Debugging

Print vectors and matrices

template <class T> ostream &operator<<(ostream &out, vector<T> &v) {
    for (auto &element : v) {
        out << element << '\t';
    }

    return out;
}

template <class T> ostream &operator<<(ostream &out, vector<vector<T>> &matrix) {
    for (auto &row : matrix) {
        out << row << endl;
    }

    return out;
}

IO

cin.tie(nullptr);
ios_base::sync_with_stdio(false);

Read numbers faster

#include <cstdio>

template <class T> inline T read_number() {
    char c = 0;
    T x = 0;

    // Ignore chars that are not numbers
    while (c < '0' || c > '9') {
        c = getchar_unlocked();
    }

    // Read number
    do {
        x = x * 10 + (c - '0');
        c = getchar_unlocked();
    } while (c >= '0' && c <= '9');

    return x;
}

Graph stuff

Dijkstra

Gets all distances from a given node in $O(n + m\log(m))$ .

Dijkstra implementation

// Input:
//  - adj: adj[src] = (dst, length), where everything is 0-indexed
//  - start
// Output:
//  - d: d[dst] = min distance from start to dst
vector<long long> dijkstra(const vector<vector<pair<int, long long>>> &adj, int start) {
    int n = adj.size();

    vector<long long> d(n, numeric_limits<long long>::max());
    d[start] = 0;

    vector<bool> is_closed(n, false);

    priority_queue<pair<long long, int>> q;
    q.push({0, start});

    while (!q.empty()) {
        auto [_, src] = q.top();
        q.pop();

        if (is_closed[src]) {
            continue;
        }
        is_closed[src] = true;

        for (auto [dst, length] : adj[src]) {
            if (d[src] + length < d[dst]) {
                d[dst] = d[src] + length;
                q.push({-d[dst], dst});
            }
        }
    }

    return d;
}

Floyd-Warshall

Gets all pair-wise distances in $O(n^3)$ .

Floyd-Warshall implementation

constexpr long long inf = 1e18;

// Input:
//  - adj: adj[src] = (dst, length), where everything is 0-indexed
// Output:
//  - n x n matrix with all pairwise distances
vector<vector<long long>> floyd_warshall(const vector<vector<pair<int, long long>>> &adj) {
    int n = adj.size();
    vector<vector<long long>> d(n, vector<long long>(n, inf));
    for (int i = 0; i < n; ++i) {
        d[i][i] = 0;
    }
    for (int src = 0; src < n; ++src) {
        for (auto [dst, length] : adj[src]) {
            d[src][dst] = min(d[src][dst], length);
        }
    }

    for (int k = 0; k < n; ++k) {
        for (int src = 0; src < n; ++src) {
            for (int dst = 0; dst < n; ++dst) {
                d[src][dst] = min(d[src][dst], d[src][k] + d[k][dst]);
            }
        }
    }

    return d;
}

Max-Flow / Min-Cut

The Ford-Fulkerson algorithm works as follows. At first, we will add reverse edges with zero capacity. This ensures that we can “backtrack” if we made a mistake choosing a earlier path. Then, we will repeat these two steps until we can no longer do Step 1.

Find a path from the source to the sink. We will be able to push as much “water” as the smallest capacity in the path.
Subtract the new flow from the original path, and add it to the reverse path.

How we find a path in Step 1 is up to us. If we do BFS, the algorithm runs in $O(nm^2)$ and is called Edmonds-Karp.

Edmonds-Karp implementation

constexpr long long inf = numeric_limits<long long>::max();

// adjacency list of the undirected graph
vector<vector<int>> adj;
// matrix for every pair of nodes
// initially, capacities[src][dst] = weight
vector<vector<long long>> capacities;
int n;

long long bfs(int source, int sink, vector<int> &parents) {
    parents.assign(n, -1);
    parents[source] = -2;
    // (node, flow until that point)
    queue<pair<int, long long>> q;
    q.push({source, inf});

    while (!q.empty()) {
        auto [current_node, current_flow] = q.front();
        q.pop();

        for (int next_node : adj[current_node]) {
            bool is_not_visited = parents[next_node] == -1;
            bool is_reachable = capacities[current_node][next_node] > 0;
            if (is_not_visited && is_reachable) {
                parents[next_node] = current_node;
                long long new_flow = min(current_flow, capacities[current_node][next_node]);
                if (next_node == sink) {
                    return new_flow;
                }
                q.push({next_node, new_flow});
            }
        }
    }

    return 0;
}

long long max_flow(int source, int sink) {
    // Ford-Fulkerson algorithm with BFS (Edmonds-Karp)
    long long flow = 0;
    vector<int> parents(n);
    long long new_flow = 0;

    while ((new_flow = bfs(source, sink, parents)) > 0) {
        flow += new_flow;
        int current_node = sink;
        while (current_node != source) {
            int previous_node = parents[current_node];
            capacities[previous_node][current_node] -= new_flow;
            capacities[current_node][previous_node] += new_flow;
            current_node = previous_node;
        }
    }

    return flow;
}

Kosaraju’s algorithm

Finds the strongly connected components (SCC) of a directed graph in $O(n + m)$ . It runs DFS twice, the first one on the original graph, and the second one on the transpose graph (the one in which all edges are reversed). Some observations:

We will be able to traverse all nodes in a SCC in both the original and the transpose graph. In the first DFS pass, we might jump from one SCC to another. In the second one, this is not possible, as we will see shortly.
Let’s imagine $u_1$ lives in $SCC_1$ and that $u_2$ lives in $SCC_2$ and that $SCC_1 \leadsto SCC_2$ . Then $u_1$ will be “closed” after $u_2$ . This is easy to see applying the “Parenthesis theorem” (the exploring intervals are either nested or disjoint).
Thus, in the second DFS, we want to start exploring nodes in $SCC_1$ first (as we will not be able to jump to $SCC_2$ in the transpose graph).

Kosaraju's algorithm implementation

void dfs(int node, const vector<vector<int>> &adj, vector<bool> &visited, vector<int> &output) {
    visited[node] = true;

    for (int neighbor : adj[node]) {
        if (visited[neighbor]) {
            continue;
        }

        dfs(neighbor, adj, visited, output);
    }

    output.push_back(node);
}

vector<int> get_scc(const vector<vector<int>> &adj) {
    // Kosaraju's algorithm
    int n = adj.size();

    // First DFS pass
    vector<bool> visited(n, false);
    vector<int> nodes_by_exit_time;
    for (int node = 0; node < n; ++node) {
        if (visited[node]) {
            continue;
        }

        dfs(node, adj, visited, nodes_by_exit_time);
    }
    reverse(nodes_by_exit_time.begin(), nodes_by_exit_time.end());

    // Construct transpose graph
    vector<vector<int>> rev_adj(n);
    for (int src = 0; src < n; ++src) {
        for (int dst : adj[src]) {
            rev_adj[dst].push_back(src);
        }
    }

    // Second DFS pass on the transpose graph
    visited.assign(n, false);
    vector<int> scc_by_node(n, -1);
    int scc_id = 0;
    for (int node : nodes_by_exit_time) {
        if (visited[node]) {
            continue;
        }

        vector<int> nodes_in_scc;
        dfs(node, rev_adj, visited, nodes_in_scc);
        for (int node_in_scc : nodes_in_scc) {
            scc_by_node[node_in_scc] = scc_id;
        }
        scc_id++;
    }

    return scc_by_node;
}

Kruskal

It finds a minimum spanning tree by adding edges in order as long as they don’t create cycles. A fast implementation will use a data structure that allows merging sets quickly (Disjoint Set Union / Union-Find / Merge-Find).

Kruskal implementation

struct Edge {
    int src;
    int dst;
    int weight;

    bool operator<(const Edge &other) const { return weight < other.weight; }
};

struct DSU {
    vector<int> parents;

    DSU(int n) {
        parents.assign(n, 0);
        for (int i = 0; i < n; ++i) {
            parents[i] = i;
        }
    }

    int find(int v) {
        if (v == parents[v]) {
            return v;
        }

        int representative = find(parents[v]);
        // path-compression optimization
        parents[v] = representative;
        return representative;
    }

    void merge(int u, int v) {
        u = find(u);
        v = find(v);

        if (u != v) {
            parents[v] = u;
        }
    }
};

// Input:
//   - n: the number of nodes in the graph
//   - edges: a list of (src, dst, weight)
// Output:
//   - cost of the MST or -1 if the graph is not connected
long long kruskal(int n, vector<Edge> &edges) {
    sort(edges.begin(), edges.end());

    DSU dsu = DSU(n);
    long long cost = 0;
    for (auto [src, dst, weight] : edges) {
        int u = dsu.find(src);
        int v = dsu.find(dst);

        if (u != v) {
            cost += weight;
            dsu.merge(u, v);
        }
    }

    // check if all nodes are connected
    int representative = dsu.find(0);
    for (int i = 1; i < n; ++i) {
        if (dsu.find(i) != representative) {
            return -1;
        }
    }

    return cost;
}

Strings

O(1) hash queries

By preprocessing a string s in $O(n)$ we can compute the hash of s[a...b] for any a and b in $O(1)$ .

Show StringHasher implementation

struct StringHasher {
    const long long A = 911382323;
    const long long B = 972663749;

    vector<long long> prefix_hash;
    vector<long long> coefficients;
    int n;

    StringHasher(string s) {
        n = s.size();

        prefix_hash.assign(n, 0);
        prefix_hash[0] = s[0];
        for (int i = 1; i < n; ++i) {
            prefix_hash[i] = (A * prefix_hash[i - 1] + s[i]) % B;
        }

        coefficients.assign(n, 0);
        coefficients[0] = 1;
        for (int i = 1; i < n; ++i) {
            coefficients[i] = (A * coefficients[i - 1]) % B;
        }
    }

    // Computes hash(s[a...b]) (both endpoints included)
    long long hash(int a, int b) {
        if (a == 0) {
            return prefix_hash[b];
        }

        long long result = (prefix_hash[b] - prefix_hash[a - 1] * coefficients[b - a + 1]) % B;
        if (result < 0) {
            result += B;
        }

        return result;
    }
};

Range queries

Name	Operations	Initialization	Update	Access
Prefix sum	Sum	$O(n)$	-	$O(1)$
Sparse table	Max/Min	$O(n\log{n})$	-	$O(1)$
Fenwick (binary indexed) tree	Sum	$O(n)$	$O(\log{n})$	$O(\log{n})$
Segment tree	Sum/Max/Min	$O(n\log{n})$	$O(\log{n})$	$O(\log{n})$

Fenwick tree implementation

template <class T> struct FenwickTree {
    vector<T> tree;
    int n;

    // v is assumed to be 1-indexed: [0, element_1, element_2, ....]
    FenwickTree(vector<T> &v) {
        n = v.size() - 1;

        // This is faster than calling .update() n times
        // O(nlogn) -> O(n)
        tree.assign(v.begin(), v.end());
        for (int i = 1; i <= n; ++i) {
            int p = i + (i & -i);
            if (p <= n) {
                tree[p] += tree[i];
            }
        }
    }

    // Sums from 1 to i (both inclusive and 1-indexed)
    T query(int i) {
        T total = 0;

        while (i > 0) {
            total += tree[i];
            i -= i & -i;
        }

        return total;
    }

    // Updates the tree by adding difference to the i-th element
    // (1-indexed)
    void update(int i, T difference) {
        while (i <= n) {
            tree[i] += difference;
            i += i & -i;
        }
    }
};

Segment tree implementation

template <class T> struct SegmentTree {
    // min, max, sum...
    function<T(T, T)> op;
    // min -> INT_MAX, max -> INT_MIN, sum -> 0...
    T identity;

    vector<T> tree;
    int n;

    // v is assumed to be 0-indexed: [element_0, element_1, ...]
    SegmentTree(vector<T> &v, function<T(T, T)> op_, T identity_) : op(op_), identity(identity_) {
        n = v.size();

        // The first element of tree will never be accessed
        tree.assign(2 * n, 0);
        for (int i = 0; i < n; ++i) {
            update(i, v[i]);
        }
    }

    // Both start and end are inclusive and 0-indexed
    T query(int start, int end) {
        start += n;
        end += n;

        T total = identity;
        while (start <= end) {
            if (start % 2 == 1) {
                total = op(total, tree[start++]);
            }
            if (end % 2 == 0) {
                total = op(total, tree[end--]);
            }

            start /= 2;
            end /= 2;
        }

        return total;
    }

    // i is 0-indexed
    void update(int i, T value) {
        int parent = i + n;
        tree[parent] = value;

        while (parent > 1) {
            parent = parent / 2;
            int child_1 = parent * 2;
            int child_2 = parent * 2 + 1;

            tree[parent] = op(tree[child_1], tree[child_2]);
        }
    }
};

g++ ordered_set

If we use a set and we want to get the index of an element, we can use distance(s.begin(), s.find(e)), but this is $O(n)$ . We can use the g++ implementation, that includes two handy functions that are $O(\log n)$ :

order_of_key(k): number of items strictly smaller than k
find_by_order(k): k-th element in the sorted collection (0-indexed)

Show g++ tree

#include <ext/pb_ds/assoc_container.hpp>
#include <ext/pb_ds/tree_policy.hpp>

using namespace __gnu_pbds;

// key type -> int
// value type -> null_type (so we get a set)
#define ordered_set tree<int, null_type, less<int>, rb_tree_tag, tree_order_statistics_node_update>