diff --git a/chapter_backtracking/backtracking_algorithm.md b/chapter_backtracking/backtracking_algorithm.md
index 2370c0a76..c9b02632e 100644
--- a/chapter_backtracking/backtracking_algorithm.md
+++ b/chapter_backtracking/backtracking_algorithm.md
@@ -1364,7 +1364,7 @@ comments: true
 - 上文介绍过的剪枝是一种常用的优化方法。它可以避免搜索那些肯定不会产生有效解的路径，从而节省时间和空间。
 - 另一个常用的优化方法是加入「启发式搜索 Heuristic Search」策略，它在搜索过程中引入一些策略或者估计值，从而优先搜索最有可能产生有效解的路径。
 
-## 13.1.6.   典型例题
+## 13.1.6.   回溯典型例题
 
 **搜索问题**：这类问题的目标是找到满足特定条件的解决方案。
 
diff --git a/chapter_divide_and_conquer/binary_search_recur.md b/chapter_divide_and_conquer/binary_search_recur.md
index 6033e7172..6fd5e9910 100644
--- a/chapter_divide_and_conquer/binary_search_recur.md
+++ b/chapter_divide_and_conquer/binary_search_recur.md
@@ -160,9 +160,32 @@ status: new
 === "C#"
 
     ```csharp title="binary_search_recur.cs"
-    [class]{binary_search_recur}-[func]{dfs}
+    /* 二分查找：问题 f(i, j) */
+    int dfs(int[] nums, int target, int i, int j) {
+        // 若区间为空，代表无目标元素，则返回 -1
+        if (i > j) {
+            return -1;
+        }
+        // 计算中点索引 m
+        int m = (i + j) / 2;
+        if (nums[m] < target) {
+            // 递归子问题 f(m+1, j)
+            return dfs(nums, target, m + 1, j);
+        } else if (nums[m] > target) {
+            // 递归子问题 f(i, m-1)
+            return dfs(nums, target, i, m - 1);
+        } else {
+            // 找到目标元素，返回其索引
+            return m;
+        }
+    }
 
-    [class]{binary_search_recur}-[func]{binarySearch}
+    /* 二分查找 */
+    int binarySearch(int[] nums, int target) {
+        int n = nums.Length;
+        // 求解问题 f(0, n-1)
+        return dfs(nums, target, 0, n - 1);
+    }
     ```
 
 === "Swift"
diff --git a/chapter_divide_and_conquer/build_binary_tree_problem.md b/chapter_divide_and_conquer/build_binary_tree_problem.md
index 41cc4ae4a..f4b2bd50a 100644
--- a/chapter_divide_and_conquer/build_binary_tree_problem.md
+++ b/chapter_divide_and_conquer/build_binary_tree_problem.md
@@ -195,9 +195,33 @@ status: new
 === "C#"
 
     ```csharp title="build_tree.cs"
-    [class]{build_tree}-[func]{dfs}
+    /* 构建二叉树：分治 */
+    TreeNode dfs(int[] preorder, int[] inorder, Dictionary<int, int> hmap, int i, int l, int r) {
+        // 子树区间为空时终止
+        if (r - l < 0)
+            return null;
+        // 初始化根节点
+        TreeNode root = new TreeNode(preorder[i]);
+        // 查询 m ，从而划分左右子树
+        int m = hmap[preorder[i]];
+        // 子问题：构建左子树
+        root.left = dfs(preorder, inorder, hmap, i + 1, l, m - 1);
+        // 子问题：构建右子树
+        root.right = dfs(preorder, inorder, hmap, i + 1 + m - l, m + 1, r);
+        // 返回根节点
+        return root;
+    }
 
-    [class]{build_tree}-[func]{buildTree}
+    /* 构建二叉树 */
+    TreeNode buildTree(int[] preorder, int[] inorder) {
+        // 初始化哈希表，存储 inorder 元素到索引的映射
+        Dictionary<int, int> hmap = new Dictionary<int, int>();
+        for (int i = 0; i < inorder.Length; i++) {
+            hmap.TryAdd(inorder[i], i);
+        }
+        TreeNode root = dfs(preorder, inorder, hmap, 0, 0, inorder.Length - 1);
+        return root;
+    }
     ```
 
 === "Swift"
diff --git a/chapter_divide_and_conquer/hanota_problem.md b/chapter_divide_and_conquer/hanota_problem.md
index df2a0afdc..4f187a934 100644
--- a/chapter_divide_and_conquer/hanota_problem.md
+++ b/chapter_divide_and_conquer/hanota_problem.md
@@ -112,7 +112,7 @@ status: new
     }
 
     /* 求解汉诺塔 */
-    void hanota(List<Integer> A, List<Integer> B, List<Integer> C) {
+    void solveHanota(List<Integer> A, List<Integer> B, List<Integer> C) {
         int n = A.size();
         // 将 A 顶部 n 个圆盘借助 B 移到 C
         dfs(n, A, B, C);
@@ -227,11 +227,36 @@ status: new
 === "C#"
 
     ```csharp title="hanota.cs"
-    [class]{hanota}-[func]{move}
+    /* 移动一个圆盘 */
+    void move(List<int> src, List<int> tar) {
+        // 从 src 顶部拿出一个圆盘
+        int pan = src[^1];
+        src.RemoveAt(src.Count - 1);
+        // 将圆盘放入 tar 顶部
+        tar.Add(pan);
+    }
 
-    [class]{hanota}-[func]{dfs}
+    /* 求解汉诺塔：问题 f(i) */
+    void dfs(int i, List<int> src, List<int> buf, List<int> tar) {
+        // 若 src 只剩下一个圆盘，则直接将其移到 tar
+        if (i == 1) {
+            move(src, tar);
+            return;
+        }
+        // 子问题 f(i-1) ：将 src 顶部 i-1 个圆盘借助 tar 移到 buf
+        dfs(i - 1, src, tar, buf);
+        // 子问题 f(1) ：将 src 剩余一个圆盘移到 tar
+        move(src, tar);
+        // 子问题 f(i-1) ：将 buf 顶部 i-1 个圆盘借助 src 移到 tar
+        dfs(i - 1, buf, src, tar);
+    }
 
-    [class]{hanota}-[func]{hanota}
+    /* 求解汉诺塔 */
+    void solveHanota(List<int> A, List<int> B, List<int> C) {
+        int n = A.Count;
+        // 将 A 顶部 n 个圆盘借助 B 移到 C
+        dfs(n, A, B, C);
+    }
     ```
 
 === "Swift"
diff --git a/chapter_divide_and_conquer/summary.md b/chapter_divide_and_conquer/summary.md
index 6edb18c43..9ed78a801 100644
--- a/chapter_divide_and_conquer/summary.md
+++ b/chapter_divide_and_conquer/summary.md
@@ -11,6 +11,6 @@ status: new
 - 引入分治策略往往可以带来算法效率的提升。一方面，分治策略减少了计算吧操作数量；另一方面，分治后有利于系统的并行优化。
 - 分治既可以解决许多算法问题，也广泛应用于数据结构与算法设计中，处处可见其身影。
 - 相较于暴力搜索，自适应搜索效率更高。时间复杂度为 $O(\log n)$ 的搜索算法通常都是基于分治策略实现的。
-- 二分查找也体现了分治思想，我们可以通过递归分治实现二分查找。
+- 二分查找是分治思想的另一个典型应用，它不包含将子问题的解进行合并的步骤。我们可以通过递归分治实现二分查找。
 - 在构建二叉树问题中，构建树（原问题）可以被划分为构建左子树和右子树（子问题），其可以通过划分前序遍历和中序遍历的索引区间来实现。
 - 在汉诺塔问题中，一个规模为 $n$ 的问题可以被划分为两个规模为 $n-1$ 的子问题和一个规模为 $1$ 的子问题。按顺序解决这三个子问题后，原问题随之得到解决。
diff --git a/chapter_dynamic_programming/dp_problem_features.md b/chapter_dynamic_programming/dp_problem_features.md
index 8e08d6225..5a9e3255a 100644
--- a/chapter_dynamic_programming/dp_problem_features.md
+++ b/chapter_dynamic_programming/dp_problem_features.md
@@ -143,7 +143,23 @@ $$
 === "Swift"
 
     ```swift title="min_cost_climbing_stairs_dp.swift"
-    [class]{}-[func]{minCostClimbingStairsDP}
+    /* 爬楼梯最小代价：动态规划 */
+    func minCostClimbingStairsDP(cost: [Int]) -> Int {
+        let n = cost.count - 1
+        if n == 1 || n == 2 {
+            return cost[n]
+        }
+        // 初始化 dp 表，用于存储子问题的解
+        var dp = Array(repeating: 0, count: n + 1)
+        // 初始状态：预设最小子问题的解
+        dp[1] = 1
+        dp[2] = 2
+        // 状态转移：从较小子问题逐步求解较大子问题
+        for i in stride(from: 3, through: n, by: 1) {
+            dp[i] = min(dp[i - 1], dp[i - 2]) + cost[i]
+        }
+        return dp[n]
+    }
     ```
 
 === "Zig"
@@ -275,7 +291,18 @@ $$
 === "Swift"
 
     ```swift title="min_cost_climbing_stairs_dp.swift"
-    [class]{}-[func]{minCostClimbingStairsDPComp}
+    /* 爬楼梯最小代价：状态压缩后的动态规划 */
+    func minCostClimbingStairsDPComp(cost: [Int]) -> Int {
+        let n = cost.count - 1
+        if n == 1 || n == 2 {
+            return cost[n]
+        }
+        var (a, b) = (cost[1], cost[2])
+        for i in stride(from: 3, through: n, by: 1) {
+            (a, b) = (b, min(a, b) + cost[i])
+        }
+        return b
+    }
     ```
 
 === "Zig"
@@ -465,7 +492,25 @@ $$
 === "Swift"
 
     ```swift title="climbing_stairs_constraint_dp.swift"
-    [class]{}-[func]{climbingStairsConstraintDP}
+    /* 带约束爬楼梯：动态规划 */
+    func climbingStairsConstraintDP(n: Int) -> Int {
+        if n == 1 || n == 2 {
+            return n
+        }
+        // 初始化 dp 表，用于存储子问题的解
+        var dp = Array(repeating: Array(repeating: 0, count: 3), count: n + 1)
+        // 初始状态：预设最小子问题的解
+        dp[1][1] = 1
+        dp[1][2] = 0
+        dp[2][1] = 0
+        dp[2][2] = 1
+        // 状态转移：从较小子问题逐步求解较大子问题
+        for i in stride(from: 3, through: n, by: 1) {
+            dp[i][1] = dp[i - 1][2]
+            dp[i][2] = dp[i - 2][1] + dp[i - 2][2]
+        }
+        return dp[n][1] + dp[n][2]
+    }
     ```
 
 === "Zig"
diff --git a/chapter_dynamic_programming/dp_solution_pipeline.md b/chapter_dynamic_programming/dp_solution_pipeline.md
index 4a359c71a..8591dddef 100644
--- a/chapter_dynamic_programming/dp_solution_pipeline.md
+++ b/chapter_dynamic_programming/dp_solution_pipeline.md
@@ -216,7 +216,22 @@ $$
 === "Swift"
 
     ```swift title="min_path_sum.swift"
-    [class]{}-[func]{minPathSumDFS}
+    /* 最小路径和：暴力搜索 */
+    func minPathSumDFS(grid: [[Int]], i: Int, j: Int) -> Int {
+        // 若为左上角单元格，则终止搜索
+        if i == 0, j == 0 {
+            return grid[0][0]
+        }
+        // 若行列索引越界，则返回 +∞ 代价
+        if i < 0 || j < 0 {
+            return .max
+        }
+        // 计算从左上角到 (i-1, j) 和 (i, j-1) 的最小路径代价
+        let left = minPathSumDFS(grid: grid, i: i - 1, j: j)
+        let up = minPathSumDFS(grid: grid, i: i, j: j - 1)
+        // 返回从左上角到 (i, j) 的最小路径代价
+        return min(left, up) + grid[i][j]
+    }
     ```
 
 === "Zig"
@@ -389,7 +404,27 @@ $$
 === "Swift"
 
     ```swift title="min_path_sum.swift"
-    [class]{}-[func]{minPathSumDFSMem}
+    /* 最小路径和：记忆化搜索 */
+    func minPathSumDFSMem(grid: [[Int]], mem: inout [[Int]], i: Int, j: Int) -> Int {
+        // 若为左上角单元格，则终止搜索
+        if i == 0, j == 0 {
+            return grid[0][0]
+        }
+        // 若行列索引越界，则返回 +∞ 代价
+        if i < 0 || j < 0 {
+            return .max
+        }
+        // 若已有记录，则直接返回
+        if mem[i][j] != -1 {
+            return mem[i][j]
+        }
+        // 左边和上边单元格的最小路径代价
+        let left = minPathSumDFSMem(grid: grid, mem: &mem, i: i - 1, j: j)
+        let up = minPathSumDFSMem(grid: grid, mem: &mem, i: i, j: j - 1)
+        // 记录并返回左上角到 (i, j) 的最小路径代价
+        mem[i][j] = min(left, up) + grid[i][j]
+        return mem[i][j]
+    }
     ```
 
 === "Zig"
@@ -565,7 +600,29 @@ $$
 === "Swift"
 
     ```swift title="min_path_sum.swift"
-    [class]{}-[func]{minPathSumDP}
+    /* 最小路径和：动态规划 */
+    func minPathSumDP(grid: [[Int]]) -> Int {
+        let n = grid.count
+        let m = grid[0].count
+        // 初始化 dp 表
+        var dp = Array(repeating: Array(repeating: 0, count: m), count: n)
+        dp[0][0] = grid[0][0]
+        // 状态转移：首行
+        for j in stride(from: 1, to: m, by: 1) {
+            dp[0][j] = dp[0][j - 1] + grid[0][j]
+        }
+        // 状态转移：首列
+        for i in stride(from: 1, to: n, by: 1) {
+            dp[i][0] = dp[i - 1][0] + grid[i][0]
+        }
+        // 状态转移：其余行列
+        for i in stride(from: 1, to: n, by: 1) {
+            for j in stride(from: 1, to: m, by: 1) {
+                dp[i][j] = min(dp[i][j - 1], dp[i - 1][j]) + grid[i][j]
+            }
+        }
+        return dp[n - 1][m - 1]
+    }
     ```
 
 === "Zig"
@@ -771,7 +828,28 @@ $$
 === "Swift"
 
     ```swift title="min_path_sum.swift"
-    [class]{}-[func]{minPathSumDPComp}
+    /* 最小路径和：状态压缩后的动态规划 */
+    func minPathSumDPComp(grid: [[Int]]) -> Int {
+        let n = grid.count
+        let m = grid[0].count
+        // 初始化 dp 表
+        var dp = Array(repeating: 0, count: m)
+        // 状态转移：首行
+        dp[0] = grid[0][0]
+        for j in stride(from: 1, to: m, by: 1) {
+            dp[j] = dp[j - 1] + grid[0][j]
+        }
+        // 状态转移：其余行
+        for i in stride(from: 1, to: n, by: 1) {
+            // 状态转移：首列
+            dp[0] = dp[0] + grid[i][0]
+            // 状态转移：其余列
+            for j in stride(from: 1, to: m, by: 1) {
+                dp[j] = min(dp[j - 1], dp[j]) + grid[i][j]
+            }
+        }
+        return dp[m - 1]
+    }
     ```
 
 === "Zig"
diff --git a/chapter_dynamic_programming/edit_distance_problem.md b/chapter_dynamic_programming/edit_distance_problem.md
index 4fa68c331..ae7f67b37 100644
--- a/chapter_dynamic_programming/edit_distance_problem.md
+++ b/chapter_dynamic_programming/edit_distance_problem.md
@@ -213,7 +213,32 @@ $$
 === "Swift"
 
     ```swift title="edit_distance.swift"
-    [class]{}-[func]{editDistanceDP}
+    /* 编辑距离：动态规划 */
+    func editDistanceDP(s: String, t: String) -> Int {
+        let n = s.utf8CString.count
+        let m = t.utf8CString.count
+        var dp = Array(repeating: Array(repeating: 0, count: m + 1), count: n + 1)
+        // 状态转移：首行首列
+        for i in stride(from: 1, through: n, by: 1) {
+            dp[i][0] = i
+        }
+        for j in stride(from: 1, through: m, by: 1) {
+            dp[0][j] = j
+        }
+        // 状态转移：其余行列
+        for i in stride(from: 1, through: n, by: 1) {
+            for j in stride(from: 1, through: m, by: 1) {
+                if s.utf8CString[i - 1] == t.utf8CString[j - 1] {
+                    // 若两字符相等，则直接跳过此两字符
+                    dp[i][j] = dp[i - 1][j - 1]
+                } else {
+                    // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+                    dp[i][j] = min(min(dp[i][j - 1], dp[i - 1][j]), dp[i - 1][j - 1]) + 1
+                }
+            }
+        }
+        return dp[n][m]
+    }
     ```
 
 === "Zig"
@@ -458,7 +483,35 @@ $$
 === "Swift"
 
     ```swift title="edit_distance.swift"
-    [class]{}-[func]{editDistanceDPComp}
+    /* 编辑距离：状态压缩后的动态规划 */
+    func editDistanceDPComp(s: String, t: String) -> Int {
+        let n = s.utf8CString.count
+        let m = t.utf8CString.count
+        var dp = Array(repeating: 0, count: m + 1)
+        // 状态转移：首行
+        for j in stride(from: 1, through: m, by: 1) {
+            dp[j] = j
+        }
+        // 状态转移：其余行
+        for i in stride(from: 1, through: n, by: 1) {
+            // 状态转移：首列
+            var leftup = dp[0] // 暂存 dp[i-1, j-1]
+            dp[0] = i
+            // 状态转移：其余列
+            for j in stride(from: 1, through: m, by: 1) {
+                let temp = dp[j]
+                if s.utf8CString[i - 1] == t.utf8CString[j - 1] {
+                    // 若两字符相等，则直接跳过此两字符
+                    dp[j] = leftup
+                } else {
+                    // 最少编辑步数 = 插入、删除、替换这三种操作的最少编辑步数 + 1
+                    dp[j] = min(min(dp[j - 1], dp[j]), leftup) + 1
+                }
+                leftup = temp // 更新为下一轮的 dp[i-1, j-1]
+            }
+        }
+        return dp[m]
+    }
     ```
 
 === "Zig"
diff --git a/chapter_dynamic_programming/intro_to_dynamic_programming.md b/chapter_dynamic_programming/intro_to_dynamic_programming.md
index f47a89e62..a675d526a 100644
--- a/chapter_dynamic_programming/intro_to_dynamic_programming.md
+++ b/chapter_dynamic_programming/intro_to_dynamic_programming.md
@@ -170,9 +170,31 @@ status: new
 === "Swift"
 
     ```swift title="climbing_stairs_backtrack.swift"
-    [class]{}-[func]{backtrack}
+    /* 回溯 */
+    func backtrack(choices: [Int], state: Int, n: Int, res: inout [Int]) {
+        // 当爬到第 n 阶时，方案数量加 1
+        if state == n {
+            res[0] += 1
+        }
+        // 遍历所有选择
+        for choice in choices {
+            // 剪枝：不允许越过第 n 阶
+            if state + choice > n {
+                break
+            }
+            backtrack(choices: choices, state: state + choice, n: n, res: &res)
+        }
+    }
 
-    [class]{}-[func]{climbingStairsBacktrack}
+    /* 爬楼梯：回溯 */
+    func climbingStairsBacktrack(n: Int) -> Int {
+        let choices = [1, 2] // 可选择向上爬 1 或 2 阶
+        let state = 0 // 从第 0 阶开始爬
+        var res: [Int] = []
+        res.append(0) // 使用 res[0] 记录方案数量
+        backtrack(choices: choices, state: state, n: n, res: &res)
+        return res[0]
+    }
     ```
 
 === "Zig"
@@ -353,9 +375,21 @@ $$
 === "Swift"
 
     ```swift title="climbing_stairs_dfs.swift"
-    [class]{}-[func]{dfs}
+    /* 搜索 */
+    func dfs(i: Int) -> Int {
+        // 已知 dp[1] 和 dp[2] ，返回之
+        if i == 1 || i == 2 {
+            return i
+        }
+        // dp[i] = dp[i-1] + dp[i-2]
+        let count = dfs(i: i - 1) + dfs(i: i - 2)
+        return count
+    }
 
-    [class]{}-[func]{climbingStairsDFS}
+    /* 爬楼梯：搜索 */
+    func climbingStairsDFS(n: Int) -> Int {
+        dfs(i: n)
+    }
     ```
 
 === "Zig"
@@ -540,9 +574,29 @@ $$
 === "Swift"
 
     ```swift title="climbing_stairs_dfs_mem.swift"
-    [class]{}-[func]{dfs}
+    /* 记忆化搜索 */
+    func dfs(i: Int, mem: inout [Int]) -> Int {
+        // 已知 dp[1] 和 dp[2] ，返回之
+        if i == 1 || i == 2 {
+            return i
+        }
+        // 若存在记录 dp[i] ，则直接返回之
+        if mem[i] != -1 {
+            return mem[i]
+        }
+        // dp[i] = dp[i-1] + dp[i-2]
+        let count = dfs(i: i - 1, mem: &mem) + dfs(i: i - 2, mem: &mem)
+        // 记录 dp[i]
+        mem[i] = count
+        return count
+    }
 
-    [class]{}-[func]{climbingStairsDFSMem}
+    /* 爬楼梯：记忆化搜索 */
+    func climbingStairsDFSMem(n: Int) -> Int {
+        // mem[i] 记录爬到第 i 阶的方案总数，-1 代表无记录
+        var mem = Array(repeating: -1, count: n + 1)
+        return dfs(i: n, mem: &mem)
+    }
     ```
 
 === "Zig"
@@ -699,7 +753,22 @@ $$
 === "Swift"
 
     ```swift title="climbing_stairs_dp.swift"
-    [class]{}-[func]{climbingStairsDP}
+    /* 爬楼梯：动态规划 */
+    func climbingStairsDP(n: Int) -> Int {
+        if n == 1 || n == 2 {
+            return n
+        }
+        // 初始化 dp 表，用于存储子问题的解
+        var dp = Array(repeating: 0, count: n + 1)
+        // 初始状态：预设最小子问题的解
+        dp[1] = 1
+        dp[2] = 2
+        // 状态转移：从较小子问题逐步求解较大子问题
+        for i in stride(from: 3, through: n, by: 1) {
+            dp[i] = dp[i - 1] + dp[i - 2]
+        }
+        return dp[n]
+    }
     ```
 
 === "Zig"
@@ -833,7 +902,18 @@ $$
 === "Swift"
 
     ```swift title="climbing_stairs_dp.swift"
-    [class]{}-[func]{climbingStairsDPComp}
+    /* 爬楼梯：状态压缩后的动态规划 */
+    func climbingStairsDPComp(n: Int) -> Int {
+        if n == 1 || n == 2 {
+            return n
+        }
+        var a = 1
+        var b = 2
+        for _ in stride(from: 3, through: n, by: 1) {
+            (a, b) = (b, a + b)
+        }
+        return b
+    }
     ```
 
 === "Zig"
diff --git a/chapter_dynamic_programming/knapsack_problem.md b/chapter_dynamic_programming/knapsack_problem.md
index 5552a5794..e89e1f1f6 100644
--- a/chapter_dynamic_programming/knapsack_problem.md
+++ b/chapter_dynamic_programming/knapsack_problem.md
@@ -172,7 +172,22 @@ $$
 === "Swift"
 
     ```swift title="knapsack.swift"
-    [class]{}-[func]{knapsackDFS}
+    /* 0-1 背包：暴力搜索 */
+    func knapsackDFS(wgt: [Int], val: [Int], i: Int, c: Int) -> Int {
+        // 若已选完所有物品或背包无容量，则返回价值 0
+        if i == 0 || c == 0 {
+            return 0
+        }
+        // 若超过背包容量，则只能不放入背包
+        if wgt[i - 1] > c {
+            return knapsackDFS(wgt: wgt, val: val, i: i - 1, c: c)
+        }
+        // 计算不放入和放入物品 i 的最大价值
+        let no = knapsackDFS(wgt: wgt, val: val, i: i - 1, c: c)
+        let yes = knapsackDFS(wgt: wgt, val: val, i: i - 1, c: c - wgt[i - 1]) + val[i - 1]
+        // 返回两种方案中价值更大的那一个
+        return max(no, yes)
+    }
     ```
 
 === "Zig"
@@ -343,7 +358,27 @@ $$
 === "Swift"
 
     ```swift title="knapsack.swift"
-    [class]{}-[func]{knapsackDFSMem}
+    /* 0-1 背包：记忆化搜索 */
+    func knapsackDFSMem(wgt: [Int], val: [Int], mem: inout [[Int]], i: Int, c: Int) -> Int {
+        // 若已选完所有物品或背包无容量，则返回价值 0
+        if i == 0 || c == 0 {
+            return 0
+        }
+        // 若已有记录，则直接返回
+        if mem[i][c] != -1 {
+            return mem[i][c]
+        }
+        // 若超过背包容量，则只能不放入背包
+        if wgt[i - 1] > c {
+            return knapsackDFSMem(wgt: wgt, val: val, mem: &mem, i: i - 1, c: c)
+        }
+        // 计算不放入和放入物品 i 的最大价值
+        let no = knapsackDFSMem(wgt: wgt, val: val, mem: &mem, i: i - 1, c: c)
+        let yes = knapsackDFSMem(wgt: wgt, val: val, mem: &mem, i: i - 1, c: c - wgt[i - 1]) + val[i - 1]
+        // 记录并返回两种方案中价值更大的那一个
+        mem[i][c] = max(no, yes)
+        return mem[i][c]
+    }
     ```
 
 === "Zig"
@@ -507,7 +542,25 @@ $$
 === "Swift"
 
     ```swift title="knapsack.swift"
-    [class]{}-[func]{knapsackDP}
+    /* 0-1 背包：动态规划 */
+    func knapsackDP(wgt: [Int], val: [Int], cap: Int) -> Int {
+        let n = wgt.count
+        // 初始化 dp 表
+        var dp = Array(repeating: Array(repeating: 0, count: cap + 1), count: n + 1)
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for c in stride(from: 1, through: cap, by: 1) {
+                if wgt[i - 1] > c {
+                    // 若超过背包容量，则不选物品 i
+                    dp[i][c] = dp[i - 1][c]
+                } else {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[i][c] = max(dp[i - 1][c], dp[i - 1][c - wgt[i - 1]] + val[i - 1])
+                }
+            }
+        }
+        return dp[n][cap]
+    }
     ```
 
 === "Zig"
@@ -727,7 +780,23 @@ $$
 === "Swift"
 
     ```swift title="knapsack.swift"
-    [class]{}-[func]{knapsackDPComp}
+    /* 0-1 背包：状态压缩后的动态规划 */
+    func knapsackDPComp(wgt: [Int], val: [Int], cap: Int) -> Int {
+        let n = wgt.count
+        // 初始化 dp 表
+        var dp = Array(repeating: 0, count: cap + 1)
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            // 倒序遍历
+            for c in stride(from: cap, through: 1, by: -1) {
+                if wgt[i - 1] <= c {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[c] = max(dp[c], dp[c - wgt[i - 1]] + val[i - 1])
+                }
+            }
+        }
+        return dp[cap]
+    }
     ```
 
 === "Zig"
diff --git a/chapter_dynamic_programming/unbounded_knapsack_problem.md b/chapter_dynamic_programming/unbounded_knapsack_problem.md
index 882a5282a..9fe60659e 100644
--- a/chapter_dynamic_programming/unbounded_knapsack_problem.md
+++ b/chapter_dynamic_programming/unbounded_knapsack_problem.md
@@ -154,7 +154,25 @@ $$
 === "Swift"
 
     ```swift title="unbounded_knapsack.swift"
-    [class]{}-[func]{unboundedKnapsackDP}
+    /* 完全背包：动态规划 */
+    func unboundedKnapsackDP(wgt: [Int], val: [Int], cap: Int) -> Int {
+        let n = wgt.count
+        // 初始化 dp 表
+        var dp = Array(repeating: Array(repeating: 0, count: cap + 1), count: n + 1)
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for c in stride(from: 1, through: cap, by: 1) {
+                if wgt[i - 1] > c {
+                    // 若超过背包容量，则不选物品 i
+                    dp[i][c] = dp[i - 1][c]
+                } else {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[i][c] = max(dp[i - 1][c], dp[i][c - wgt[i - 1]] + val[i - 1])
+                }
+            }
+        }
+        return dp[n][cap]
+    }
     ```
 
 === "Zig"
@@ -329,7 +347,25 @@ $$
 === "Swift"
 
     ```swift title="unbounded_knapsack.swift"
-    [class]{}-[func]{unboundedKnapsackDPComp}
+    /* 完全背包：状态压缩后的动态规划 */
+    func unboundedKnapsackDPComp(wgt: [Int], val: [Int], cap: Int) -> Int {
+        let n = wgt.count
+        // 初始化 dp 表
+        var dp = Array(repeating: 0, count: cap + 1)
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for c in stride(from: 1, through: cap, by: 1) {
+                if wgt[i - 1] > c {
+                    // 若超过背包容量，则不选物品 i
+                    dp[c] = dp[c]
+                } else {
+                    // 不选和选物品 i 这两种方案的较大值
+                    dp[c] = max(dp[c], dp[c - wgt[i - 1]] + val[i - 1])
+                }
+            }
+        }
+        return dp[cap]
+    }
     ```
 
 === "Zig"
@@ -368,7 +404,7 @@ $$
 
 !!! question
 
-    给定 $n$ 种硬币，第 $i$ 个硬币的面值为 $coins[i - 1]$ ，为目标金额 $amt$ ，**每种硬币可以重复选取**，问能够凑出目标金额的最少硬币个数。如果无法凑出目标金额则返回 $-1$ 。
+    给定 $n$ 种硬币，第 $i$ 个硬币的面值为 $coins[i - 1]$ ，目标金额为 $amt$ ，**每种硬币可以重复选取**，问能够凑出目标金额的最少硬币个数。如果无法凑出目标金额则返回 $-1$ 。
 
 如下图所示，凑出 $11$ 元最少需要 $3$ 枚硬币，方案为 $1 + 2 + 5 = 11$ 。
 
@@ -547,7 +583,30 @@ $$
 === "Swift"
 
     ```swift title="coin_change.swift"
-    [class]{}-[func]{coinChangeDP}
+    /* 零钱兑换：动态规划 */
+    func coinChangeDP(coins: [Int], amt: Int) -> Int {
+        let n = coins.count
+        let MAX = amt + 1
+        // 初始化 dp 表
+        var dp = Array(repeating: Array(repeating: 0, count: amt + 1), count: n + 1)
+        // 状态转移：首行首列
+        for a in stride(from: 1, through: amt, by: 1) {
+            dp[0][a] = MAX
+        }
+        // 状态转移：其余行列
+        for i in stride(from: 1, through: n, by: 1) {
+            for a in stride(from: 1, through: amt, by: 1) {
+                if coins[i - 1] > a {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[i][a] = dp[i - 1][a]
+                } else {
+                    // 不选和选硬币 i 这两种方案的较小值
+                    dp[i][a] = min(dp[i - 1][a], dp[i][a - coins[i - 1]] + 1)
+                }
+            }
+        }
+        return dp[n][amt] != MAX ? dp[n][amt] : -1
+    }
     ```
 
 === "Zig"
@@ -768,7 +827,27 @@ $$
 === "Swift"
 
     ```swift title="coin_change.swift"
-    [class]{}-[func]{coinChangeDPComp}
+    /* 零钱兑换：状态压缩后的动态规划 */
+    func coinChangeDPComp(coins: [Int], amt: Int) -> Int {
+        let n = coins.count
+        let MAX = amt + 1
+        // 初始化 dp 表
+        var dp = Array(repeating: MAX, count: amt + 1)
+        dp[0] = 0
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for a in stride(from: 1, through: amt, by: 1) {
+                if coins[i - 1] > a {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[a] = dp[a]
+                } else {
+                    // 不选和选硬币 i 这两种方案的较小值
+                    dp[a] = min(dp[a], dp[a - coins[i - 1]] + 1)
+                }
+            }
+        }
+        return dp[amt] != MAX ? dp[amt] : -1
+    }
     ```
 
 === "Zig"
@@ -962,7 +1041,29 @@ $$
 === "Swift"
 
     ```swift title="coin_change_ii.swift"
-    [class]{}-[func]{coinChangeIIDP}
+    /* 零钱兑换 II：动态规划 */
+    func coinChangeIIDP(coins: [Int], amt: Int) -> Int {
+        let n = coins.count
+        // 初始化 dp 表
+        var dp = Array(repeating: Array(repeating: 0, count: amt + 1), count: n + 1)
+        // 初始化首列
+        for i in stride(from: 0, through: n, by: 1) {
+            dp[i][0] = 1
+        }
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for a in stride(from: 1, through: amt, by: 1) {
+                if coins[i - 1] > a {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[i][a] = dp[i - 1][a]
+                } else {
+                    // 不选和选硬币 i 这两种方案之和
+                    dp[i][a] = dp[i - 1][a] + dp[i][a - coins[i - 1]]
+                }
+            }
+        }
+        return dp[n][amt]
+    }
     ```
 
 === "Zig"
@@ -1125,7 +1226,26 @@ $$
 === "Swift"
 
     ```swift title="coin_change_ii.swift"
-    [class]{}-[func]{coinChangeIIDPComp}
+    /* 零钱兑换 II：状态压缩后的动态规划 */
+    func coinChangeIIDPComp(coins: [Int], amt: Int) -> Int {
+        let n = coins.count
+        // 初始化 dp 表
+        var dp = Array(repeating: 0, count: amt + 1)
+        dp[0] = 1
+        // 状态转移
+        for i in stride(from: 1, through: n, by: 1) {
+            for a in stride(from: 1, through: amt, by: 1) {
+                if coins[i - 1] > a {
+                    // 若超过背包容量，则不选硬币 i
+                    dp[a] = dp[a]
+                } else {
+                    // 不选和选硬币 i 这两种方案之和
+                    dp[a] = dp[a] + dp[a - coins[i - 1]]
+                }
+            }
+        }
+        return dp[amt]
+    }
     ```
 
 === "Zig"
diff --git a/chapter_graph/graph_traversal.md b/chapter_graph/graph_traversal.md
index 7e6c0e2c1..33a5f1295 100644
--- a/chapter_graph/graph_traversal.md
+++ b/chapter_graph/graph_traversal.md
@@ -39,9 +39,11 @@ BFS 通常借助「队列」来实现。队列具有“先入先出”的性质
         // 顶点遍历序列
         List<Vertex> res = new ArrayList<>();
         // 哈希表，用于记录已被访问过的顶点
-        Set<Vertex> visited = new HashSet<>() {{ add(startVet); }};
+        Set<Vertex> visited = new HashSet<>();
+        visited.add(startVet);
         // 队列用于实现 BFS
-        Queue<Vertex> que = new LinkedList<>() {{ offer(startVet); }};
+        Queue<Vertex> que = new LinkedList<>();
+        que.offer(startVet);
         // 以顶点 vet 为起点，循环直至访问完所有顶点
         while (!que.isEmpty()) {
             Vertex vet = que.poll(); // 队首顶点出队
@@ -448,6 +450,7 @@ BFS 通常借助「队列」来实现。队列具有“先入先出”的性质
 
     def graph_dfs(graph: GraphAdjList, start_vet: Vertex) -> list[Vertex]:
         """深度优先遍历 DFS"""
+        # 使用邻接表来表示图，以便获取指定顶点的所有邻接顶点
         # 顶点遍历序列
         res = []
         # 哈希表，用于记录已被访问过的顶点
diff --git a/chapter_heap/build_heap.md b/chapter_heap/build_heap.md
index e38a3fe4a..48aef4068 100644
--- a/chapter_heap/build_heap.md
+++ b/chapter_heap/build_heap.md
@@ -217,7 +217,7 @@ $$
 $$
 \begin{aligned}
 T(h) & = 2 \frac{1 - 2^h}{1 - 2} - h \newline
-& = 2^{h+1} - h \newline
+& = 2^{h+1} - h - 2 \newline
 & = O(2^h)
 \end{aligned}
 $$
diff --git a/chapter_tree/binary_tree_traversal.md b/chapter_tree/binary_tree_traversal.md
index 98926e71c..06bafce22 100755
--- a/chapter_tree/binary_tree_traversal.md
+++ b/chapter_tree/binary_tree_traversal.md
@@ -28,7 +28,8 @@ comments: true
     /* 层序遍历 */
     List<Integer> levelOrder(TreeNode root) {
         // 初始化队列，加入根节点
-        Queue<TreeNode> queue = new LinkedList<>() {{ add(root); }};
+        Queue<TreeNode> queue = new LinkedList<>();
+        queue.add(root);
         // 初始化一个列表，用于保存遍历序列
         List<Integer> list = new ArrayList<>();
         while (!queue.isEmpty()) {