From 9a485cffeea5fc8dd8afbe348baa40fee99e0803 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Tue, 10 Sep 2019 16:28:42 +0200
Subject: [PATCH] reword

---
 paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paper.md b/paper.md
index 34324d9..a223819 100755
--- a/paper.md
+++ b/paper.md
@@ -47,7 +47,7 @@ Here we associate a *local loss* to each of the *candidate points* within an int
 In the case of the integration algorithm the loss could just be an error estimate.
 The most significant advantage of these *local* algorithms is that they allow for easy parallelization and have a low computational overhead.
 
-![Visualization of the point choosing algorithm for a blackbox function (grey).
+![Visualization of a simple point choosing algorithm for a blackbox function (grey).
 The existing data points (green) $\{x_i, y_i\}_{i \in 1...4}$ and corresponding candidate points (red) in the middle of each interval.
 Each candidate point has a loss $L$ indicated by the size of the red dots.
 The candidate point with the largest loss will be chosen, which in this case is the one with $L_{1,2}$.
-- 
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