
l
emma.
\end{proof}
\begin{corollary}
...
...
This diff is collapsed.
Click to expand it.
tex/computing-solutions.tex
+
4
−
4
View file @
cb98c26a
...
...
@@ -247,7 +247,7 @@ This could be due to a range of potential reasons:
For relatively small Chern characters (as those appearing in examples so far),
the difference in performance between the program
\cite
{
NaylorRust2023
}
when
patched with the results of the different theorems above, do not show any
significant difference in performance
. T
he earlier, weaker theorems occasionally
significant difference in performance
, with t
he earlier, weaker theorems occasionally
producing the results marginally faster.
Note that this program patched with Theorem
\ref
{
thm:loose-bound-on-r
}
will be
...
...
@@ -271,8 +271,8 @@ indicators of the size of the bounds on the pseudo-semistabiliser ranks.
\caption
{
Comparing the performance of program
\cite
{
NaylorRust2023
}
with different patches corresponding to the results of Theorems
\ref
{
thm:loose-bound-on-r
}
\ref
{
thm:rmax
_
with
_
uniform
_
eps
}
\ref
{
thm:loose-bound-on-r
}
,
\ref
{
thm:rmax
_
with
_
uniform
_
eps
}
,
\ref
{
thm:rmax
_
with
_
eps1
}
when computing solutions to Problem
\ref
{
problem:problem-statement-2
}
for
$
(
45
,
54
\ell
,
-
41
\frac
{
\ell
^
2
}{
2
}
)
$
...
...
@@ -304,6 +304,6 @@ versus the average looser.
However, the actual ratio in the benchmark shown in Figure
\ref
{
fig:benchmark
}
between the two instances of the program patched with the two corresponding
bounds is around 0.6 instead.
N
ot as good as the improvement on the bound, however still not insignificant
This is n
ot as good as the improvement on the bound, however
it is
still not insignificant
for examples with larger `
$
n
$
'-value, where the execution time will
potentially be in the order of minutes, or even hours.
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