build

en/docs/chapter_dynamic_programming/unbounded_knapsack_problem.md

@@ -751,7 +751,7 @@ The two-dimensional $dp$ table has size $(n+1) \times (amt+1)$.
 This problem differs from the unbounded knapsack problem in the following two aspects regarding the state transition equation.
 
 - This problem seeks the minimum value, so the operator $\max()$ needs to be changed to $\min()$.
-- The optimization target is the number of coins rather than item value, so when a coin is selected, simply execute $+1$.
+- The optimization target is the number of coins rather than item value, so when a coin is selected, simply add $1$.
 
 $$
 dp[i, a] = \min(dp[i-1, a], dp[i, a - coins[i-1]] + 1)
@@ -765,7 +765,7 @@ When there are no coins, **it is impossible to make up any amount $> 0$**, which
 
 ### 2.   Code Implementation
 
-Most programming languages do not provide a $+ \infty$ variable, and can only use the maximum value of integer type `int` as a substitute. However, this can lead to large number overflow: the $+ 1$ operation in the state transition equation may cause overflow.
+Most programming languages do not provide a $+ \infty$ variable, and can only use the maximum value of integer type `int` as a substitute. However, this can lead to integer overflow: the $+ 1$ operation in the state transition equation may cause overflow.
 
 For this reason, we use the number $amt + 1$ to represent invalid solutions, because the maximum number of coins needed to make up $amt$ is at most $amt$. Before returning, check whether $dp[n, amt]$ equals $amt + 1$; if so, return $-1$, indicating that the target amount cannot be made up. The code is as follows:
 
@@ -1572,7 +1572,7 @@ The space optimization for the coin change problem is handled in the same way as
     end
     ```
 
-## 14.5.3   Coin Change Problem Ii
+## 14.5.3   Coin Change Problem II
 
 !!! question