From 7cfb743278890b2880fe35f7305103fcb83dea51 Mon Sep 17 00:00:00 2001
From: Humphrey Yang <u6474961@anu.edu.au>
Date: Tue, 18 Mar 2025 13:32:03 +1100
Subject: [PATCH] update newton and opt_transport

---
 lectures/newton_method.md |  2 +-
 lectures/opt_transport.md | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/lectures/newton_method.md b/lectures/newton_method.md
index ce340d2..ab66d14 100644
--- a/lectures/newton_method.md
+++ b/lectures/newton_method.md
@@ -426,7 +426,7 @@ k_star_approx_newton = newton(f=lambda x: g(x, params) - x,
 ```
 
 ```{code-cell} ipython3
-k_star_近似牛顿法
+k_star_approx_newton
 ```
 
 结果证实了我们在上面图表中看到的收敛情况：仅需5次迭代就达到了非常精确的结果。
diff --git a/lectures/opt_transport.md b/lectures/opt_transport.md
index b3317d8..b64761a 100644
--- a/lectures/opt_transport.md
+++ b/lectures/opt_transport.md
@@ -403,8 +403,8 @@ arr = np.arange(m+n)
 ```
 
 ```{code-cell} ipython3
-已找到解 = []
-成本 = []
+sol_found = []
+cost = []
 
 # 模拟1000次
 for i in range(1000):
@@ -414,9 +414,9 @@ for i in range(1000):
 
     # 如果找到新解
     sol = tuple(res_shuffle.x)
-    if sol not in 已找到解:
-        已找到解.append(sol)
-        成本.append(res_shuffle.fun)
+    if sol not in sol_found:
+        sol_found.append(sol)
+        cost.append(res_shuffle.fun)
 ```
 
 ```{code-cell} ipython3