--- comments: true difficulty: 中等 edit_url: https://github.com/doocs/leetcode/edit/main/solution/0300-0399/0322.Coin%20Change/README.md tags: - 广度优先搜索 - 数组 - 动态规划 --- # [322. 零钱兑换](https://leetcode.cn/problems/coin-change) [English Version](/solution/0300-0399/0322.Coin%20Change/README_EN.md) ## 题目描述

给你一个整数数组 coins ,表示不同面额的硬币;以及一个整数 amount ,表示总金额。

计算并返回可以凑成总金额所需的 最少的硬币个数 。如果没有任何一种硬币组合能组成总金额,返回 -1

你可以认为每种硬币的数量是无限的。

 

示例 1:

输入:coins = [1, 2, 5], amount = 11
输出:3 
解释:11 = 5 + 5 + 1

示例 2:

输入:coins = [2], amount = 3
输出:-1

示例 3:

输入:coins = [1], amount = 0
输出:0

 

提示:

## 解法 ### 方法一:动态规划(完全背包) 我们定义 $f[i][j]$ 表示使用前 $i$ 种硬币,凑出金额 $j$ 的最少硬币数。初始时 $f[0][0] = 0$,其余位置的值均为正无穷。 我们可以枚举使用的最后一枚硬币的数量 $k$,那么有: $$ f[i][j] = \min(f[i - 1][j], f[i - 1][j - x] + 1, \cdots, f[i - 1][j - k \times x] + k) $$ 其中 $x$ 表示第 $i$ 种硬币的面值。 不妨令 $j = j - x$,那么有: $$ f[i][j - x] = \min(f[i - 1][j - x], f[i - 1][j - 2 \times x] + 1, \cdots, f[i - 1][j - k \times x] + k - 1) $$ 将二式代入一式,我们可以得到以下状态转移方程: $$ f[i][j] = \min(f[i - 1][j], f[i][j - x] + 1) $$ 最后答案即为 $f[m][n]$。 时间复杂度 $O(m \times n)$,空间复杂度 $O(m \times n)$。其中 $m$ 和 $n$ 分别为硬币的种类数和总金额。 #### Python3 ```python class Solution: def coinChange(self, coins: List[int], amount: int) -> int: m, n = len(coins), amount f = [[inf] * (n + 1) for _ in range(m + 1)] f[0][0] = 0 for i, x in enumerate(coins, 1): for j in range(n + 1): f[i][j] = f[i - 1][j] if j >= x: f[i][j] = min(f[i][j], f[i][j - x] + 1) return -1 if f[m][n] >= inf else f[m][n] ``` #### Java ```java class Solution { public int coinChange(int[] coins, int amount) { final int inf = 1 << 30; int m = coins.length; int n = amount; int[][] f = new int[m + 1][n + 1]; for (var g : f) { Arrays.fill(g, inf); } f[0][0] = 0; for (int i = 1; i <= m; ++i) { for (int j = 0; j <= n; ++j) { f[i][j] = f[i - 1][j]; if (j >= coins[i - 1]) { f[i][j] = Math.min(f[i][j], f[i][j - coins[i - 1]] + 1); } } } return f[m][n] >= inf ? -1 : f[m][n]; } } ``` #### C++ ```cpp class Solution { public: int coinChange(vector& coins, int amount) { int m = coins.size(), n = amount; int f[m + 1][n + 1]; memset(f, 0x3f, sizeof(f)); f[0][0] = 0; for (int i = 1; i <= m; ++i) { for (int j = 0; j <= n; ++j) { f[i][j] = f[i - 1][j]; if (j >= coins[i - 1]) { f[i][j] = min(f[i][j], f[i][j - coins[i - 1]] + 1); } } } return f[m][n] > n ? -1 : f[m][n]; } }; ``` #### Go ```go func coinChange(coins []int, amount int) int { m, n := len(coins), amount f := make([][]int, m+1) const inf = 1 << 30 for i := range f { f[i] = make([]int, n+1) for j := range f[i] { f[i][j] = inf } } f[0][0] = 0 for i := 1; i <= m; i++ { for j := 0; j <= n; j++ { f[i][j] = f[i-1][j] if j >= coins[i-1] { f[i][j] = min(f[i][j], f[i][j-coins[i-1]]+1) } } } if f[m][n] > n { return -1 } return f[m][n] } ``` #### TypeScript ```ts function coinChange(coins: number[], amount: number): number { const m = coins.length; const n = amount; const f: number[][] = Array(m + 1) .fill(0) .map(() => Array(n + 1).fill(1 << 30)); f[0][0] = 0; for (let i = 1; i <= m; ++i) { for (let j = 0; j <= n; ++j) { f[i][j] = f[i - 1][j]; if (j >= coins[i - 1]) { f[i][j] = Math.min(f[i][j], f[i][j - coins[i - 1]] + 1); } } } return f[m][n] > n ? -1 : f[m][n]; } ``` #### Rust ```rust impl Solution { pub fn coin_change(coins: Vec, amount: i32) -> i32 { let n = amount as usize; let mut f = vec![n + 1; n + 1]; f[0] = 0; for &x in &coins { for j in x as usize..=n { f[j] = f[j].min(f[j - (x as usize)] + 1); } } if f[n] > n { -1 } else { f[n] as i32 } } } ``` #### JavaScript ```js /** * @param {number[]} coins * @param {number} amount * @return {number} */ var coinChange = function (coins, amount) { const m = coins.length; const n = amount; const f = Array(m + 1) .fill(0) .map(() => Array(n + 1).fill(1 << 30)); f[0][0] = 0; for (let i = 1; i <= m; ++i) { for (let j = 0; j <= n; ++j) { f[i][j] = f[i - 1][j]; if (j >= coins[i - 1]) { f[i][j] = Math.min(f[i][j], f[i][j - coins[i - 1]] + 1); } } } return f[m][n] > n ? -1 : f[m][n]; }; ``` 我们注意到 $f[i][j]$ 只与 $f[i - 1][j]$ 和 $f[i][j - x]$ 有关,因此我们可以将二维数组优化为一维数组,空间复杂度降为 $O(n)$。 相似题目: - [279. 完全平方数](https://github.com/doocs/leetcode/blob/main/solution/0200-0299/0279.Perfect%20Squares/README.md) #### Python3 ```python class Solution: def coinChange(self, coins: List[int], amount: int) -> int: n = amount f = [0] + [inf] * n for x in coins: for j in range(x, n + 1): f[j] = min(f[j], f[j - x] + 1) return -1 if f[n] >= inf else f[n] ``` #### Java ```java class Solution { public int coinChange(int[] coins, int amount) { final int inf = 1 << 30; int n = amount; int[] f = new int[n + 1]; Arrays.fill(f, inf); f[0] = 0; for (int x : coins) { for (int j = x; j <= n; ++j) { f[j] = Math.min(f[j], f[j - x] + 1); } } return f[n] >= inf ? -1 : f[n]; } } ``` #### C++ ```cpp class Solution { public: int coinChange(vector& coins, int amount) { int n = amount; int f[n + 1]; memset(f, 0x3f, sizeof(f)); f[0] = 0; for (int x : coins) { for (int j = x; j <= n; ++j) { f[j] = min(f[j], f[j - x] + 1); } } return f[n] > n ? -1 : f[n]; } }; ``` #### Go ```go func coinChange(coins []int, amount int) int { n := amount f := make([]int, n+1) for i := range f { f[i] = 1 << 30 } f[0] = 0 for _, x := range coins { for j := x; j <= n; j++ { f[j] = min(f[j], f[j-x]+1) } } if f[n] > n { return -1 } return f[n] } ``` #### TypeScript ```ts function coinChange(coins: number[], amount: number): number { const n = amount; const f: number[] = Array(n + 1).fill(1 << 30); f[0] = 0; for (const x of coins) { for (let j = x; j <= n; ++j) { f[j] = Math.min(f[j], f[j - x] + 1); } } return f[n] > n ? -1 : f[n]; } ``` #### JavaScript ```js /** * @param {number[]} coins * @param {number} amount * @return {number} */ var coinChange = function (coins, amount) { const n = amount; const f = Array(n + 1).fill(1 << 30); f[0] = 0; for (const x of coins) { for (let j = x; j <= n; ++j) { f[j] = Math.min(f[j], f[j - x] + 1); } } return f[n] > n ? -1 : f[n]; }; ```