segment_tree: TYPES_OF

Classic Segment Tree

Classic, is the way I call it. This type of segment tree, is the most simple and common type. In this kind of segment trees, for each node, we should keep some simple elements, like integers or boolians or etc.

This kind of problems don't have update queries on intervals.

Example 1 (Online):

Problem 380C - Sereja and Brackets :

For each node (for example x), we keep three integers : 1.t[x] = Answer for it's interval. 2. o[x] = The number of $($s after deleting the brackets who belong to the correct bracket sequence in this interval whit lengtht[x]. 3. c[x] = The number of $)$s after deleting the brackets who belong to the correct bracket sequence in this interval whit length t[x].

Lemma : For merging to nodes 2x and 2x + 1 (children of node 2x + 1) all we need to do is this :

tmp = min(o[2 * x], c[2 * x + 1])
t[x] = t[2 * x] + t[2 * x + 1] + tmp
o[x] = o[2 * x] + o[2 * x + 1] - tmp
c[x] = c[2 * x] + c[2 * x + 1] - tmp

So, as you know, first of all we need a build function which would be this : (as above) (C++ and [l, r) is inclusive-outclusive )

void build(int id = 1,int l = 0,int r = n){
    if(r - l < 2){
        if(s[l] == '(')
            o[id] = 1;
        else
            c[id] = 1;
        return ;
    }
    int mid = (l+r)/2;
    build(2 * id,l,mid);
    build(2 * id + 1,mid,r);
    int tmp = min(o[2 * id],c[2 * id + 1]);
    t[id] = t[2 * id] + t[2 * id + 1] + tmp;
    o[id] = o[2 * id] + o[2 * id + 1] - tmp;
    c[id] = c[2 * id] + c[2 * id + 1] - tmp;
}

For queries, return value of the function should be 3 values : t, o, c which is the values I said above for the intersection of the node's interval and the query's interval (we consider query's interval is [x, y) ), so in C++ code, return value is a pair<int,pair<int,int> > (pair<t, pair<o,c> >) :

typedef pair<int,int>pii;
typedef pair<int,pii>   node;
node segment(int x,int y,int id = 1,int l = 0,int r = n){
    if(l >= y || x >= r)   return node(0,pii(0,0));
    if(x <= l && r <= y)
        return node(t[id],pii(o[id],c[id]));
    int mid = (l+r)/2;
    node a = segment(x,y,2 * id,l,mid), b = segment(x,y,2 * id + 1,mid,r);
    int T, temp, O, C;
    temp = min(a.y.x , b.y.y);
    T = a.x + b.x + temp;
    O = a.y.x + b.y.x - temp;
        C = a.y.y + b.y.y - temp;
    return node(T,pii(O,C));    
}

Example 2 (Offline): Problem KQUERY

Imagine we have an array b1, b2, ..., bn which, $\text{[math]}$ and bi = 1 if an only if ai > k, then we can easily answer the query (i, j, k) in O(log(n)) using a simple segment tree (answer is bi + bi + 1 + ... + bj ).

We can do this ! We can answer the queries offline.

First of all, read all the queries and save them somewhere, then sort them in increasing order of k and also the arraya in increasing order (compute the permutation p1, p2, ..., pn where ap1 ≤ ap2 ≤ ... ≤ apn)

At first we'll set all array b to 1 and we will set all of them to 0 one by one.

Consider after sorting the queries in increasing order of their k, we have a permutation w1, w2, ..., wq (of1, 2, ..., q) where kw1 ≤ kw2 ≤ kw2 ≤ ... ≤ kwq (we keep the answer to the i - th query in ansi .

Pseudo code : (all 0-based)

po = 0
for j = 0 to q-1
 while po < n and a[p[po]] <= k[w[j]]
  b[p[po]] = 0, po = po + 1

So, build function would be like this (s[x] is the sum of b in the interval of node x) :

void build(int id = 1,int l = 0,int r = n){
 if(r - l < 2){
  s[id] = 1;
  return ;
 }
 int mid = (l+r)/2;
 build(2 * id, l, mid);
 build(2 * id + 1, mid, r);
 s[id] = s[2 * id] + s[2 * id + 1];
}

et An update function for when we want to st b[p[po]] = 0 to update the segment tree:

void update(int p,int id = 1,int l = 0,int r = n){
 if(r - l < 2){
  s[id] = 0;
  return ;
 }
 int mid = (l+r)/2;
 if(p < mid)
  update(p, 2 * id, l, mid);
 else
  update(p, 2 * id + 1, mid, r);
 s[id] = s[2 * id] + s[2 * id + 1];
}

Finally, a function for sum of an interval

int sum(int x,int y,int id = 1,int l = 0,int r = n){// [x, y)
 if(x >= r or l >= y) return 0;// [x, y) intersection [l,r) = empty
 if(x <= l && r <= y) // [l,r) is a subset of [x,y)
  return s[id];
 int mid = (l + r)/2;
 return sum(x, y, id * 2, l, mid) +
        sum(x, y, id*2+1, mid, r) ;
}

So, in main function instead of that pseudo code, we will use this :

build();
int po = 0;
for(int y = 0;y < q;++ y){
 int x = w[y];
 while(po < n && a[p[po]] <= k[x])
  update(p[po ++]);
 ans[x] = sum(i[x], j[x] + 1); // the interval [i[x], j[x] + 1)
}

Lazy Propagation

I told you enough about lazy propagation in the last lecture. In this lecture, I want to solve ans example .

Example : Problem POSTERS.

We don't need all elements in the interval [1, 107]. The only thing we need is the set s1, s2, ..., sk where for each i,si is at least l or r in one of the queries.

We can use interval 1, 2, ..., k instead of that (each query is running in this interval, in code, we use 0-based, I mean [0, k) ). For the i - th query, we will paint all the interval [l, r] whit color i (1-based).

For each interval, if all it's interval is from the same color, I will keep that color for it and update the nodes using lazy propagation.

So,we will have a value lazy for each node and there is no any build function (if lazy[i] ≠ 0 then all the interval of node i is from the same color (color lazy[i]) and we haven't yet shifted the updates to its children. Every member oflazy is 0 at first).

A function for shifting the updates to a node, to its children using lazy propagation :

void shift(int id){
 if(lazy[id])
  lazy[2 * is] = lazy[2 * id + 1] = lazy[id];
 lazy[id] = 0;
}

Update (paint) function (for queries) :

void upd(int x,int y,int color, int id = 0,int l = 0,int r = n){//painting the interval [x,y) whith color "color"
 if(x >= r or l >= y) return ;
 if(x <= l && r <= y){
  lazy[id] = color;
  return ;
 }
 int mid = (l+r)/2;
 shift(id);
 upd(x, y, color, 2 * id, l, mid);
 upd(x, y, color, 2*id+1, mid, r);
}

So, for each query you should call upd(x, y + 1, i) (i is the query's 1-base index) where sx = l and sy = r .

At last, for counting the number of different colors (posters), we run the code below (it's obvious that it's correct) :

set <int> se;
void cnt(int id = 1,int l = 0,int r = n){
 if(lazy[id]){
  se.insert(lazy[id]);
  return ; // there is no need to see the children, because all the interval is from the same color
 }
 if(r - l < 2) return ;
 int mid = (l+r)/2;
 cnt(2 * id, l, mid);
 cnt(2*id+1, mid, r);
}

And answer will be se.size() .

Segment tree with vectors

In this type of segment tree, for each node we have a vector (we may also have some other variables beside this) .

Example : Online approach for problem KQUERYO (I added this problem as the online version of KQUERY):

It will be nice if for each node, with interval [l, r) such that i ≤ l ≤ r ≤ j + 1 and this interval is maximal (it's parent's interval is not in the interval [i, j + 1) ), we can count the answer.

For that propose, we can keep all elements of al, al + 1, ..., ar in increasing order and use binary search for counting. So, memory will be O(n.log(n)) (each element is in O(log(n)) nodes ). We keep this sorted elements in verctor v[i] for i - th node. Also, we don't need to run sort on all node's vectors, for node i, we can merge v[2 * i]and v[2 * id + 1] (like merge sort) .

So, build function is like below :

void build(int id = 1,int l = 0,int r = n){
 if(r - l < 2){
  v[id].push_back(a[l]);
  return ;
 }
 int mid = (l+r)/2;
 build(2 * id, l, mid);
 build(2*id+1, mid, r);
 merge(v[2 * id].begin(), v[2 * id].end(), v[2 * id + 1].begin(), v[2 * id + 1].end(), back_inserter(v[id])); // read more about back_inserter in http://www.cplusplus.com/reference/iterator/back_inserter/
}

And function for solving queries :

int cnt(int x,int y,int k,int id = 1,int l = 0,int r  = n){// solve the query (x,y-1,k)
 if(x >= r or l >= y) return 0;
 if(x <= l && r <= n)
  return v[id].size() - (upper_bound(v[id].begin(), v[id].end(), k) - v[id].begin());
 int mid = (l+r)/2;
 return cnt(x, y, k, 2 * id, l, mid) +
     cnt(x, y, k, 2*id+1, mid, r) ;
}

Another example : Component Tree

Segment tree with sets

In this type of segment tree, for each node we have a set or multiset or hash_map (here) orunorderd_map or etc (we may also have some other variables beside this) .

Consider this problem :

We have n vectors, a1, a2, ..., an and all of them are initially empty. We should perform m queries on this vectors of two types :

A p k Add number kat the end of ap
C l r k print the number $\text{[math]}$ where count(ai, k) is the number of occurrences of k in ai .

For this problem, we use a segment tree where each node has a multiset, node i with interval [l, r) has amultiset s[i] that contains each number k exactly $\text{[math]}$ times (memory would be O(q.log(n)) ) .

For answer query C x y k, we will print the sum of all sx.count(k) where if the interval of node x is [l, r),x ≤ l ≤ r ≤ y + 1 and its maximal (its parent doesn't fulfill this condition) .

We have no build function (because vectors are initially empty). But we need an add function :

void add(int p,int k,int id = 1,int l = 0,int r = n){// perform query A p k
 s[id].insert(k);
 if(r - l < 2) return ;
 int mid = (l+r)/2;
 if(p < mid)
  add(p, k, id * 2, l, mid);
 else
  add(p, k, id*2+1, mid, r);
}

And the function for the second query is :

int ask(int x,int y,int k,int id = 1,int l = 0,int r = n){// Answer query C x y-1 k
 if(x >= r or l >= y) return 0;
 if(x <= l && r <= y)
  return s[id].count(k);
 int mid = (l+r)/2;
 return ask(x, y, k, 2 * id, l, mid) + 
     ask(x, y, k, 2*id+1, mid, r) ;
}

Segment tree with other data structures in each node

From now, all the other types of segments, are like the types above.

2D Segment trees

In this type of segment tree, for each node we have another segment tree (we may also have some other variables beside this) .

Segment trees with tries

In this type of segment tree, for each node we have a trie (we may also have some other variables beside this) .

Segment trees with DSU

In this type of segment tree, for each node we have a disjoint set (we may also have some other variables beside this) .

Example : Problem 76A - Gift, you can read my source code (8613428) with this type of segment trees .

Segment trees with Fenwick

In this type of segment tree, for each node we have a Fenwick (we may also have some other variables beside this) . Example :

Consider this problem :

We have n vectors, a1, a2, ..., an and all of them are initially empty. We should perform m queries on this vectors of two types :

A p k Add number kat the end of ap
C l r k print the number $\text{[math]}$ for each j ≤ k where count(ai, k) is the number of occurrences of k in ai .

For this problem, we use a segment tree where each node has a vector, node i with interval [l, r) has a setv[i] that contains each number k if and only if $\text{[math]}$ (memory would be O(q.log(n)) ) (in increasing order).

First of all, we will read all queries, store them and for each query of type A, we will insert k in v for all nodes that contain p (and after all of them, we sort these vectors using merge sort and run unique function to delete repeated elements) .

Then, for each node i, we build a vector fen[i] with size |s[i]| (initially 0).

Insert function :

void insert(int p,int k,int id = 1,int l = 0,int r = n){// perform query A p k
 if(r - l < 2){
  v[id].push_back(k);
  return ;
 }
 int mid = (l+r)/2;
 if(p < mid)
  insert(p, k, id * 2, l, mid);
 else
  insert(p, k, id*2+1, mid, r);
}

Sort function (after reading all queries) :

void SORT(int id = 1,int l = 0,int r = n){
 if(r - l < 2)
  return ;
 int mid = (l+r)/2;
 SORT(2 * id, l, mid);
 SORT(2*id+1, mid, r);
 merge(v[2 * id].begin(), v[2 * id].end(), v[2 * id + 1].begin(), v[2 * id + 1].end(), back_inserter(v[id])); // read more about back_inserter in http://www.cplusplus.com/reference/iterator/back_inserter/
 v[id].resize(unique(v[id].begin(), v[id].end()) - v[id].begin());
 fen[id] = vector<int> (v[id].size() + 1, 0);
}

Then for all queries of type A, for each node x containing p we will run :

for(int i = a + 1;i < fen[x].size(); i += i & -i)       fen[x][i] ++;

Where v[x][a] = k . Code :

void upd(int p,int k, int id = 1,int l = 0,int r = n){
 int a = lower_bound(v[id].begin(), v[id].end(), k) - v[id].begin();
 for(int i = a + 1; i < fen[id].size(); i += i & -i )
  fen[id][i] ++ ;
 if(r - l < 2) return;
 int mid = (l+r)/2;
 if(p < mid)
  upd(p, k, 2 * id, l, mid);
 else
  upd(p, k, 2*id+1, mid, r);
}

And now we can easily compute the answer for queries of type C :

int ask(int x,int y,int k,int id = 1,int l = 0,int r = n){// Answer query C x y-1 k
 if(x >= r or l >= y) return 0;
 if(x <= l && r <= y){
  int a = lower_bound(v[id].begin(), v[id].end(), k) - v[id].begin();
  int ans = 0;
  for(int i = a + 1; i > 0; i -= i & -i)
   ans += fen[id][i];
  return ans;
 }
 int mid = (l+r)/2;
 return ask(x, y, k, 2 * id, l, mid) + 
     ask(x, y, k, 2*id+1, mid, r) ;
}

Segment tree on a rooted tree

As you know, segment tree is for problems with array. So, obviously we should convert the rooted tree into an array. You know DFS algorithm and starting time (the time when we go into a vertex, starting from 1). So, if sv is starting time of v, element number sv (in the segment tree) belongs to the vertex number v and if fv = max(su) + 1 where uis in subtree of v, the interval [sv, fv) shows the interval of subtree of v (in the segment tree) .

Example : Problem 396C - On Changing Tree

Consider hv height if vertex v (distance from root).

For each query of first of type, if u is in subtree of v, its value increasing byx + (hu - hv) × - k = x + k(hv - hu) = x + k × hv - k × hu. So for each u, if s is the set of all queries of first type which u is in the subtree of their v, answer to query 2 u is $\text{[math]}$ , so we should calculate two values $\text{[math]}$ and $\text{[math]}$ , we can answer the queries. So, we for each query, we can store values in all members of its subtree ( [sv, fv) ).

So for each node of segment tree, we will have two variables $\text{[math]}$ and $\text{[math]}$ (we don't need lazy propagation, because we only update maximal nodes).

Source code of update function :

void update(int x,int k,int v,int id = 1,int l = 0,int r = n){
 if(s[v] >= r or l >= f[v]) return ;
 if(s[v] <= l && r <= f[v]){
  hkx[id] = (hkx[id] + x) % mod;
  int a = (1LL * h[v] * k) % mod;
  hkx[id] = (hkx[id] + a) % mod;
  sk[id] = (sk[id] + k) % mod;
  return ;
 }
 int mid = (l+r)/2;
 update(x, k, v, 2 * id, l, mid);
 update(x, k, v, 2*id+1, mid, r);
}

Function for 2nd type query :

int ask(int v,int id = 1,int l = 0,int r = n){
 int a = (1LL * h[v] * sk[id]) % mod;
 int ans = (hkx[id] + mod - a) % mod;
 if(r - l < 2) return ans;
 int mid = (l+r)/2;
 if(s[v] < mid)
  return (ans + ask(v, 2 * id, l, mid)) % mod;
  return (ans + ask(v, 2*id+1, mid, r)) % mod;
}

Persistent Segment Trees

In the last lecture, I talked about this type of segment trees, now I just want to solve an important example.

Example : Problem MKTHNUM

First approach : O((n + m).log2(n))

I won't discuss this approach, it's using binary search an will get TLE.

Second approach : O((n + m).log(n))

This approach is really important and pretty and too useful :

Sort elements of a to compute permutation p1, p2, ..., pn such that ap1 ≤ ap2 ≤ ... ≤ apn and q1, q2, ..., qn where, for each i, pqi = i.

We have an array b1, b2, ..., bn (initially 0) and a persistent segment tree on it.

Then n step,for each i, starting from 1, we perform bqi = 1 .

Lest sum(l, r, k) be bl + bl + 1 + ... + br after k - th update (if k = 0, it equals to 0)

As I said in the last lecture, we have an array root and the root of the empty segment tree, ir . So for each queryQ(x, y, k), we need to find the first i such that sum(1, i, r) - sum(1, i, l - 1) > k - 1 and answer will be api. (I'll explain how in the source code) :

Build function (s is the sum of the node's interval):

void build(int id = ir,int l = 0,int r = n){
 s[id] = 0;
 if(r - l < 2)
  return ;
 int mid = (l+r)/2;
 L[id] = NEXT_FREE_INDEX ++;
 R[id] = NEXT_FREE_INDEX ++;
 build(L[id], l, mid);
 build(R[id], mid, r);
 s[id] = s[L[id]] + s[R[id]];
}

Update function :

int upd(int p, int v,int id,int l = 0,int r = n){
 int ID =  NEXT_FREE_INDEX ++; // index of the node in new version of segment tree
 s[ID] = s[id] + 1;
 if(r - l < 2)
  return ID;
 int mid = (l+r)/2;
 L[ID] = L[id], R[ID] = R[id]; // in case of not updating the interval of left child or right child
 if(p < mid)
  L[ID] = upd(p, v, L[ID], l, mid);
 else
  R[ID] = upd(p, v, R[ID], mid, r);
 return ID;
}

Ask function (it returns i, so you should print api :

int ask(int id, int ID, int k, int l = 0,int r = n){// id is the index of the node after l-1-th update (or ir) and ID will be its index after r-th update
 if(r - l < 2) return l;
 int mid = (l+r)/2;
 if(s[L[ID]] - s[L[id]] >= k)// answer is in the left child's interval
  return ask(L[id], L[ID], k, l, mid);
 else
  return ask(R[id], R[ID], k - (s[L[ID]] - s[L[id]] ), mid, r);// there are already s[L[ID]] - s[L[id]] 1s in the left child's interval
}

As you can see, this problem is too tricky.

segment_tree

Thursday, 9 July 2015

TYPES_OF_SEGTREE