From a1d99953303a509f1bcda37516b9cc1215215356 Mon Sep 17 00:00:00 2001
From: beckyperriment <93582518+beckyperriment@users.noreply.github.com>
Date: Fri, 15 Dec 2023 10:46:50 +0000
Subject: [PATCH] Update 2_dtw.md

---
 docs/2_method/2_dtw.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/2_method/2_dtw.md b/docs/2_method/2_dtw.md
index 4e3ff64..74b147e 100644
--- a/docs/2_method/2_dtw.md
+++ b/docs/2_method/2_dtw.md
@@ -27,7 +27,7 @@ The DTW distance is the sum of the Euclidean distance between each point and its
     $y_1$ (monotonicity). 
 3. Each point is mapped to at least one other point, i.e., there are no jumps in time (continuity).
 
-Finding the optimal warping arrangement is an optimisation problem that can be solved using dynamic programming, which splits the problem into easier sub-problems and solves them recursively, storing intermediate solutions until the final solution is reached. To understand the memory-efficient method used in ''DTW-C++``, it is useful to first examine the full-cost matrix solution, as follows. For each pairwise comparison, an ($n$) by ($m$) matrix $C^{n\times m}$ is calculated, where each element represents the cumulative cost between series up to the points $x_i$ and $y_j$:
+Finding the optimal warping arrangement is an optimisation problem that can be solved using dynamic programming, which splits the problem into easier sub-problems and solves them recursively, storing intermediate solutions until the final solution is reached. To understand the memory-efficient method used in *DTW-C++*, it is useful to first examine the full-cost matrix solution, as follows. For each pairwise comparison, an ($n$) by ($m$) matrix $C^{n\times m}$ is calculated, where each element represents the cumulative cost between series up to the points $x_i$ and $y_j$:
 
 $$
 c_{i,j} = (x_i-y_j)^2+\min \begin{cases}