目录
快速排序算法模板
void quick_sort(int q[], int l, int r){
if (l >= r) return;
int i = l - 1, j = r + 1, x = q[l + r >> 1];
while (i < j){
do i ++ ; while (q[i] < x);
do j -- ; while (q[j] > x);
if (i < j) swap(q[i], q[j]);
}
quick_sort(q, l, j), quick_sort(q, j + 1, r);
}
归并排序算法模板
void merge_sort(int q[], int l, int r){
if (l >= r) return;
int mid = l + r >> 1;
merge_sort(q, l, mid);
merge_sort(q, mid + 1, r);
int k = 0, i = l, j = mid + 1;
while (i <= mid && j <= r)
if (q[i] < q[j]) tmp[k ++ ] = q[i ++ ];
else tmp[k ++ ] = q[j ++ ];
while (i <= mid) tmp[k ++ ] = q[i ++ ];
while (j <= r) tmp[k ++ ] = q[j ++ ];
for (i = l, j = 0; i <= r; i ++, j ++ ) q[i] = tmp[j];
}
整数二分算法模板
bool check(int x) {/* ... */} // 检查x是否满足某种性质
// 区间[l, r]被划分成[l, mid]和[mid + 1, r]时使用:
int bsearch_1(int l, int r){
while (l < r){
int mid = l + r >> 1;
if (check(mid)) r = mid; // check()判断mid是否满足性质
else l = mid + 1;
}
return l;
}
// 区间[l, r]被划分成[l, mid - 1]和[mid, r]时使用:
int bsearch_2(int l, int r){
while (l < r){
int mid = l + r + 1 >> 1;
if (check(mid)) l = mid;
else r = mid - 1;
}
return l;
}
浮点数二分算法模板
bool check(double x) {/* ... */} // 检查x是否满足某种性质
double bsearch_3(double l, double r){
const double eps = 1e-6; // eps 表示精度,取决于题目对精度的要求
while (r - l > eps){
double mid = (l + r) / 2;
if (check(mid)) r = mid;
else l = mid;
}
return l;
}
高精度加法
// C = A + B, A >= 0, B >= 0
vector<int> add(vector<int> &A, vector<int> &B){
if (A.size() < B.size()) return add(B, A);
vector<int> C;
int t = 0;
for (int i = 0; i < A.size(); i ++ ){
t += A[i];
if (i < B.size()) t += B[i];
C.push_back(t % 10);
t /= 10;
}
if (t) C.push_back(t);
return C;
}
高精度减法
// C = A - B, 满足A >= B, A >= 0, B >= 0
vector<int> sub(vector<int> &A, vector<int> &B){
vector<int> C;
for (int i = 0, t = 0; i < A.size(); i ++ ){
t = A[i] - t;
if (i < B.size()) t -= B[i];
C.push_back((t + 10) % 10);
if (t < 0) t = 1;
else t = 0;
}
while (C.size() > 1 && C.back() == 0) C.pop_back();
return C;
}
高精度乘低精度
// C = A * b, A >= 0, b > 0
vector<int> mul(vector<int> &A, int b){
vector<int> C;
int t = 0;
for (int i = 0; i < A.size() || t; i ++ ){
if (i < A.size()) t += A[i] * b;
C.push_back(t % 10);
t /= 10;
}
return C;
}
高精度除以低精度
// A / b = C ... r, A >= 0, b > 0
vector<int> div(vector<int> &A, int b, int &r){
vector<int> C;
r = 0;
for (int i = A.size() - 1; i >= 0; i -- ){
r = r * 10 + A[i];
C.push_back(r / b);
r %= b;
}
reverse(C.begin(), C.end());
while (C.size() > 1 && C.back() == 0) C.pop_back();
return C;
}
一维前缀和
S[i] = a[1] + a[2] + ... a[i]
a[l] + ... + a[r] = S[r] - S[l - 1]
二维前缀和
S[i, j] = 第i行j列格子左上部分所有元素的和
以(x1, y1)为左上角,(x2, y2)为右下角的子矩阵的和为:
S[x2, y2] - S[x1 - 1, y2] - S[x2, y1 - 1] + S[x1 - 1, y1 - 1]
一维差分
给区间[l, r]中的每个数加上c:B[l] += c, B[r + 1] -= c
二维差分
给以(x1, y1)为左上角,(x2, y2)为右下角的子矩阵中的所有元素加上c:
S[x1, y1] += c, S[x2 + 1, y1] -= c, S[x1, y2 + 1] -= c, S[x2 + 1, y2 + 1] += c
位运算
求n的第k位数字: n >> k & 1
返回n的最后一位1:lowbit(n) = n & -n
双指针算法
for (int i = 0, j = 0; i < n; i ++ ){
while (j < i && check(i, j)) j ++ ;
// 具体问题的逻辑
}
常见问题分类:
(1) 对于一个序列,用两个指针维护一段区间
(2) 对于两个序列,维护某种次序,比如归并排序中合并两个有序序列的操作
离散化
vector<int> alls; // 存储所有待离散化的值
sort(alls.begin(), alls.end()); // 将所有值排序
alls.erase(unique(alls.begin(), alls.end()), alls.end()); // 去掉重复元素
// 二分求出x对应的离散化的值
int find(int x){ // 找到第一个大于等于x的位置
int l = 0, r = alls.size() - 1;
while (l < r){
int mid = l + r >> 1;
if (alls[mid] >= x) r = mid;
else l = mid + 1;
}
return r + 1; // 映射到1, 2, ...n
}
区间合并
// 将所有存在交集的区间合并
void merge(vector<PII> &segs){
vector<PII> res;
sort(segs.begin(), segs.end());
int st = -2e9, ed = -2e9;
for (auto seg : segs)
if (ed < seg.first){
if (st != -2e9) res.push_back({st, ed});
st = seg.first, ed = seg.second;
}
else ed = max(ed, seg.second);
if (st != -2e9) res.push_back({st, ed});
segs = res;
}