diff --git a/codes/java/chapter_dynamic_programming/min_path_sum.java b/codes/java/chapter_dynamic_programming/min_path_sum.java
index 37ff22d2..d8bc9923 100644
--- a/codes/java/chapter_dynamic_programming/min_path_sum.java
+++ b/codes/java/chapter_dynamic_programming/min_path_sum.java
@@ -104,7 +104,7 @@ public class min_path_sum {
 
         // 暴力搜索
         int res = minPathSumDFS(grid, n - 1, m - 1);
-        System.out.println("从左上角到右下角的做小路径和为 " + res);
+        System.out.println("从左上角到右下角的最小路径和为 " + res);
 
         // 记忆化搜索
         int[][] mem = new int[n][m];
@@ -112,14 +112,14 @@ public class min_path_sum {
             Arrays.fill(row, -1);
         }
         res = minPathSumDFSMem(grid, mem, n - 1, m - 1);
-        System.out.println("从左上角到右下角的做小路径和为 " + res);
+        System.out.println("从左上角到右下角的最小路径和为 " + res);
 
         // 动态规划
         res = minPathSumDP(grid);
-        System.out.println("从左上角到右下角的做小路径和为 " + res);
+        System.out.println("从左上角到右下角的最小路径和为 " + res);
 
         // 空间优化后的动态规划
         res = minPathSumDPComp(grid);
-        System.out.println("从左上角到右下角的做小路径和为 " + res);
+        System.out.println("从左上角到右下角的最小路径和为 " + res);
     }
 }
diff --git a/codes/javascript/chapter_dynamic_programming/climbing_stairs_constraint_dp.js b/codes/javascript/chapter_dynamic_programming/climbing_stairs_constraint_dp.js
new file mode 100644
index 00000000..67f024f1
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/climbing_stairs_constraint_dp.js
@@ -0,0 +1,30 @@
+/**
+ * File: climbing_stairs_constraint_dp.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 带约束爬楼梯：动态规划 */
+function climbingStairsConstraintDP(n) {
+    if (n === 1 || n === 2) {
+        return n;
+    }
+    // 初始化 dp 表，用于存储子问题的解
+    const dp = Array.from(new Array(n + 1), () => new Array(3));
+    // 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 (let i = 3; i <= n; i++) {
+        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];
+}
+
+/* Driver Code */
+const n = 9;
+const res = climbingStairsConstraintDP(n);
+console.log(`爬 ${n} 阶楼梯共有 ${res} 种方案`);
diff --git a/codes/javascript/chapter_dynamic_programming/coin_change.js b/codes/javascript/chapter_dynamic_programming/coin_change.js
new file mode 100644
index 00000000..2aa5fc44
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/coin_change.js
@@ -0,0 +1,66 @@
+/**
+ * File: coin_change.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 零钱兑换：动态规划 */
+function coinChangeDP(coins, amt) {
+    const n = coins.length;
+    const MAX = amt + 1;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: amt + 1 }, () => 0)
+    );
+    // 状态转移：首行首列
+    for (let a = 1; a <= amt; a++) {
+        dp[0][a] = MAX;
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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] = Math.min(dp[i - 1][a], dp[i][a - coins[i - 1]] + 1);
+            }
+        }
+    }
+    return dp[n][amt] !== MAX ? dp[n][amt] : -1;
+}
+
+/* 零钱兑换：状态压缩后的动态规划 */
+function coinChangeDPComp(coins, amt) {
+    const n = coins.length;
+    const MAX = amt + 1;
+    // 初始化 dp 表
+    const dp = Array.from({ length: amt + 1 }, () => MAX);
+    dp[0] = 0;
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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] = Math.min(dp[a], dp[a - coins[i - 1]] + 1);
+            }
+        }
+    }
+    return dp[amt] !== MAX ? dp[amt] : -1;
+}
+
+/* Driver Code */
+const coins = [1, 2, 5];
+const amt = 4;
+
+// 动态规划
+let res = coinChangeDP(coins, amt);
+console.log(`凑到目标金额所需的最少硬币数量为 ${res}`);
+
+// 状态压缩后的动态规划
+res = coinChangeDPComp(coins, amt);
+console.log(`凑到目标金额所需的最少硬币数量为 ${res}`);
diff --git a/codes/javascript/chapter_dynamic_programming/coin_change_ii.js b/codes/javascript/chapter_dynamic_programming/coin_change_ii.js
new file mode 100644
index 00000000..92d29487
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/coin_change_ii.js
@@ -0,0 +1,64 @@
+/**
+ * File: coin_change_ii.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 零钱兑换 II：动态规划 */
+function coinChangeIIDP(coins, amt) {
+    const n = coins.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: amt + 1 }, () => 0)
+    );
+    // 初始化首列
+    for (let i = 0; i <= n; i++) {
+        dp[i][0] = 1;
+    }
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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];
+}
+
+/* 零钱兑换 II：状态压缩后的动态规划 */
+function coinChangeIIDPComp(coins, amt) {
+    const n = coins.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: amt + 1 }, () => 0);
+    dp[0] = 1;
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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];
+}
+
+/* Driver Code */
+const coins = [1, 2, 5];
+const amt = 5;
+
+// 动态规划
+let res = coinChangeIIDP(coins, amt);
+console.log(`凑出目标金额的硬币组合数量为 ${res}`);
+
+// 状态压缩后的动态规划
+res = coinChangeIIDPComp(coins, amt);
+console.log(`凑出目标金额的硬币组合数量为 ${res}`);
diff --git a/codes/javascript/chapter_dynamic_programming/edit_distance.js b/codes/javascript/chapter_dynamic_programming/edit_distance.js
new file mode 100644
index 00000000..a8dd3655
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/edit_distance.js
@@ -0,0 +1,138 @@
+/**
+ * File: edit_distance.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 编辑距离：暴力搜索 */
+function editDistanceDFS(s, t, i, j) {
+    // 若 s 和 t 都为空，则返回 0
+    if (i === 0 && j === 0) return 0;
+
+    // 若 s 为空，则返回 t 长度
+    if (i === 0) return j;
+
+    // 若 t 为空，则返回 s 长度
+    if (j === 0) return i;
+
+    // 若两字符相等，则直接跳过此两字符
+    if (s.charAt(i - 1) === t.charAt(j - 1))
+        return editDistanceDFS(s, t, i - 1, j - 1);
+
+    // 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
+    const insert = editDistanceDFS(s, t, i, j - 1);
+    const del = editDistanceDFS(s, t, i - 1, j);
+    const replace = editDistanceDFS(s, t, i - 1, j - 1);
+    // 返回最少编辑步数
+    return Math.min(Math.min(insert, del), replace) + 1;
+}
+
+/* 编辑距离：记忆化搜索 */
+function editDistanceDFSMem(s, t, mem, i, j) {
+    // 若 s 和 t 都为空，则返回 0
+    if (i === 0 && j === 0) return 0;
+
+    // 若 s 为空，则返回 t 长度
+    if (i === 0) return j;
+
+    // 若 t 为空，则返回 s 长度
+    if (j === 0) return i;
+
+    // 若已有记录，则直接返回之
+    if (mem[i][j] !== -1) return mem[i][j];
+
+    // 若两字符相等，则直接跳过此两字符
+    if (s.charAt(i - 1) === t.charAt(j - 1))
+        return editDistanceDFSMem(s, t, mem, i - 1, j - 1);
+
+    // 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
+    const insert = editDistanceDFSMem(s, t, mem, i, j - 1);
+    const del = editDistanceDFSMem(s, t, mem, i - 1, j);
+    const replace = editDistanceDFSMem(s, t, mem, i - 1, j - 1);
+    // 记录并返回最少编辑步数
+    mem[i][j] = Math.min(Math.min(insert, del), replace) + 1;
+    return mem[i][j];
+}
+
+/* 编辑距离：动态规划 */
+function editDistanceDP(s, t) {
+    const n = s.length,
+        m = t.length;
+    const dp = Array.from({ length: n + 1 }, () => new Array(m + 1).fill(0));
+    // 状态转移：首行首列
+    for (let i = 1; i <= n; i++) {
+        dp[i][0] = i;
+    }
+    for (let j = 1; j <= m; j++) {
+        dp[0][j] = j;
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i <= n; i++) {
+        for (let j = 1; j <= m; j++) {
+            if (s.charAt(i - 1) === t.charAt(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] =
+                    Math.min(
+                        Math.min(dp[i][j - 1], dp[i - 1][j]),
+                        dp[i - 1][j - 1]
+                    ) + 1;
+            }
+        }
+    }
+    return dp[n][m];
+}
+
+/* 编辑距离：状态压缩后的动态规划 */
+function editDistanceDPComp(s, t) {
+    const n = s.length,
+        m = t.length;
+    const dp = new Array(m + 1).fill(0);
+    // 状态转移：首行
+    for (let j = 1; j <= m; j++) {
+        dp[j] = j;
+    }
+    // 状态转移：其余行
+    for (let i = 1; i <= n; i++) {
+        // 状态转移：首列
+        let leftup = dp[0]; // 暂存 dp[i-1, j-1]
+        dp[0] = i;
+        // 状态转移：其余列
+        for (let j = 1; j <= m; j++) {
+            const temp = dp[j];
+            if (s.charAt(i - 1) === t.charAt(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] = Math.min(Math.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];
+}
+
+const s = 'bag';
+const t = 'pack';
+const n = s.length,
+    m = t.length;
+
+// 暴力搜索
+let res = editDistanceDFS(s, t, n, m);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 记忆化搜索
+const mem = Array.from(new Array(n + 1), () => new Array(m + 1).fill(-1));
+res = editDistanceDFSMem(s, t, mem, n, m);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 动态规划
+res = editDistanceDP(s, t);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 状态压缩后的动态规划
+res = editDistanceDPComp(s, t);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
diff --git a/codes/javascript/chapter_dynamic_programming/knapsack.js b/codes/javascript/chapter_dynamic_programming/knapsack.js
new file mode 100644
index 00000000..885fc54d
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/knapsack.js
@@ -0,0 +1,112 @@
+/**
+ * File: knapsack.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 0-1 背包：暴力搜索 */
+function knapsackDFS(wgt, val, i, c) {
+    // 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, val, i - 1, c);
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    const no = knapsackDFS(wgt, val, i - 1, c);
+    const yes = knapsackDFS(wgt, val, i - 1, c - wgt[i - 1]) + val[i - 1];
+    // 返回两种方案中价值更大的那一个
+    return Math.max(no, yes);
+}
+
+/* 0-1 背包：记忆化搜索 */
+function knapsackDFSMem(wgt, val, mem, i, c) {
+    // 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, val, mem, i - 1, c);
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    const no = knapsackDFSMem(wgt, val, mem, i - 1, c);
+    const yes = knapsackDFSMem(wgt, val, mem, i - 1, 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] = Math.max(no, yes);
+    return mem[i][c];
+}
+
+/* 0-1 背包：动态规划 */
+function knapsackDP(wgt, val, cap) {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array(n + 1)
+        .fill(0)
+        .map(() => Array(cap + 1).fill(0));
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(
+                    dp[i - 1][c],
+                    dp[i - 1][c - wgt[i - 1]] + val[i - 1]
+                );
+            }
+        }
+    }
+    return dp[n][cap];
+}
+
+/* 0-1 背包：状态压缩后的动态规划 */
+function knapsackDPComp(wgt, val, cap) {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array(cap + 1).fill(0);
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        // 倒序遍历
+        for (let c = cap; c >= 1; c--) {
+            if (wgt[i - 1] <= c) {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[c] = Math.max(dp[c], dp[c - wgt[i - 1]] + val[i - 1]);
+            }
+        }
+    }
+    return dp[cap];
+}
+
+/* Driver Code */
+const wgt = [10, 20, 30, 40, 50];
+const val = [50, 120, 150, 210, 240];
+const cap = 50;
+const n = wgt.length;
+
+// 暴力搜索
+let res = knapsackDFS(wgt, val, n, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 记忆化搜索
+const mem = Array.from({ length: n + 1 }, () =>
+    Array.from({ length: cap + 1 }, () => -1)
+);
+res = knapsackDFSMem(wgt, val, mem, n, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 动态规划
+res = knapsackDP(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 状态压缩后的动态规划
+res = knapsackDPComp(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
diff --git a/codes/javascript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.js b/codes/javascript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.js
new file mode 100644
index 00000000..0fc26970
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.js
@@ -0,0 +1,49 @@
+/**
+ * File: min_cost_climbing_stairs_dp.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 爬楼梯最小代价：动态规划 */
+function minCostClimbingStairsDP(cost) {
+    const n = cost.length - 1;
+    if (n === 1 || n === 2) {
+        return cost[n];
+    }
+    // 初始化 dp 表，用于存储子问题的解
+    const dp = new Array(n + 1);
+    // 初始状态：预设最小子问题的解
+    dp[1] = cost[1];
+    dp[2] = cost[2];
+    // 状态转移：从较小子问题逐步求解较大子问题
+    for (let i = 3; i <= n; i++) {
+        dp[i] = Math.min(dp[i - 1], dp[i - 2]) + cost[i];
+    }
+    return dp[n];
+}
+
+/* 爬楼梯最小代价：状态压缩后的动态规划 */
+function minCostClimbingStairsDPComp(cost) {
+    const n = cost.length - 1;
+    if (n === 1 || n === 2) {
+        return cost[n];
+    }
+    let a = cost[1],
+        b = cost[2];
+    for (let i = 3; i <= n; i++) {
+        const tmp = b;
+        b = Math.min(a, tmp) + cost[i];
+        a = tmp;
+    }
+    return b;
+}
+
+/* Driver Code */
+const cost = [0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1];
+console.log('输入楼梯的代价列表为：', cost);
+
+let res = minCostClimbingStairsDP(cost);
+console.log(`爬完楼梯的最低代价为：${res}`);
+
+res = minCostClimbingStairsDPComp(cost);
+console.log(`爬完楼梯的最低代价为：${res}`);
diff --git a/codes/javascript/chapter_dynamic_programming/min_path_sum.js b/codes/javascript/chapter_dynamic_programming/min_path_sum.js
new file mode 100644
index 00000000..7f6a473b
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/min_path_sum.js
@@ -0,0 +1,121 @@
+/**
+ * File: min_path_sum.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 最小路径和：暴力搜索 */
+function minPathSumDFS(grid, i, j) {
+    // 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 Infinity;
+    }
+    // 计算从左上角到 (i-1, j) 和 (i, j-1) 的最小路径代价
+    const left = minPathSumDFS(grid, i - 1, j);
+    const up = minPathSumDFS(grid, i, j - 1);
+    // 返回从左上角到 (i, j) 的最小路径代价
+    return Math.min(left, up) + grid[i][j];
+}
+
+/* 最小路径和：记忆化搜索 */
+function minPathSumDFSMem(grid, mem, i, j) {
+    // 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 Infinity;
+    }
+    // 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
+    const left = minPathSumDFSMem(grid, mem, i - 1, j);
+    const up = minPathSumDFSMem(grid, mem, i, j - 1);
+    // 记录并返回左上角到 (i, j) 的最小路径代价
+    mem[i][j] = Math.min(left, up) + grid[i][j];
+    return mem[i][j];
+}
+
+/* 最小路径和：动态规划 */
+function minPathSumDP(grid) {
+    const n = grid.length,
+        m = grid[0].length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n }, () =>
+        Array.from({ length: m }, () => 0)
+    );
+    dp[0][0] = grid[0][0];
+    // 状态转移：首行
+    for (let j = 1; j < m; j++) {
+        dp[0][j] = dp[0][j - 1] + grid[0][j];
+    }
+    // 状态转移：首列
+    for (let i = 1; i < n; i++) {
+        dp[i][0] = dp[i - 1][0] + grid[i][0];
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i < n; i++) {
+        for (let j = 1; j < m; j++) {
+            dp[i][j] = Math.min(dp[i][j - 1], dp[i - 1][j]) + grid[i][j];
+        }
+    }
+    return dp[n - 1][m - 1];
+}
+
+/* 最小路径和：状态压缩后的动态规划 */
+function minPathSumDPComp(grid) {
+    const n = grid.length,
+        m = grid[0].length;
+    // 初始化 dp 表
+    const dp = new Array(m);
+    // 状态转移：首行
+    dp[0] = grid[0][0];
+    for (let j = 1; j < m; j++) {
+        dp[j] = dp[j - 1] + grid[0][j];
+    }
+    // 状态转移：其余行
+    for (let i = 1; i < n; i++) {
+        // 状态转移：首列
+        dp[0] = dp[0] + grid[i][0];
+        // 状态转移：其余列
+        for (let j = 1; j < m; j++) {
+            dp[j] = Math.min(dp[j - 1], dp[j]) + grid[i][j];
+        }
+    }
+    return dp[m - 1];
+}
+
+/* Driver Code */
+const grid = [
+    [1, 3, 1, 5],
+    [2, 2, 4, 2],
+    [5, 3, 2, 1],
+    [4, 3, 5, 2],
+]
+const n = grid.length,
+    m = grid[0].length;
+// 暴力搜索
+let res = minPathSumDFS(grid, n - 1, m - 1);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 记忆化搜索
+const mem = Array.from({ length: n }, () =>
+    Array.from({ length: m }, () => -1)
+);
+res = minPathSumDFSMem(grid, mem, n - 1, m - 1);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 动态规划
+res = minPathSumDP(grid);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 状态压缩后的动态规划
+res = minPathSumDPComp(grid);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
\ No newline at end of file
diff --git a/codes/javascript/chapter_dynamic_programming/unbounded_knapsack.js b/codes/javascript/chapter_dynamic_programming/unbounded_knapsack.js
new file mode 100644
index 00000000..582ee460
--- /dev/null
+++ b/codes/javascript/chapter_dynamic_programming/unbounded_knapsack.js
@@ -0,0 +1,63 @@
+/**
+ * File: unbounded_knapsack.js
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 完全背包：动态规划 */
+function unboundedKnapsackDP(wgt, val, cap) {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: cap + 1 }, () => 0)
+    );
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(
+                    dp[i - 1][c],
+                    dp[i][c - wgt[i - 1]] + val[i - 1]
+                );
+            }
+        }
+    }
+    return dp[n][cap];
+}
+
+/* 完全背包：状态压缩后的动态规划 */
+function unboundedKnapsackDPComp(wgt, val, cap) {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: cap + 1 }, () => 0);
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(dp[c], dp[c - wgt[i - 1]] + val[i - 1]);
+            }
+        }
+    }
+    return dp[cap];
+}
+
+/* Driver Code */
+const wgt = [1, 2, 3];
+const val = [5, 11, 15];
+const cap = 4;
+
+// 动态规划
+let res = unboundedKnapsackDP(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 状态压缩后的动态规划
+res = unboundedKnapsackDPComp(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
diff --git a/codes/typescript/chapter_backtracking/n_queens.ts b/codes/typescript/chapter_backtracking/n_queens.ts
index 702ce907..3aab291d 100644
--- a/codes/typescript/chapter_backtracking/n_queens.ts
+++ b/codes/typescript/chapter_backtracking/n_queens.ts
@@ -61,3 +61,5 @@ res.forEach((state) => {
     console.log('--------------------');
     state.forEach((row) => console.log(row));
 });
+
+export {};
\ No newline at end of file
diff --git a/codes/typescript/chapter_dynamic_programming/climbing_stairs_constraint_dp.ts b/codes/typescript/chapter_dynamic_programming/climbing_stairs_constraint_dp.ts
new file mode 100644
index 00000000..2f3b2118
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/climbing_stairs_constraint_dp.ts
@@ -0,0 +1,35 @@
+/**
+ * File: climbing_stairs_constraint_dp.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 带约束爬楼梯：动态规划 */
+function climbingStairsConstraintDP(n: number): number {
+    if (n === 1 || n === 2) {
+        return n;
+    }
+    // 初始化 dp 表，用于存储子问题的解
+    const dp = Array.from(
+        { length: n + 1 },
+        () => new Array(3)
+    );
+    // 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 (let i = 3; i <= n; i++) {
+        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];
+}
+
+/* Driver Code */
+const n = 9;
+const res = climbingStairsConstraintDP(n);
+console.log(`爬 ${n} 阶楼梯共有 ${res} 种方案`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/coin_change.ts b/codes/typescript/chapter_dynamic_programming/coin_change.ts
new file mode 100644
index 00000000..09141cd7
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/coin_change.ts
@@ -0,0 +1,68 @@
+/**
+ * File: coin_change.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 零钱兑换：动态规划 */
+function coinChangeDP(coins: Array<number>, amt: number): number {
+    const n = coins.length;
+    const MAX = amt + 1;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: amt + 1 }, () => 0)
+    );
+    // 状态转移：首行首列
+    for (let a = 1; a <= amt; a++) {
+        dp[0][a] = MAX;
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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] = Math.min(dp[i - 1][a], dp[i][a - coins[i - 1]] + 1);
+            }
+        }
+    }
+    return dp[n][amt] !== MAX ? dp[n][amt] : -1;
+}
+
+/* 零钱兑换：状态压缩后的动态规划 */
+function coinChangeDPComp(coins: Array<number>, amt: number): number {
+    const n = coins.length;
+    const MAX = amt + 1;
+    // 初始化 dp 表
+    const dp = Array.from({ length: amt + 1 }, () => MAX);
+    dp[0] = 0;
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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] = Math.min(dp[a], dp[a - coins[i - 1]] + 1);
+            }
+        }
+    }
+    return dp[amt] !== MAX ? dp[amt] : -1;
+}
+
+/* Driver Code */
+const coins = [1, 2, 5];
+const amt = 4;
+
+// 动态规划
+let res = coinChangeDP(coins, amt);
+console.log(`凑到目标金额所需的最少硬币数量为 ${res}`);
+
+// 状态压缩后的动态规划
+res = coinChangeDPComp(coins, amt);
+console.log(`凑到目标金额所需的最少硬币数量为 ${res}`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/coin_change_ii.ts b/codes/typescript/chapter_dynamic_programming/coin_change_ii.ts
new file mode 100644
index 00000000..55ba0af4
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/coin_change_ii.ts
@@ -0,0 +1,66 @@
+/**
+ * File: coin_change_ii.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 零钱兑换 II：动态规划 */
+function coinChangeIIDP(coins: Array<number>, amt: number): number {
+    const n = coins.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: amt + 1 }, () => 0)
+    );
+    // 初始化首列
+    for (let i = 0; i <= n; i++) {
+        dp[i][0] = 1;
+    }
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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];
+}
+
+/* 零钱兑换 II：状态压缩后的动态规划 */
+function coinChangeIIDPComp(coins: Array<number>, amt: number): number {
+    const n = coins.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: amt + 1 }, () => 0);
+    dp[0] = 1;
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let a = 1; a <= amt; a++) {
+            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];
+}
+
+/* Driver Code */
+const coins = [1, 2, 5];
+const amt = 5;
+
+// 动态规划
+let res = coinChangeIIDP(coins, amt);
+console.log(`凑出目标金额的硬币组合数量为 ${res}`);
+
+// 状态压缩后的动态规划
+res = coinChangeIIDPComp(coins, amt);
+console.log(`凑出目标金额的硬币组合数量为 ${res}`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/edit_distance.ts b/codes/typescript/chapter_dynamic_programming/edit_distance.ts
new file mode 100644
index 00000000..ef91b13a
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/edit_distance.ts
@@ -0,0 +1,151 @@
+/**
+ * File: edit_distance.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 编辑距离：暴力搜索 */
+function editDistanceDFS(s: string, t: string, i: number, j: number): number {
+    // 若 s 和 t 都为空，则返回 0
+    if (i === 0 && j === 0) return 0;
+
+    // 若 s 为空，则返回 t 长度
+    if (i === 0) return j;
+
+    // 若 t 为空，则返回 s 长度
+    if (j === 0) return i;
+
+    // 若两字符相等，则直接跳过此两字符
+    if (s.charAt(i - 1) === t.charAt(j - 1))
+        return editDistanceDFS(s, t, i - 1, j - 1);
+
+    // 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
+    const insert = editDistanceDFS(s, t, i, j - 1);
+    const del = editDistanceDFS(s, t, i - 1, j);
+    const replace = editDistanceDFS(s, t, i - 1, j - 1);
+    // 返回最少编辑步数
+    return Math.min(Math.min(insert, del), replace) + 1;
+}
+
+/* 编辑距离：记忆化搜索 */
+function editDistanceDFSMem(
+    s: string,
+    t: string,
+    mem: Array<Array<number>>,
+    i: number,
+    j: number
+): number {
+    // 若 s 和 t 都为空，则返回 0
+    if (i === 0 && j === 0) return 0;
+
+    // 若 s 为空，则返回 t 长度
+    if (i === 0) return j;
+
+    // 若 t 为空，则返回 s 长度
+    if (j === 0) return i;
+
+    // 若已有记录，则直接返回之
+    if (mem[i][j] !== -1) return mem[i][j];
+
+    // 若两字符相等，则直接跳过此两字符
+    if (s.charAt(i - 1) === t.charAt(j - 1))
+        return editDistanceDFSMem(s, t, mem, i - 1, j - 1);
+
+    // 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
+    const insert = editDistanceDFSMem(s, t, mem, i, j - 1);
+    const del = editDistanceDFSMem(s, t, mem, i - 1, j);
+    const replace = editDistanceDFSMem(s, t, mem, i - 1, j - 1);
+    // 记录并返回最少编辑步数
+    mem[i][j] = Math.min(Math.min(insert, del), replace) + 1;
+    return mem[i][j];
+}
+
+/* 编辑距离：动态规划 */
+function editDistanceDP(s: string, t: string): number {
+    const n = s.length,
+        m = t.length;
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: m + 1 }, () => 0)
+    );
+    // 状态转移：首行首列
+    for (let i = 1; i <= n; i++) {
+        dp[i][0] = i;
+    }
+    for (let j = 1; j <= m; j++) {
+        dp[0][j] = j;
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i <= n; i++) {
+        for (let j = 1; j <= m; j++) {
+            if (s.charAt(i - 1) === t.charAt(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] =
+                    Math.min(
+                        Math.min(dp[i][j - 1], dp[i - 1][j]),
+                        dp[i - 1][j - 1]
+                    ) + 1;
+            }
+        }
+    }
+    return dp[n][m];
+}
+
+/* 编辑距离：状态压缩后的动态规划 */
+function editDistanceDPComp(s: string, t: string): number {
+    const n = s.length,
+        m = t.length;
+    const dp = new Array(m + 1).fill(0);
+    // 状态转移：首行
+    for (let j = 1; j <= m; j++) {
+        dp[j] = j;
+    }
+    // 状态转移：其余行
+    for (let i = 1; i <= n; i++) {
+        // 状态转移：首列
+        let leftup = dp[0]; // 暂存 dp[i-1, j-1]
+        dp[0] = i;
+        // 状态转移：其余列
+        for (let j = 1; j <= m; j++) {
+            const temp = dp[j];
+            if (s.charAt(i - 1) === t.charAt(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] = Math.min(Math.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];
+}
+
+/* Driver Code */
+const s = 'bag';
+const t = 'pack';
+const n = s.length,
+    m = t.length;
+
+// 暴力搜索
+let res = editDistanceDFS(s, t, n, m);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 记忆化搜索
+const mem = Array.from({ length: n + 1 }, () =>
+    Array.from({ length: m + 1 }, () => -1)
+);
+res = editDistanceDFSMem(s, t, mem, n, m);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 动态规划
+res = editDistanceDP(s, t);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+// 状态压缩后的动态规划
+res = editDistanceDPComp(s, t);
+console.log(`将 ${s} 更改为 ${t} 最少需要编辑 ${res} 步`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/knapsack.ts b/codes/typescript/chapter_dynamic_programming/knapsack.ts
new file mode 100644
index 00000000..39ced7f3
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/knapsack.ts
@@ -0,0 +1,135 @@
+/**
+ * File: knapsack.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 0-1 背包：暴力搜索 */
+function knapsackDFS(
+    wgt: Array<number>,
+    val: Array<number>,
+    i: number,
+    c: number
+): number {
+    // 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, val, i - 1, c);
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    const no = knapsackDFS(wgt, val, i - 1, c);
+    const yes =
+        knapsackDFS(wgt, val, i - 1, c - wgt[i - 1]) + val[i - 1];
+    // 返回两种方案中价值更大的那一个
+    return Math.max(no, yes);
+}
+
+/* 0-1 背包：记忆化搜索 */
+function knapsackDFSMem(
+    wgt: Array<number>,
+    val: Array<number>,
+    mem: Array<Array<number>>,
+    i: number,
+    c: number
+): number {
+    // 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, val, mem, i - 1, c);
+    }
+    // 计算不放入和放入物品 i 的最大价值
+    const no = knapsackDFSMem(wgt, val, mem, i - 1, c);
+    const yes =
+        knapsackDFSMem(wgt, val, mem, i - 1, 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] = Math.max(no, yes);
+    return mem[i][c];
+}
+
+/* 0-1 背包：动态规划 */
+function knapsackDP(
+    wgt: Array<number>,
+    val: Array<number>,
+    cap: number
+): number {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: cap + 1 }, () => 0)
+    );
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(
+                    dp[i - 1][c],
+                    dp[i - 1][c - wgt[i - 1]] + val[i - 1]
+                );
+            }
+        }
+    }
+    return dp[n][cap];
+}
+
+/* 0-1 背包：状态压缩后的动态规划 */
+function knapsackDPComp(
+    wgt: Array<number>,
+    val: Array<number>,
+    cap: number
+): number {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array(cap + 1).fill(0);
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        // 倒序遍历
+        for (let c = cap; c >= 1; c--) {
+            if (wgt[i - 1] <= c) {
+                // 不选和选物品 i 这两种方案的较大值
+                dp[c] = Math.max(dp[c], dp[c - wgt[i - 1]] + val[i - 1]);
+            }
+        }
+    }
+    return dp[cap];
+}
+
+/* Driver Code */
+const wgt = [10, 20, 30, 40, 50];
+const val = [50, 120, 150, 210, 240];
+const cap = 50;
+const n = wgt.length;
+
+// 暴力搜索
+let res = knapsackDFS(wgt, val, n, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 记忆化搜索
+const mem = Array.from({ length: n + 1 }, () =>
+    Array.from({ length: cap + 1 }, () => -1)
+);
+res = knapsackDFSMem(wgt, val, mem, n, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 动态规划
+res = knapsackDP(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 状态压缩后的动态规划
+res = knapsackDPComp(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.ts b/codes/typescript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.ts
new file mode 100644
index 00000000..ade829b5
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/min_cost_climbing_stairs_dp.ts
@@ -0,0 +1,51 @@
+/**
+ * File: min_cost_climbing_stairs_dp.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 爬楼梯最小代价：动态规划 */
+function minCostClimbingStairsDP(cost: Array<number>): number {
+    const n = cost.length - 1;
+    if (n === 1 || n === 2) {
+        return cost[n];
+    }
+    // 初始化 dp 表，用于存储子问题的解
+    const dp = new Array(n + 1);
+    // 初始状态：预设最小子问题的解
+    dp[1] = cost[1];
+    dp[2] = cost[2];
+    // 状态转移：从较小子问题逐步求解较大子问题
+    for (let i = 3; i <= n; i++) {
+        dp[i] = Math.min(dp[i - 1], dp[i - 2]) + cost[i];
+    }
+    return dp[n];
+}
+
+/* 爬楼梯最小代价：状态压缩后的动态规划 */
+function minCostClimbingStairsDPComp(cost: Array<number>): number {
+    const n = cost.length - 1;
+    if (n === 1 || n === 2) {
+        return cost[n];
+    }
+    let a = cost[1],
+        b = cost[2];
+    for (let i = 3; i <= n; i++) {
+        const tmp = b;
+        b = Math.min(a, tmp) + cost[i];
+        a = tmp;
+    }
+    return b;
+}
+
+/* Driver Code */
+const cost = [0, 1, 10, 1, 1, 1, 10, 1, 1, 10, 1];
+console.log(`输入楼梯的代价列表为：${cost}`);
+
+let res = minCostClimbingStairsDP(cost);
+console.log(`爬完楼梯的最低代价为：${res}`);
+
+res = minCostClimbingStairsDPComp(cost);
+console.log(`爬完楼梯的最低代价为：${res}`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/min_path_sum.ts b/codes/typescript/chapter_dynamic_programming/min_path_sum.ts
new file mode 100644
index 00000000..b0a08df2
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/min_path_sum.ts
@@ -0,0 +1,132 @@
+/**
+ * File: min_path_sum.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 最小路径和：暴力搜索 */
+function minPathSumDFS(
+    grid: Array<Array<number>>,
+    i: number,
+    j: number
+): number {
+    // 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 Infinity;
+    }
+    // 计算从左上角到 (i-1, j) 和 (i, j-1) 的最小路径代价
+    const left = minPathSumDFS(grid, i - 1, j);
+    const up = minPathSumDFS(grid, i, j - 1);
+    // 返回从左上角到 (i, j) 的最小路径代价
+    return Math.min(left, up) + grid[i][j];
+}
+
+/* 最小路径和：记忆化搜索 */
+function minPathSumDFSMem(
+    grid: Array<Array<number>>,
+    mem: Array<Array<number>>,
+    i: number,
+    j: number
+): number {
+    // 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 Infinity;
+    }
+    // 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
+    const left = minPathSumDFSMem(grid, mem, i - 1, j);
+    const up = minPathSumDFSMem(grid, mem, i, j - 1);
+    // 记录并返回左上角到 (i, j) 的最小路径代价
+    mem[i][j] = Math.min(left, up) + grid[i][j];
+    return mem[i][j];
+}
+
+/* 最小路径和：动态规划 */
+function minPathSumDP(grid: Array<Array<number>>): number {
+    const n = grid.length,
+        m = grid[0].length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n }, () =>
+        Array.from({ length: m }, () => 0)
+    );
+    dp[0][0] = grid[0][0];
+    // 状态转移：首行
+    for (let j = 1; j < m; j++) {
+        dp[0][j] = dp[0][j - 1] + grid[0][j];
+    }
+    // 状态转移：首列
+    for (let i = 1; i < n; i++) {
+        dp[i][0] = dp[i - 1][0] + grid[i][0];
+    }
+    // 状态转移：其余行列
+    for (let i = 1; i < n; i++) {
+        for (let j: number = 1; j < m; j++) {
+            dp[i][j] = Math.min(dp[i][j - 1], dp[i - 1][j]) + grid[i][j];
+        }
+    }
+    return dp[n - 1][m - 1];
+}
+
+/* 最小路径和：状态压缩后的动态规划 */
+function minPathSumDPComp(grid: Array<Array<number>>): number {
+    const n = grid.length,
+        m = grid[0].length;
+    // 初始化 dp 表
+    const dp = new Array(m);
+    // 状态转移：首行
+    dp[0] = grid[0][0];
+    for (let j = 1; j < m; j++) {
+        dp[j] = dp[j - 1] + grid[0][j];
+    }
+    // 状态转移：其余行
+    for (let i = 1; i < n; i++) {
+        // 状态转移：首列
+        dp[0] = dp[0] + grid[i][0];
+        // 状态转移：其余列
+        for (let j = 1; j < m; j++) {
+            dp[j] = Math.min(dp[j - 1], dp[j]) + grid[i][j];
+        }
+    }
+    return dp[m - 1];
+}
+
+/* Driver Code */
+const grid = [
+    [1, 3, 1, 5],
+    [2, 2, 4, 2],
+    [5, 3, 2, 1],
+    [4, 3, 5, 2],
+];
+const n = grid.length,
+    m = grid[0].length;
+// 暴力搜索
+let res = minPathSumDFS(grid, n - 1, m - 1);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 记忆化搜索
+const mem = Array.from({ length: n }, () =>
+    Array.from({ length: m }, () => -1)
+);
+res = minPathSumDFSMem(grid, mem, n - 1, m - 1);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 动态规划
+res = minPathSumDP(grid);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+// 状态压缩后的动态规划
+res = minPathSumDPComp(grid);
+console.log(`从左上角到右下角的最小路径和为 ${res}`);
+
+export {};
diff --git a/codes/typescript/chapter_dynamic_programming/unbounded_knapsack.ts b/codes/typescript/chapter_dynamic_programming/unbounded_knapsack.ts
new file mode 100644
index 00000000..9005b99b
--- /dev/null
+++ b/codes/typescript/chapter_dynamic_programming/unbounded_knapsack.ts
@@ -0,0 +1,73 @@
+/**
+ * File: unbounded_knapsack.ts
+ * Created Time: 2023-08-23
+ * Author: Gaofer Chou (gaofer-chou@qq.com)
+ */
+
+/* 完全背包：动态规划 */
+function unboundedKnapsackDP(
+    wgt: Array<number>,
+    val: Array<number>,
+    cap: number
+): number {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: n + 1 }, () =>
+        Array.from({ length: cap + 1 }, () => 0)
+    );
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(
+                    dp[i - 1][c],
+                    dp[i][c - wgt[i - 1]] + val[i - 1]
+                );
+            }
+        }
+    }
+    return dp[n][cap];
+}
+
+/* 完全背包：状态压缩后的动态规划 */
+function unboundedKnapsackDPComp(
+    wgt: Array<number>,
+    val: Array<number>,
+    cap: number
+): number {
+    const n = wgt.length;
+    // 初始化 dp 表
+    const dp = Array.from({ length: cap + 1 }, () => 0);
+    // 状态转移
+    for (let i = 1; i <= n; i++) {
+        for (let c = 1; c <= cap; c++) {
+            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] = Math.max(dp[c], dp[c - wgt[i - 1]] + val[i - 1]);
+            }
+        }
+    }
+    return dp[cap];
+}
+
+/* Driver Code */
+const wgt = [1, 2, 3];
+const val = [5, 11, 15];
+const cap = 4;
+
+// 动态规划
+let res = unboundedKnapsackDP(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+// 状态压缩后的动态规划
+res = unboundedKnapsackDPComp(wgt, val, cap);
+console.log(`不超过背包容量的最大物品价值为 ${res}`);
+
+export {};