@@ -51,14 +51,15 @@ $dp[i][w] = max \lbrace dp[i - 1][w - k \times weight[i - 1]] + k \times value[i
5151
5252``` Python
5353class Solution :
54+ # 思路 1:动态规划 + 二维基本思路
5455 def completePackMethod1 (self weight : [int ], value : [int ], W : int ):
5556 size = len (weight)
5657 dp = [[0 for _ in range (W + 1 )] for _ in range (size + 1 )]
5758
5859 # 枚举前 i 种物品
5960 for i in range (1 , size + 1 ):
6061 # 枚举背包装载重量
61- for w in range (1 , W + 1 ):
62+ for w in range (W + 1 ):
6263 # 枚举第 i 种物品能取个数
6364 for k in range (w // weight[i - 1 ] + 1 ):
6465 # dp[i][w] 取所有 dp[i - 1][w - k * weight[i - 1] + k * value[i - 1] 中最大值
@@ -142,15 +143,16 @@ $\quad dp[i][w] = \begin{cases} dp[i - 1][w] & w < weight[i - 1] \cr max \lbrac
142143#### 思路 2:代码
143144
144145``` Python
145- class Solution :
146+ class Solution :
147+ # 思路 2:动态规划 + 状态转移方程优化
146148 def completePackMethod2 (self weight : [int ], value : [int ], W : int ):
147149 size = len (weight)
148150 dp = [[0 for _ in range (W + 1 )] for _ in range (size + 1 )]
149151
150152 # 枚举前 i 种物品
151153 for i in range (1 , size + 1 ):
152154 # 枚举背包装载重量
153- for w in range (1 , W + 1 ):
155+ for w in range (W + 1 ):
154156 # 第 i - 1 件物品装不下
155157 if w < weight[i - 1 ]:
156158 # dp[i][w] 取「前 i - 1 种物品装入载重为 w 的背包中的最大价值」
@@ -209,6 +211,7 @@ $dp[w] = \begin{cases} dp[w] & w < weight[i - 1] \cr max \lbrace dp[w], \quad d
209211
210212``` Python
211213class Solution :
214+ # 思路 3:动态规划 + 滚动数组优化
212215 def completePackMethod3 (self weight : [int ], value : [int ], W : int ):
213216 size = len (weight)
214217 dp = [0 for _ in range (W + 1 )]
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