[codeforces534E]Listening to Music && 可持久化线段树

本文介绍了一种使用压缩技巧的线段树实现方法,通过将节点的多个值压缩到一个unsigned long long变量中来节省内存空间。这种方法适用于空间受限的情况,并展示了如何在更新和查询操作中正确维护这些压缩的值。

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每个线段树节点需要保存四个值,ls,rs,min,tag

由于空间不够 所以把他们压缩成一个unsinged long long

t[x] = (ls * N + rs) * T + val + tag

t[x] % T 即可得到val + tag, ls = t[x] / T / N, rs = t[x] / T % N

进行标记永久化过后可以用左右儿子的值解出自己的val,再解出tag

然后就卡着内存过去了

黑科技简直可怕

#include<cstdio>
#include<algorithm>
#include<cstring>
#include<iostream>
#include<queue>
#define SF scanf
#define PF printf
#define mp make_pair
#define fir first
#define sec second
#define sqr(x) ((x)*(x))
using namespace std;
typedef long long LL;
typedef pair<int, int> pii;
typedef unsigned long long ull;
const int MAXN = 200000;
int n, m, rt[MAXN+10], Q;
pii a[MAXN+10];
struct Seg_Tree {
	static const int N = 8000000 + 10;
	static const int T = MAXN + 3;
	int ncnt;
	ull t[N];
	inline void calc_s(const bool &nl, const bool &nr, ull x, int &ls, int &rs, int &tag) {
		int s = x % T;
		ls = x / T / N; rs = x / T % N;
		tag = s - min((nl ? ls : t[ls] % T), (nr ? rs : t[rs] % T));
	}
	inline ull calc(const int &ls, const int &rs, const int &val) {
		return (1ull * ls * N + rs) * T + val;
	}
	void ins(const int &last, int &u, const int &L, const int &R, const int &l, const int &r) {
		if(L == R) {
			u = last+1;
			return ;
		}
		t[u = ++ncnt] = t[last];
		if(l <= L && R <= r) {
			t[u]++;
			return ;
		}
		int mid = (L+R) >> 1, ls, rs, val, lson = 0, rson = 0;
		calc_s((L == mid), (R == mid+1), t[u], ls, rs, val);
		if(l <= mid) ins(ls, lson, L, mid, l, r);
		if(r > mid) ins(rs, rson, mid+1, R, l, r);
		if(!lson) lson = ls;
		if(!rson) rson = rs;
		val += min(((L == mid) ? lson : t[lson] % T), ((R == mid+1) ? rson : t[rson] % T));
		t[u] = calc(lson, rson, val);
	}
	int query(const int &u, const int &L, const int &R, const int &l, const int &r) {
		if(L == R) return u;
		if(!u) return 0;
		if(l <= L && R <= r) return t[u] % T;
		int mid = (L+R) >> 1, ls, rs, val, ret = N;
		calc_s((L == mid), (mid+1 == R), t[u], ls, rs, val);
		if(l <= mid) ret = min(ret, query(ls, L, mid, l, r));
		if(r > mid) ret = min(ret, query(rs, mid+1, R, l, r));
		return ret + val;
	}
	inline void build() {
		for(int i = 1; i <= n; i++) 
			ins(rt[i-1], rt[i], 1, n-m+1, max(1, a[i].sec-m+1), a[i].sec);
	}
} seg;
int main() {
	SF("%d%d", &n, &m);
	for(int i = 1; i <= n; i++) SF("%d", &a[i].fir), a[i].sec = i;
	sort(a+1, a+1+n);
	seg.build();
	SF("%d", &Q);
	int ans = 0;
	for(int i = 1; i <= Q; i++) {
		int x, l, r;
		SF("%d%d%d", &l, &r, &x);
		x ^= ans;
		x = lower_bound(a+1, a+1+n, mp(x, 0)) - a;
		if(x == 1) ans = 0;
		else ans = seg.query(rt[x-1], 1, n-m+1, l, r);
		PF("%d\n", ans);
	}
	return 0;
}


### Codeforces 887E Problem Solution and Discussion The problem **887E - The Great Game** on Codeforces involves a strategic game between two players who take turns to perform operations under specific rules. To tackle this challenge effectively, understanding both dynamic programming (DP) techniques and bitwise manipulation is crucial. #### Dynamic Programming Approach One effective method to approach this problem utilizes DP with memoization. By defining `dp[i][j]` as the optimal result when starting from state `(i,j)` where `i` represents current position and `j` indicates some status flag related to previous moves: ```cpp #include <bits/stdc++.h> using namespace std; const int MAXN = ...; // Define based on constraints int dp[MAXN][2]; // Function to calculate minimum steps using top-down DP int minSteps(int pos, bool prevMoveType) { if (pos >= N) return 0; if (dp[pos][prevMoveType] != -1) return dp[pos][prevMoveType]; int res = INT_MAX; // Try all possible next positions and update 'res' for (...) { /* Logic here */ } dp[pos][prevMoveType] = res; return res; } ``` This code snippet outlines how one might structure a solution involving recursive calls combined with caching results through an array named `dp`. #### Bitwise Operations Insight Another critical aspect lies within efficiently handling large integers via bitwise operators instead of arithmetic ones whenever applicable. This optimization can significantly reduce computation time especially given tight limits often found in competitive coding challenges like those hosted by platforms such as Codeforces[^1]. For detailed discussions about similar problems or more insights into solving strategies specifically tailored towards contest preparation, visiting forums dedicated to algorithmic contests would be beneficial. Websites associated directly with Codeforces offer rich resources including editorials written after each round which provide comprehensive explanations alongside alternative approaches taken by successful contestants during live events. --related questions-- 1. What are common pitfalls encountered while implementing dynamic programming solutions? 2. How does bit manipulation improve performance in algorithms dealing with integer values? 3. Can you recommend any online communities focused on discussing competitive programming tactics? 4. Are there particular patterns that frequently appear across different levels of difficulty within Codeforces contests?
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