暴力递归:
// 方法1, 暴力递归, 时间复杂度为O(n!),空间复杂度为O(n),最多分n段,每段长度为1,递归深度为n
public static int cutRope(int target) {
if (target == 2) {
return 1;
} else if (target == 3) {
return 2;
} else {
return backTrack(target);
}
}
private static int backTrack(int n) {
// n<=4,表明不分割的时候,长度是最长的。
if (n <= 4) {
return n;
}
int res = 0;
for (int i = 1; i < n; i++) {
res += Math.max(res, i * backTrack(n - i));
}
return res;
}
记忆化递归:
// 记忆化递归,时间复杂度为O(n^2),空间复杂度为O(n)
static int[] mark = null;
public static int cutRope2(int target) {
if (target == 2) {
return 1;
} else if (target == 3) {
return 2;
}
// 创建一个数组,初始值为0
mark = new int[target+1];
return backTrack2(target);
}
private static int backTrack2(int target) {
if (target <= 4) {
return target;
}
if (mark[target] != 0) {
return mark[target];
}
int ret = 0;
for (int i = 1; i < target; i++) {
ret = Math.max(ret, i * backTrack2(target - i));
}
return mark[target] = ret;
}
动态规划:
// 动态规划, 时间复杂度为O(n^2),空间复杂度为O(n)
public static int cutRope3(int target) {
if (target == 2) {
return 1;
} else if (target == 3) {
return 2;
}
int[] mark = new int[target + 1];
for (int i = 1; i <= 4; i++) {
mark[i] = i;
}
for (int i = 5; i <= target; i++) {
for (int j = 1; j < i; j++) {
mark[i] = Math.max(mark[i], j * mark[i - j]);
}
}
return mark[target];
}
参考文章:
https://blog.csdn.net/arpospf/article/details/110492761



