From 1bf95765e31325763af51162d7c04929d85c1334 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 2 Oct 2019 11:24:40 +0200
Subject: [PATCH] fix double occurence of Sec. sec.
---
Makefile | 2 +-
abbreviations.txt | 2 ++
pandoc-crossref.yaml | 3 +++
paper.md | 4 ++--
4 files changed, 8 insertions(+), 3 deletions(-)
create mode 100644 pandoc-crossref.yaml
diff --git a/Makefile b/Makefile
index 1834115..f730da2 100755
--- a/Makefile
+++ b/Makefile
@@ -7,7 +7,7 @@ paper.bbl: paper.tex paper.bib
bibtex paper.aux
paper.tex: paper.md revtex.template
- pandoc -s --filter pandoc-fignos --filter pandoc-citeproc --filter pandoc-crossref --natbib paper.md -o paper.tex --bibliography paper.bib --abbreviations=abbreviations.txt --wrap=preserve --listings --template revtex.template
+ pandoc -s --filter pandoc-fignos --filter pandoc-citeproc --filter pandoc-crossref -M "crossrefYaml=pandoc-crossref.yaml" --natbib paper.md -o paper.tex --bibliography paper.bib --abbreviations=abbreviations.txt --wrap=preserve --listings --template revtex.template
.PHONY: clean
clean:
diff --git a/abbreviations.txt b/abbreviations.txt
index ae73b4a..9c7d303 100644
--- a/abbreviations.txt
+++ b/abbreviations.txt
@@ -1,2 +1,4 @@
Fig.
Eq.
+i.e.
+e.g.
diff --git a/pandoc-crossref.yaml b/pandoc-crossref.yaml
new file mode 100644
index 0000000..0f5018a
--- /dev/null
+++ b/pandoc-crossref.yaml
@@ -0,0 +1,3 @@
+figPrefix: "Fig."
+secPrefix: "Sec."
+autoSectionLabels: false
diff --git a/paper.md b/paper.md
index f5c3ad7..4ee60ee 100755
--- a/paper.md
+++ b/paper.md
@@ -30,7 +30,7 @@ Even though it is suboptimal, one usually resorts to sampling $X$ on a homogeneo
#### Choosing new points based on existing data improves the simulation efficiency.
<!-- This should convey the point that it is advantageous to do this. -->
-An alternative, which improves the simulation efficiency, is to choose new potentially interesting points in $X$, based on existing data [@Gramacy2004; @Figueiredo1995; @Castro2008; @Chen2017]. <!-- cite i.e., hydrodynamics-->
+An alternative, which improves the simulation efficiency, is to choose new potentially interesting points in $X$, based on existing data [@Gramacy2004; @Figueiredo1995; @Castro2008; @Chen2017].
Bayesian optimization works well for high-cost simulations where one needs to find a minimum (or maximum) [@Takhtaganov2018].
However, if the goal of the simulation is to approximate a continuous function using the fewest points, the continuity of the approximation is achieved by a greedy algorithm that samples mid-points of intervals with the largest distance or curvature [@Wolfram2011].
Such a sampling strategy (i.e., in Fig. @fig:algo) would trivially speedup many simulations.
@@ -298,7 +298,7 @@ Here, we see that for homogeneous sampling to get the same error as sampling wit
## A parallelizable adaptive integration algorithm based on cquad
#### The `cquad` algorithm belongs to a class that is parallelizable.
-In Sec. @sec:review we mentioned the doubly-adaptive integration algorithm `CQUAD` [@Gonnet2010].
+In @sec:review we mentioned the doubly-adaptive integration algorithm `CQUAD` [@Gonnet2010].
This algorithm uses a Clenshaw-Curtis quadrature rules of increasing degree $d$ in each interval [@Clenshaw1960].
The error estimate is $\sqrt{\int{\left(f_0(x) - f_1(x)\right)^2}}$, where $f_0$ and $f_1$ are two successive interpolations of the integrand.
To reach the desired total error, intervals with the maximum absolute error are improved.
--
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