Yet Another Jellyfish and Mex

You are given an array of $$$n$$$ nonnegative integers $$$a_1, a_2, \dots, a_n$$$, satisfying $$$\max(a) < m$$$.

Let $$$v$$$ be a variable that is initialized to $$$0$$$, Jellyfish will perform the following operation until $$$a$$$ is empty:

• Select a number $$$z$$$ satisfying $$$1 \leq z \leq k$$$ and select $$$z$$$ indices $$$i_1, i_2, \dots, i_z$$$ ($$$1 \leq i_1 < i_2 < ... < i_z \leq |a|$$$), then delete $$$a_{i_1}, a_{i_2}, \dots, a_{i_z}$$$ from $$$a$$$.
• add $$$\operatorname{MEX}(a)^{\dagger}$$$ to $$$v$$$.

Now Jellyfish wants to know the minimum possible final value of $$$v$$$ if he performs all the operations optimally.

$$$^{\dagger}$$$ The MEX (minimum excluded) of an array is the smallest non-negative integer that does not belong to the array. For instance:

• The MEX of $$$[2,2,1]$$$ is $$$0$$$, because $$$0$$$ does not belong to the array.
• The MEX of $$$[3,1,0,1]$$$ is $$$2$$$, because $$$0$$$ and $$$1$$$ belong to the array, but $$$2$$$ does not.
• The MEX of $$$[0,3,1,2]$$$ is $$$4$$$ because $$$0$$$, $$$1$$$, $$$2$$$, and $$$3$$$ belong to the array, but $$$4$$$ does not.

Input

The first line of each test contains three single integers $$$n\ (m \leq n \leq 10^8), m(1 \leq m \leq 2 \times 10^5), k(1 \leq k \leq 500)$$$.

Due to excessive input volume, we do not directly input $$$a$$$, but for each $$$x (0 \leq x < m)$$$, input the number of occurrences of $$$x$$$ in $$$a$$$.

The next line contains $$$m$$$ numbers $$$c_1, c_2, \dots, c_{m-1}\ (0 \leq c_i \leq n, \sum_{i=1}^n{c_i}=n)$$$. — the number of occurrences of $$$x$$$ in $$$a$$$.

It can prove that the order of the values is not important in $$$a$$$.

Output

Output a single integer. — the minimum possible final value of $$$v$$$.

Similar to the tutorial of 1875D - Медуза и Mex, We only care about the operation before $$$\text{MEX}(a)$$$ reaches $$$0$$$.

By using greedy we know that each time we should try to delete as many numbers as possible, which means every time we will delete $$$\min(|a|, k)$$$ values of $$$a$$$.

Lemma1. If $$$i < j$$$ and $$$c_i \leq c_j$$$, we won't delete $$$j$$$ from $$$a$$$.

Proof. Similar to the tutorial of 1875D - Медуза и Mex, if we have deleted $$$j$$$, we will delete all of $$$j$$$ in $$$a$$$, then make $$$\text{MEX}(a)$$$ to $$$j$$$, but because $$$c_i \leq c_j$$$, we can delete all of $$$i$$$ instead of $$$j$$$ in the same number of operations, but the $$$\text{MEX}(a)$$$ will become $$$i$$$, which is better than $$$j$$$.

Because $$$1 + 2 + \dots + \sqrt{2n} > n$$$, and according to Lemma1, there will be only $$$\min(\sqrt{2n}, m)$$$ possible values we will delete.

Lemma2. Every operation, there are at most two values of numbers we will delete. And if we delete two values of numbers, let's call them $$$x$$$ and $$$y$$$ $$$(x < y)$$$. We will delete all of $$$y$$$, and we can't delete all of $$$x$$$ in the operation.

Proof. For each operation, if we delete all the numbers of value $$$x$$$, we will have no need to delete $$$y$$$ satisfying $$$x < y$$$, because we have removed all of $$$x$$$, so $$$y$$$ will no longer have an effect on $$$\text{MEX}(a)$$$. And also, let's consider the next value of $$$\text{MEX}(a)$$$ after this operation, let's call it $$$x'$$$, except the value of the $$$\text{MEX}(a)$$$ after this operation, We won't delete other values except $$$x'$$$, because the deletion of these numbers can be delay to the operations after $$$\text{MEX}(a)$$$ reaches $$$x'$$$.

Now we can use dynamic programming to solve the problem. Let's define $$$dp(i, x)$$$ means the $$$\text{MEX}(a)$$$ after next operation will be $$$i$$$, and the number of $$$i$$$ in $$$a$$$ is $$$x$$$. We can't enumerate directly for transition, it's $$$O(\min(n, m^2) \times k)$$$, so we can use Convex Hull Optimisation to solve this problem. for each $$$x$$$, we build a convex of $$$(i, dp(i, x))$$$, Since both $$$c_i$$$ and $$$i$$$ are monotone while transiting, so we can use two pointers instead of binary searching.

Time complexity: $$$O(\min(\sqrt n, m) \times k)$$$

Memory complexity: $$$O(\min(\sqrt n, m) \times k)$$$

We can use one tool each time the timer reaches to $$$1$$$, then the answer will be $$$\sum_{i=1}^n \min(a - 1, x_i) + b$$$. This can prove to be optimal. Because for each tool, if we use it when the timer is $$$c$$$, its contribution to the answer is $$$\min(x_i, a - c)$$$. We can't use the tool when the timer is less than or equal to $$$0$$$ because the bomb will explode before that, so $$$c=1$$$ is the optimal.

Time complexity: $$$O(n)$$$ per test case.

Memory complexity: $$$O(1)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;

int n = 0, a = 0, b = 0;
long long ans = 0;

inline void solve(){
	scanf("%d %d %d", &a, &b, &n);
	ans = b;
	for(int i = 0, x = 0 ; i < n ; i ++){
		scanf("%d", &x);
		ans += min(a - 1, x);
	}
	printf("%lld\n", ans);
}

int T = 0;

int main(){
	scanf("%d", &T);
	for(int i = 0 ; i < T ; i ++) solve();
	return 0;
}

Let us define $$$\min(a)$$$ to be the minimum value of $$$a$$$ in the current round, $$$\max(a)$$$ to be the maximum value of $$$a$$$ in the current round, $$$\min(b)$$$ to be the minimum value of $$$b$$$ in the current round, $$$\max(b)$$$ to be the maximum value of $$$b$$$ in the current round, $$$\text{MIN}$$$ to be the minimum value of all the apples, $$$\text{MAX}$$$ to be the maximum value of all the apples.

By greedy and induction, we would come to the following conclusion:

• If Jellyfish is the one operating this round: If $$$\min(a) < \max(b)$$$, she will swap this two apples, otherwise she will do nothing.
• If Gellyfish is the one operating this round: If $$$\max(a) > \min(b)$$$, he will swap this two apples, otherwise she will do nothing.

We consider who $$$\text{MAX}$$$ and $$$\text{MIN}$$$ will belong to after the first round.

In the first round:

• If $$$\max(a) < \max(b)$$$, $$$\text{MAX} = \max(b)$$$. And because $$$\min(a) < \max(b)$$$, Jellyfish will swap this two apples. So $$$\text{MAX}$$$ belongs to Jellyfish.
• If $$$\max(a) > \max(b)$$$, $$$\text{MAX} = \max(a)$$$. If $$$\min(a)=\max(a)$$$, then $$$\min(a) > \max(b)$$$, Jellyfish will do nothing. Otherwise Jellyfish won't swap the apple with value $$$\text{MAX}$$$. In conclusion $$$\text{MAX}$$$ belongs to Jellyfish

We can also show that $$$\text{MIN}$$$ belongs to Gellyfish, and the proof is symmetric with the above.

So in the second round, $$$\min(b) = \text{MIN}, \max(a) = \text{MAX}$$$, We have $$$\text{MIN}<\text{MAX}$$$. So Gellyfish will swap this two apples, in the third round, $$$\min(a)=\text{MIN}, \max(b) = \text{MAX}$$$, Jellyfish will swap this two apples.

So after the first round, the game will go two rounds at a time, with two people swapping two apples with the minimum value and the maximum value back and forth.

So we only need to know the answer for $$$k = 1$$$ and $$$k = 2$$$.

Time complexity: $$$O(n + m)$$$ per test case.

Memory complexity: $$$O(n + m)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;

const int N = 1000 + 5;
int n = 0, m = 0, k = 0, x = 0, y = 0, a[N] = {}, b[N] = {};

inline void solve(){
	scanf("%d %d %d", &n, &m, &k); k --;
	for(int i = 0 ; i < n ; i ++) scanf("%d", &a[i]);
	for(int i = 0 ; i < m ; i ++) scanf("%d", &b[i]);
	x = y = 0;
	for(int i = 1 ; i < n ; i ++) if(a[i] < a[x]) x = i;
	for(int i = 1 ; i < m ; i ++) if(b[i] > b[y]) y = i;
	if(b[y] > a[x]) swap(a[x], b[y]);
	if(k & 1){
		x = 0, y = 0;
		for(int i = 1 ; i < n ; i ++) if(a[i] > a[x]) x = i;
		for(int i = 1 ; i < m ; i ++) if(b[i] < b[y]) y = i;
		swap(a[x], b[y]);
	}
	long long ans = 0;
	for(int i = 0 ; i < n ; i ++) ans += a[i];
	printf("%lld\n", ans);
	
}

int T = 0;

int main(){
	scanf("%d", &T);
	for(int i = 0 ; i < T ; i ++) solve();
	return 0;
}

Firstly, if $$$n \geq m$$$, we can make the problem transform $$$n < m$$$ by giving each person an apple at a time until there are not enough apples.

We can calculate the mass of apples that each person ends up with as $$$\frac n m$$$.

Since it's cut in half every time, if $$$\frac m {\gcd(n, m)}$$$ is not an integral power of $$$2$$$, there's no solution.

Since the number of apple pieces is added to exactly $$$1$$$ for each cut, So we just need to minimize the final number of apple pieces.

As the problem is transformed, $$$\frac n m$$$ is less than $$$1$$$, and $$$\frac m {\gcd(n, m)}$$$ is an integral power of $$$2$$$. So we can uniquely find a set of positive integers $$$S$$$ satisfying $$$\frac n m = \sum_{i \in S} \frac 1 {2^i}$$$. And this method can be proven to be optimal, if we find another multiset $$$T$$$ satisfying $$$S \neq T$$$, for every element $$$x$$$ that appears twice or more, We can make the answer better by removing two $$$x$$$ at a time from $$$T$$$ and adding one $$$x - 1$$$ to $$$T$$$. By repeating this process, eventually $$$T$$$ will become $$$S$$$.

We can use $$$\text{std::__builtin_popcount()}$$$ to get $$$|S|$$$, the answer is $$$m \times |S|-n$$$.

Time complexity: $$$O(\log m)$$$ per test case.

Memory complexity: $$$O(1)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;

int n = 0, m = 0;

inline void solve(){
	scanf("%d %d", &n, &m); n %= m;
	int a = n / __gcd(n, m), b = m / __gcd(n, m);
	if(__builtin_popcount(b) > 1) printf("-1\n");
	else printf("%lld\n", 1ll * __builtin_popcount(a) * m - n);
}

int T = 0;

int main(){
	scanf("%d", &T);
	for(int i = 0 ; i < T ; i ++) solve();
	return 0;
}

We only care about the operation before $$$\text{MEX}(a)$$$ reaches $$$0$$$, because after that, $$$m$$$ will never change.

Lemma. Before $$$\text{MEX}(a)$$$ reaches $$$0$$$, we will choose a positive integer $$$x$$$ at a time that satisfies $$$x < \text{MEX}(a)$$$, and delete all $$$x$$$ from $$$a$$$, the $$$\text{MEX}(a)$$$ will become $$$x$$$.

Proof. Because if $$$x > \text{MEX}(a)$$$, we can place this operation after the $$$\text{MEX}(a)$$$ becomes $$$0$$$, if we don't delete all of $$$x$$$, $$$\text{MEX}(a)$$$ won't change, we can also put this operation later.

So before $$$\text{MEX}(a)$$$ reaches $$$0$$$, the $$$x$$$ we delete is non-increasing.

It means we can solve this problem by dynamic programming. Let $$$dp_i$$$ represents when $$$\text{MEX}(a)=i$$$, and we haven't delete any $$$x$$$ satisfying $$$x < i$$$,the minimum value of $$$m$$$.

Let $$$c_i$$$ represents the number of times $$$i$$$ appears in $$$a$$$, the transition is: $$$\forall j < i, dp_j \leftarrow dp_i + i \times (c_j - 1) + j$$$.

Time complexity: $$$O(n^2)$$$ per test case.

Memory complexity: $$$O(n)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;
typedef long long ll;

const ll N = 5000 + 5, Inf = 0x3f3f3f3f3f3f3f3f;
ll n = 0, m = 0, a[N] = {}, dp[N] = {};

inline void init(){
	for(ll i = 0 ; i <= n ; i ++) a[i] = 0, dp[i] = Inf;
	n = m = 0;
}

inline void solve(){
	scanf("%lld", &n);
	for(ll i = 1, x = 0 ; i <= n ; i ++){
		scanf("%lld", &x);
		if(x < n) a[x] ++;
	}
	while(a[m]) m ++;
	dp[m] = 0;
	for(ll i = m ; i >= 1 ; i --) for(ll j = 0 ; j < i ; j ++) dp[j] = min(dp[j], dp[i] + i * a[j]);
	printf("%lld\n", dp[0] - m);
}

ll T = 0;

int main(){
	memset(dp, 0x3f, sizeof(dp));
	scanf("%lld", &T);
	for(ll i = 0 ; i < T ; i ++) init(), solve();
	return 0;
}

First of all, since $$$\text{and}, \text{or}, \text{xor}$$$ are all bitwise operations, each bit is independent of the other.

We define $$$a_i$$$ as the $$$i$$$-th bit of $$$a$$$, $$$b_i$$$ as the $$$i$$$-th bit of $$$b$$$, $$$c_i$$$ as the $$$i$$$-th bit of $$$c$$$, $$$d_i$$$ as the $$$i$$$-th bit of $$$d$$$, $$$m_i$$$ as the $$$i$$$-th bit of $$$m$$$, $$$x_i$$$ as the $$$i$$$-th bit of $$$x$$$, $$$y_i$$$ as the $$$i$$$-th bit of $$$y$$$. (in binary)

Lemma. For all $$$i \neq j$$$, if $$$(a_i, b_i, m_i) = (a_j, b_j, m_j)$$$ and $$$(c_i, d_i) \neq (c_j, d_j)$$$, the goal cannot be achieved.

Proof. Because after every operation we will have $$$(x_i, y_i) = (x_j, y_j)$$$, so we can't achieve he goal.

Since $$$(a_i, b_i, m_i)$$$ has only $$$2^3=8$$$ cases, and $$$(c_i, d_i)$$$ has only $$$2^2=4$$$ cases, and there are some $$$(0/1, 0/1, 0/1)$$$ that do not appear in $$$\{(a_i, b_i, m_i)\ |\ 0 \leq i \leq \log \max(a, b, c, d, m)\}$$$, so there are only $$$(4+1)^8<4\times 10^5$$$ cases in this problem. We can use BFS(breadth-first search) for preprocessing.

Time complexity: $$$O(5^8)$$$ for preprocessing and $$$O(\log \max(a, b, c, d, m))$$$ per test case.

Memory complexity: $$$O(5^8)$$$ for preprocessing and $$$O(1)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;

const int S = 4e5 + 5, Inf = 0x3f3f3f3f;
int pw5[10] = {}, dp[S] = {};
queue<int> Q;

inline void checkmin(int &x, int y){
	if(y < x) x = y;
}

inline int w(int mask, int i){
	return (mask / pw5[i]) % 5;
}

inline int f(int a, int b, int m){
	return (a << 2) | (b << 1) | m;
}

inline int g(int c, int d){
	return (c << 1) | d;
}

inline int work(int mask, int opt){
	int ret = 0;
	for(int a = 0 ; a < 2 ; a ++) for(int b = 0 ; b < 2 ; b ++) for(int m = 0 ; m < 2 ; m ++){
		int x = w(mask, f(a, b, m)), c = x >> 1, d = x & 1;
		if(opt == 1) c = c & d;
		else if(opt == 2) c = c | d;
		else if(opt == 3) d = c ^ d;
		else d = m ^ d;
		ret += pw5[f(a, b, m)] * g(c, d);
	}
	return ret;
}

inline void init(){
	pw5[0] = 1;
	for(int i = 1 ; i <= 8 ; i ++) pw5[i] = pw5[i - 1] * 5;
	memset(dp, 0x3f, sizeof(dp));
	int mask = 0;
	for(int a = 0 ; a < 2 ; a ++) for(int b = 0 ; b < 2 ; b ++) for(int m = 0 ; m < 2 ; m ++) mask += pw5[f(a, b, m)] * g(a, b);
	dp[mask] = 0, Q.push(mask);
	while(Q.size()){
		int s = Q.front(); Q.pop();
		for(int opt = 0 ; opt < 4 ; opt ++){
			int t = work(s, opt);
			if(dp[t] == Inf) dp[t] = dp[s] + 1, Q.push(t);
		}
	}
	for(int mask = 0 ; mask < pw5[8] ; mask ++) for(int i = 0 ; i < 8 ; i ++) if(w(mask, i) == 4){
		for(int x = 1 ; x <= 4 ; x ++) checkmin(dp[mask], dp[mask - x * pw5[i]]);
		break;
	}
}

inline void solve(){
	int A = 0, B = 0, C = 0, D = 0, M = 0, mask = pw5[8] - 1;
	scanf("%d %d %d %d %d", &A, &B, &C, &D, &M);
	for(int i = 0 ; i < 30 ; i ++){
		int a = (A >> i) & 1, b = (B >> i) & 1, c = (C >> i) & 1, d = (D >> i) & 1, m = (M >> i) & 1;
		if(w(mask, f(a, b, m)) == 4) mask -= (4 - g(c, d)) * pw5[f(a, b, m)];
		else if(w(mask, f(a, b, m)) != g(c, d)){
			printf("-1\n");
			return;
		}
	}
	printf("%d\n", (dp[mask] < Inf) ? dp[mask] : -1);
}

int T = 0;

int main(){
	init();
	scanf("%d", &T);
	for(int i = 0 ; i < T ; i ++) solve();
	return 0;
}

Let's solve this problem by dynamic programming, Let $$$f_u$$$ represents the max probability of reaching city $$$n$$$ starting from city $$$u$$$.

The problem is how to transition. for city $$$u$$$, we assume that there are $$$d$$$ roads from city $$$u$$$, the $$$i$$$-th road from city $$$u$$$ is to city $$$v_i$$$.

Let's sort $$$v$$$ by using $$$f_v$$$ as the keyword, with $$$v$$$ with the larger $$$f_v$$$ coming first.

Let's define an array $$$a$$$ of $$$d$$$ elements equals to $$$[f_{v_1}, f_{v_2}, \dots, f_{v_d}]$$$, according to the definition, $$$a$$$ is non-increasing.

That is, next we want to find an optimal solution such that the probability of going to city $$$v_i$$$ is $$$p_i$$$, maximizing the value of $$$\sum_{i=1}^d{a_i \times p_i}$$$.

Tips: the sum of $$$p_i$$$ may not be $$$1$$$, because it is possible to stay at city $$$u$$$ when $$$d$$$ is even.

For two choices of $$$p$$$, $$$p_1$$$ and $$$p_2$$$, Sometimes we can't which is better, for example:

$$$p_1 = [0.6, 0.2, 0.2], p_2 = [0.5, 0.5, 0]$$$

when $$$a=[1.0, 0.2, 0.2]$$$, $$$p_1$$$ is better than $$$p_2$$$. But when $$$a = [1.0, 0.8, 0]$$$, $$$p_2$$$ is better than $$$p_1$$$.

Tips. In fact, when $$$d=3$$$, $$$p$$$ has only one choice $$$[\frac 1 3, \frac 1 3, \frac 1 3]$$$, the above is just a hypothetical to illustrate the problem.

So when can we say, $$$p_1$$$ is always not worse than $$$p_2$$$?

Lemma. If there are two arrays $$$p_1$$$ and $$$p_2$$$, satisfying $$$\forall i \leq d, \sum_{j=1}^i{p_1}_j \geq \sum_{j=1}^i{p_2}_j$$$, $$$p_1$$$ is always not worse than $$$p_2$$$.

Proof. Let's consider $$$a_{d+1}$$$ as $$$0$$$ and define $$$a'_i = a_i - a_{i+1}$$$, $$${p_1'}_i = \sum_{j=1}^i{p_1}_j$$$ and $$${p_2'}_i = \sum_{j=1}^i{p_2}_j$$$, then $$$\sum_{i=1}^d a_i \times {p_1}_i = \sum_{i=1}^d a'_i \times {p_1'}_i, \sum_{i=1}^d a_i \times {p_2}_i = \sum_{i=1}^d a'_i \times {p_2'}_i$$$, Because $$$a$$$ is non-increasing, so $$$a'_i \geq 0$$$, and we have $$$\forall i \leq d, {p_1}_i \geq {p_2}_i$$$, so $$$\sum_{i=1}^d a_i \times {p_1}_i \geq \sum_{i=1}^d a_i \times {p_2}_i$$$.

Now the problem is, there is whether an array $$$p$$$ not worse than any other arrays for for all $$$d$$$.

For $$$d = 1$$$, $$$p = [1.0]$$$ satisfies the condition.

For $$$d = 2$$$, $$$p = [0.5, 0]$$$ satisfies the condition.

What about $$$d>2$$$ ? After they choose the roads for the first time, The size of the problem will become $$$d - 2$$$.

For example, when $$$d = 4$$$, Let's assume Jellyfish choose $$$v_1$$$ for the first time, then there will be $$$4$$$ situations:

1. When Asuka chooses $$$v_1$$$ they will go to $$$v_1$$$.
2. When Asuka chooses $$$v_2$$$, the problem changes into the subproblem with $$$[v_3, v_4]$$$.
3. When Asuka chooses $$$v_3$$$, the problem changes into the subproblem with $$$[v_2, v_4]$$$.
4. When Asuka chooses $$$v_4$$$, the problem changes into the subproblem with $$$[v_2, v_3]$$$.

By calculating, $$$p = [0.25, 0.25, 0.125, 0]$$$, it also satisfies the condition.

Let's define $$$g_k$$$ as the best array $$$p$$$ when $$$d=k$$$, $$$g'_k$$$ as the the probabilities of going to cities except the city Jellyfish choose for the first time. By recursion, we can get $$$g'_k$$$ from $$$g_{k-2}$$$. After that, we insert $$$\frac 1 k$$$ into $$$g'_k$$$, keeping it non-increasing, we will get $$$g_k$$$.

By calculating, we will find $$$\frac 1 k$$$ is always the first element in $$$g_k$$$. So when $$$k > 2$$$, we have the following transition:

• $$$g_{k, 1} = \frac 1 k$$$

• $$$\forall 1 < i \leq k, g_{k, i} = g_{k-2, i - 2} \times \frac {i - 2} k + g_{k-2, i - 1} \times \frac {k - i} k$$$

In the above we consider $$$g_{k, x}$$$ as $$$0$$$ for all $$$x = 0$$$ or $$$x > k$$$.

Because $$$g_{k-2}$$$ satisfies the condition, by greedy and induction we can show that $$$g_k$$$ satisfies the condition.

Time complexity: $$$O(\max(n)^2)$$$ for preprocessing and $$$O(m \log n)$$$ per test case.

Memory complexity: $$$O(\max(n)^2)$$$ for preprocessing and $$$O(m)$$$ per test case.

#include<bits/stdc++.h>

using namespace std;

const int N = 5000 + 5;
int n = 5000, m = 0;
vector<vector<int> > G(N), Gx(N);
long double f[N] = {}, g[N][N] = {};

inline bool cmp(int u, int v){
	return f[u] > f[v];
}

inline void work(int u){
	if(u == n){
		f[u] = 1.00;
		return;
	}
	sort(G[u].begin(), G[u].end(), cmp);
	int k = G[u].size();
	for(int i = 0 ; i < k ; i ++){
		int v = G[u][i];
		f[u] += f[v] * g[k][i + 1];
	}
}

inline void init(){
	for(int u = 1 ; u <= n ; u ++){
		f[u] = 0.00;
		G[u].clear(), Gx[u].clear();
	}
	n = m = 0;
}

inline void solve(){
	scanf("%d %d", &n, &m);
	for(int i = 1, u = 0, v = 0 ; i <= m ; i ++){
		scanf("%d %d", &u, &v);
		if(u != n){
			G[u].push_back(v);
			Gx[v].push_back(u);
		}
	}
	for(int u = n ; u >= 1 ; u --) work(u);
	printf("%.12Lf\n", f[1]);
}

int T = 0;

int main(){
	for(int i = 1 ; i <= n ; i += 2) for(int j = 1 ; j <= i ; j ++) g[i][j] = 1.00 / i;
	for(int i = 2 ; i <= n ; i += 2){
		g[i][1] = 1.00;
		for(int j = 1 ; j <= i ; j ++) g[i][j] /= i;
		if(i + 2 <= n) for(int j = 1 ; j <= i ; j ++) g[i + 2][j + 1] += g[i][j] * (i - j + 1), g[i + 2][j + 2] += g[i][j] * j;
	}
	scanf("%d", &T);
	for(int i = 1 ; i <= T ; i ++) init(), solve();
	return 0;
}

Let's assume that $$$a$$$ is given. We can use dynamic programming to solve the problem.

Let's define $$$f_i$$$ as the expected number of days Jellyfish needs to reach city $$$i$$$ from city $$$0$$$.

We will have:

• $$$f_0=0, f_1=1$$$

• $$$\forall i > 1, f_i=f_{i-1}+1+\frac {a_{i-1}} {a_{i-1}+a_i} \times (f_i - f_{i-2})$$$

Let's make it better:

• $$$f_i = f_{i-1} + 1 + \frac {a_{i-1}} {a_i} \times (f_{i-1}-f_{i-2}+1)$$$

What does this inspire us to do? Let's define $$$g_i=f_i-f_{i-1}$$$, then we will get:

• $$$g_1=1$$$

• $$$\forall i > 1, g_i = 1 + \frac {a_{i-1}} {a_i} \times (g_{i-1}+1)$$$

By induction, we will find $$$g_i = 1 + 2 \times \sum_{j=1}^{i-1} \frac {a_j} {a_i}$$$. According to the definition, $$$f_n = \sum_{i=1}^n g_n = n + 2 \times \sum_{i=1}^n\sum_{j=1}^{i-1} \frac{a_j}{a_i}$$$.

Then we can use dynamic programming to solve the problem itself.

Let's define $$$s_i = \sum_{j=1}^i a_j$$$, then $$$f_n = n + 2 \times \sum_{i=1}^n \frac{s_{i-1}}{a_i}$$$.

Let's define $$$dp_{i, x}$$$ as the minimum value of $$$\sum_{i=1}^n \frac{s_{i-1}}{a_i}$$$ when $$$s_i = x$$$.

We transit by enumerating the values of $$$a_{i+1}$$$, The transition is: $$$dp_{i+1, x+y} \leftarrow dp_{i, x} + \frac x y$$$.

But the time complexity is $$$O(n m^2)$$$, how can it becomes faster?

Let's take a closer look at $$$\sum_{i=1}^n\sum_{j=1}^{i-1} \frac{a_j}{a_i}$$$. If there exists $$$i < n$$$ satisfying $$$a_i > a_{i+1}$$$. We can swap $$$a_i$$$ and $$$a_{i+1}$$$, the answer will be better! so $$$a$$$ is a non-decreasing array, which means $$$a_i \leq \frac m {n - i + 1}$$$.

Because $$$\sum_{i=1}^n \frac n i$$$ is $$$O(n \log n)$$$, so if we only enumerate the possible values of $$$a_i$$$ the time complexity will be $$$O(m^2 \log m)$$$.

Time complexity: $$$O(m^2 \log m)$$$

Memory complexity: $$$O(nm)$$$

#include<bits/stdc++.h>

using namespace std;

const int N = 3000 + 5;
const long double Inf = 1e18;
int n = 0, m = 0;
long double dp[N][N] = {};

int main(){
	scanf("%d %d", &n, &m);
	for(int i = 0 ; i <= n ; i ++) for(int x = 0 ; x <= m ; x ++) dp[i][x] = Inf;
	dp[0][0] = 0.00;
	for(int i = 1 ; i <= n ; i ++) for(int x = 0 ; x <= m ; x ++) for(int y = 1 ; x + y * (n - i + 1) <= m ; y ++) dp[i][x + y] = min(dp[i][x + y], dp[i - 1][x] + 1.00 * x / y);
	printf("%.12Lf", 2 * dp[n][m] + n);
	return 0;
}

Firstly, if $$$lim > \frac {n(n+1)} 2$$$, the answer will be $$$0$$$. So we only need to solve the problem which satisfies $$$lim \leq \frac{n(n+1)} 2$$$.

We can use dynamic programming to solve the problem:

Let's define $$$dp_{i, a}$$$ means the number of the permutations $$$P$$$ of $$$[1, 2, \dots, i]$$$, satisfying $$$\mathrm{fun(P)} = a$$$.

Since only relative rankings are useful, we have the following transition:

• $$$dp_{0, 0} = 1$$$

• $$$\forall i > 0, dp_{i, a} = \sum_{j=1}^i \binom {i-1} {j-1} \sum_{b=0}^{a - i} dp_{j - 1, b} \times dp_{i - j, a - b - i}$$$

The time complexity is $$$O(n^6)$$$, because $$$a \leq \frac {i(i+1)} 2$$$, how can it becomes faster?

FFT (Fast Fourier Transform) might come to mind first. If we use FFT instead of enumerating $$$t$$$, The time complexity will become $$$O(n^4 \log n)$$$. This is not enough because $$$n$$$ is large and $$$10^9+7$$$ is not a modulus suitable for NTT (Number-theoretic Transform).

Tips. Also, if you use divide and conquer and FFT, The time complexity will be $$$O(n^3 \text{poly}(\log n))$$$, but because FFT have a big constant factor, this still can't pass the problem.

But we can do something similar to what the FFT does. We define $$$F_i = \sum_{a=1}^{\frac {n(n + 1)} 2} dp_{i, a} \times x^a$$$, then we will have the following transition:

• $$$F_0=1$$$

• $$$F_i = x^i \times \sum_{j=1}^i \binom{i - 1}{j - 1} F_{j-1} \times F_{i-j}$$$

For all $$$1 \leq i \leq n$$$, The degree of $$$F_i$$$ won't exceed $$$\frac {n(n+1)} 2$$$. So if we have $$$\frac{n(n+1)} 2 + 1$$$ points on $$$F_n$$$, We can get $$$F_n$$$ by in time complexity $$$O(n^4)$$$ using Lagrange Interpolation. The only thing we need to do is the dot product of the functions, so the time complexity of the transition is also $$$O(n^4)$$$.

Time complexity: $$$O(n^4)$$$

Memory complexity: $$$O(n^3)$$$

#include<bits/stdc++.h>

using namespace std;
typedef long long ll;

const ll N = 200 + 5, Mod = 1e9 + 7;

inline ll power(ll x, ll y){
	ll ret = 1;
	while(y){
		if(y & 1) ret = ret * x % Mod;
		x = x * x % Mod, y >>= 1;
	}
	return ret;
}

ll n = 0, k = 0, lim = 0;
ll C[N][N] = {}, pw[N * N][N] = {}, iv[N * N] = {}, ifac[N * N] = {}, dp[N][N * N] = {};
ll a[N * N] = {}, b[N * N] = {}, ans = 0;

int main(){
	scanf("%lld %lld", &n, &k); lim = n * (n + 1) / 2;
	for(ll i = 0 ; i <= n ; i ++){
		C[i][0] = 1;
		for(ll j = 1 ; j <= i ; j ++) C[i][j] = (C[i - 1][j - 1] + C[i - 1][j]) % Mod;
	}
	ifac[0] = 1;
	for(ll x = 0 ; x <= lim ; x ++){
		pw[x][0] = 1;
		if(x) iv[x] = power(x, Mod - 2), ifac[x] = ifac[x - 1] * iv[x] % Mod;
		for(ll i = 1 ; i <= n ; i ++) pw[x][i] = pw[x][i - 1] * x % Mod;
	}
	for(ll x = 0 ; x <= lim ; x ++) dp[0][x] = 1;
	for(ll i = 1 ; i <= n ; i ++){
		for(ll j = 0 ; j < i ; j ++) for(ll x = 0 ; x <= lim ; x ++) dp[i][x] = (dp[i][x] + dp[j][x] * dp[i - 1 - j][x] % Mod * C[i - 1][j]) % Mod;
		for(ll x = 0 ; x <= lim ; x ++) dp[i][x] = dp[i][x] * pw[x][i] % Mod;
	}
	a[0] = 1;
	for(ll i = 0 ; i <= lim ; i ++){
		for(ll x = lim ; x >= 1 ; x --) a[x] = (a[x - 1] + a[x] * (Mod - i)) % Mod;
		a[0] = a[0] * (Mod - i) % Mod;
	}
	for(ll i = 1 ; i <= lim ; i ++) if(dp[n][i]){
		ll w = dp[n][i] * ifac[i] % Mod * ifac[lim - i] % Mod;
		if((lim - i) & 1) w = Mod - w;
		b[0] = a[0] * (Mod - iv[i]) % Mod;
		for(ll x = 1 ; x <= lim ; x ++) b[x] = (a[x] - b[x - 1] + Mod) * (Mod - iv[i]) % Mod;
		for(ll x = k ; x <= lim ; x ++) ans = (ans + b[x] * w) % Mod;
	}
	printf("%lld\n", ans);
	return 0;
}

Let's call a section $$$[l, r]$$$ bad if $$$[p_l, p_{l+1}, \dots, p_{r-1}, p_r]$$$ is a permutation of $$$[l, l + 1, \dots, r - 1, r]$$$ and $$$l \leq r \leq m_l$$$.

Let's call a section $$$[l, r]$$$ primitive if it's a bad section and there are no section $$$[l', r']$$$ satisfying $$$[l', r']$$$ is also a bad section and $$$[l, r]$$$ covers $$$[l', r']$$$.

Lemma. For all primitive sections, none two of them intersect.

Proof. If $$$[l_1, r_1]$$$ is a bad section, $$$[l_2, r_2]$$$ is a bad section and $$$l_1 < l_2 < r_1 < r_2$$$, the section $$$[l_2, r_1]$$$ will also be a bad section, but both $$$[l_1, r_1]$$$ and $$$[l_2, r_2]$$$ covers $$$[l_2, r_1]$$$, so $$$[l_1, r_1]$$$ and $$$[l_2, r_2]$$$ can't be primitive at the same time.

Let's use the principle of inclusion-exclusion and dynamic programming to solve the problem.

Let's define $$$f(l, r)$$$ as the number of $$$p_l, p_{l+1}, \dots, p_{r-1}, p_r$$$ which is a primitive section. By the principle of inclusion-exclusion, we count the ways to fix $$$k$$$ primitive sections in range $$$[l, r]$$$, and arrange the rest arbitrarily.

Then we can define $$$g_{1/2}(l, r, x)$$$ as the number of ways to fix $$$k$$$ ($$$k$$$ is odd/even) primitive sections in range $$$[l, r]$$$, and the number of the positions which doesn't cover by any primitive section is $$$x$$$.

Finally we define $$$g(l, r, x) = g_2(l, r, x) - g_1(l, r, x)$$$, We will have the following transition:

• $$$\forall l \leq r \leq m_l, f(l, r) = \sum_{x=0}^{r-l+1} g(l, r, x) \times x! + f(l, r)$$$

• $$$g(l, r, x) = g(l, r - 1, x - 1) - \sum_{mid=l}^r g(l, mid - 1, x )\times f(mid, r)$$$

Tips. In the transition of $$$f(l, r)$$$, We add $$$f(l, r)$$$ because $$$f(l, r)$$$ contributes to $$$g(l, r, 0)$$$, but it should not contribute to $$$f(l, r)$$$, This problem can be solved by ordering the transitions.

According to the definition, $$$\sum_{x=0}^{n} g(1, n, x) \times x!$$$ is the answer.

Time complexity: $$$O(n^4)$$$

Memory complexity: $$$O(n^3)$$$

#include<bits/stdc++.h>

using namespace std;
typedef long long ll;

const ll N = 200 + 5, Mod = 1e9 + 7;
ll n = 0, m[N] = {}, fac[N] = {}, f[N][N] = {}, g[N][N][N] = {};

int main(){
	scanf("%lld", &n);
	for(ll i = 1 ; i <= n ; i ++) scanf("%lld", &m[i]);
	if(m[1] == n){
		printf("0");
		return 0;
	}
	fac[0] = 1;
	for(ll i = 1 ; i <= n ; i ++) fac[i] = fac[i - 1] * i % Mod;
	for(ll i = 1 ; i <= n ; i ++) g[i][i - 1][0] = 1;
	for(ll l = n ; l >= 1 ; l --) for(ll r = l ; r <= n ; r ++){
		for(ll x = 1 ; x <= r - l + 1 ; x ++) g[l][r][x] = g[l][r - 1][x - 1];
		for(ll mid = l ; mid < r ; mid ++) if(r <= m[mid + 1]) for(ll x = 0 ; x <= mid - l + 1 ; x ++) g[l][r][x] = (g[l][r][x] + g[l][mid][x] * (Mod - f[mid + 1][r])) % Mod;
		for(ll x = 0 ; x <= r - l + 1 ; x ++) f[l][r] = (f[l][r] + g[l][r][x] * fac[x]) % Mod;
		if(r <= m[l]) g[l][r][0] = (g[l][r][0] + (Mod - f[l][r])) % Mod;
	}
	printf("%lld", f[1][n]);
	return 0;
}

For convenience, let's define $$$k = \max(a, b, x, y)$$$, use operation 1 for "You will get a card with $$$a$$$ HP, and $$$b$$$ damage", operation 2 for "If you have at least one card, choose one of your cards and increase its HP by $$$x$$$" and operation 3 for "If you have at least one card, choose one of your cards and increase its damage by $$$y$$$", operation 4 for "You will get a prop with $$$w$$$ power", operation 5 for "You can choose at most one of your cards and multiply its damage by $$$10^9$$$", "the $$$i$$$-th card" for the card we get from vertex $$$i$$$ (It must be operation 1 on vertex $$$i$$$), "do operation 2/3/5 onto card $$$i$$$" for "When we do this operation 2/3/5, we will choose the card $$$i$$$ and increase his HP or damage".

Let us consider the problem without the operation 5, what's the maximum possible answer. If you want to maximize the the sum of the power of your cards, the answer will not exceeding $$$(100 \times 200)^2 = 4 \times 10^8$$$; If you you want to maximize the the sum of the power of your props, the answer will not exceeding $$$200 \times 10^6 = 2 \times 10^8$$$. Because $$$4 \times 10^8 + 2 \times 10^8 = 6 \times 10^8 < 10^9$$$, so the operation $$$n$$$ is the most important. Let's called the card we wil do the operation 5 onto it the "flash card". Let's use meet-in-the-middle, the problem is divided into two subproblems — the game before we get the flash card and the game after we get the flash card.

1. The game after we get the flash card

It's a easy problem, we can use dynamic programming to solve this problem.

Since the most important thing is the HP and damage of the flash card, so we define the following dynamic programming state:

• $$$dp_b(u, a)$$$ means we are current at vertex $$$u$$$, the current HP of the flash card is $$$a$$$, the maximum damage of the flash card.
• $$$dp_c(u, a)$$$ means we are current at vertex $$$u$$$, the current HP of the flash card is $$$a$$$, the damage of the flash card is $$$dp_b(u, a)$$$, the maximum sum of the power of all the other cards and props.

Since $$$a \leq nk$$$, the time complexity of the transition is $$$O(m \times n \times k)$$$.

2. The game before we get the flash card

This is the key of the problem, and since it's much more difficult, we first consider the subproblems of this problem.

I. If the graph is a chain, and all the operation 2 and operation 3 is after the operation 1

Lemma. We will do all the operation 2 onto one of the cards, symmetrically we will also do all the operation 3 onto one of the cards.

Proof. We consider a sequence of operations, let's consider if we do all the operation 1 on the card with the max damage after doing all the operation 2, the answer won't be worse, we can do all the operation 1 onto this card instead, then we make a symmetrical adjustment, the answer won't be worse, and all the operation 2 is done onto one of the cards, all the operation 3 is done onto one of the cards.

II. If the graph is a chain

It's similar to the subproblem I. If we say subproblem I is like a "global max value", then subproblem II is like a "prefix max value".

Let's define $$$p_i$$$ means we will do the operation 2/3 on vertex $$$i$$$ onto the $$$p_i$$$-th card.

Lemma1. If there is a operation 2 on vertex $$$i$$$ and a operation 2 on vertex $$$j$$$, if $$$p_i < j < i$$$, we will have $$$p_j = p_i$$$.

Proof. Let's consider the final HP and damage of the cards after all the operations. Because we do the operation 2 on vertex $$$i$$$ onto the $$$p_i$$$-th card, for all $$$i' < i$$$, the damage of the $$$i'$$$-th card is not larger than the $$$p_i$$$-th card. So for $$$j$$$, if we don't do the operation 2 on vertex j onto the $$$p_i$$$-th card, we can do it onto the $$$p_i$$$-th card instead, the answer won't be worse.

Symmetrically, Lemma1 is also correct for operation 3.

Now we can use dynamic programming to solve the problem, we define the following dynamic programming state:

• $$$f(u, a, b)$$$ means we are current at vertex $$$u$$$, we will do the next several operation 3 onto a card with $$$a$$$ HP after all the operations and we will do the next several operation 2 onto a card with $$$b$$$ damage after all the operations, and this two cards are not the same.
• $$$g(u, a, b)$$$ means we are current at vertex $$$u$$$, We will do the next several operation 2 and operation 3 onto a card currently having $$$a$$$ HP and $$$b$$$ damage.

The time complexity is $$$O(m \times n^2 \times k^2)$$$, but it's not enough.

Lemma2. If a card has $$$a$$$ HP and $$$b$$$ damage after all the operations and it's not the flash card, $$$\min(a, b) \leq k$$$.

Proof. If we use this card as the flash card instead of the last one, and do all the operations done onto the last flash card onto this card, the power of the flash card will be larger. So it won't exist in this half problem.

Now the time complexity becomes $$$O(m \times n \times k^2)$$$, it's still not enough.

Lemma3. Let's define $$$A$$$ as the maximum HP of all the cards except the flash card after all the operations, $$$B$$$ as the maximum damage of all the cards except the flash card after all the operations, $$$\min(A, B) \leq k$$$.

Proof. Let's assume that the $$$i$$$-th card has $$$A$$$ HP after all the operations, the $$$j$$$-th card has $$$B$$$ damage after all the operations and $$$A > k, B > k$$$. If $$$i = j$$$, it conflicts with Lemma 2; If $$$i < j$$$, because of the Lemma 2, the HP of the $$$j$$$-th card after all the operations won't exceed $$$k$$$, let's use $$$A'$$$ as the HP of the $$$j$$$-th card after all the operations, and $$$B > k$$$, so we have done some operation 3 onto the $$$j$$$-th card. But because $$$A > k \geq A'$$$, if we do these operations onto the $$$i$$$-th card, the answer will be better; If $$$i > j$$$, it's symmetric with $$$i < j$$$.

But we can make the dynamic programming state better:

• $$$f(u, a, b)$$$ means we are current at vertex $$$u$$$, we will do the next several operation 3 onto a card with $$$a$$$ HP after all the operations and we will do the next several operation 2 onto a card with $$$b$$$ damage after all the operations, and this two cards are not the same.

• $$$g_a(u, a, a')$$$ means we are current at vertex $$$u$$$, we will do the next several operation 3 onto a card with $$$a$$$ HP after all the operations, we will do the next several operation 2 onto a card and totally increase $$$a'$$$ HP in the next several operations to reach it's HP after all the operations.

• $$$g_b(u, b, b')$$$ it's symmetric with $$$g_a$$$, just swap "HP" and "damage".

Let's define $$$A$$$ as the maximum HP of all the cards except the flash card after all the operations, $$$B$$$ as the maximum damage of all the cards except the flash card after all the operations. We will find the $$$a, a', b, b'$$$ in $$$f, g_a, g_b$$$ is $$$O(k)$$$ because if $$$A \leq k$$$, using $$$g_a$$$ we can get the right answer, if $$$B \leq k$$$, using $$$g_b$$$ we can get the right answer.

the time complexity of the transition is $$$O(m \times k^2)$$$.

The transition is very complex, you can see more details in my code : )

In my code, I use $$$g(u)$$$ to make the transition better. When $$$a'$$$ or $$$b'$$$ reaches $$$0$$$, we reaches the vertex $$$u$$$ and there is a operation 1 on vertex $$$u$$$, I don't enumerate the value of the next $$$a$$$ and $$$b$$$ while transiting. I transit it to $$$g(u)$$$ first, and enumerate the value of the next $$$a$$$ and $$$b$$$ together.

III. the problem itself

Since the path is a chain, and we use dynamic programming to solve the problem. There's no difference whether the graph is a chain.

Time complexity: $$$O(m(nk+k^2))$$$

Memory complexity: $$$O(n^2k+nk^2)$$$

#include<bits/stdc++.h>

using namespace std;
typedef long long ll;

const int N = 200 + 5;
const ll lim = 1e9;

inline void checkmax(int &x, int y){
	if(y > x) x = y;
}

inline void checkmax(ll &x, ll y){
	if(y > x) x = y;
}

inline void checkmax(pair<int, int> &x, pair<int, int> y){
	if(y > x) x = y;
}

int n = 0, m = 0, k = 0, opt[N] = {}, a[N] = {}, b[N] = {}, w[N] = {};
int f[N][N][N] = {}, g[N] = {}, g_a[N][N][N] = {}, g_b[N][N][N] = {};
pair<int, int> dp[N][N * N] = {};
vector<vector<int> > G(N);

int main(){
	scanf("%d %d", &n, &m);
	for(int u = 1 ; u <= n ; u ++){
		scanf("%d", &opt[u]);
		if(opt[u] == 1) scanf("%d %d", &a[u], &b[u]);
		else if(opt[u] == 2) scanf("%d", &a[u]);
		else if(opt[u] == 3) scanf("%d", &b[u]);
		else if(opt[u] == 4) scanf("%d", &w[u]);
		k = max(k, max(a[u], b[u]));
	}
	for(int i = 1, u = 0, v = 0 ; i <= m ; i ++){
		scanf("%d %d", &u, &v);
		G[u].push_back(v);
	}
	memset(f, -1, sizeof(f));
	memset(g, -1, sizeof(g)), memset(g_a, -1, sizeof(g_a)), memset(g_b, -1, sizeof(g_b));
	memset(dp, -1, sizeof(dp));
	f[1][0][0] = 0;
	for(int u = 1 ; u <= n ; u ++){
		if(opt[u] == 1 && g[u] != -1){
			for(int x = a[u] ; x <= k ; x ++) checkmax(g_a[u][x][x - a[u]], g[u] + x * b[u]);
			for(int x = b[u] ; x <= k ; x ++) checkmax(g_b[u][x][x - b[u]], g[u] + x * a[u]);
			checkmax(dp[u][a[u]], make_pair(b[u], g[u]));
		}
		for(int v : G[u]){
			if(opt[v] == 1){
				for(int x = 0 ; x <= k ; x ++) for(int y = 0 ; y <= k ; y ++){
					if(f[u][x][y] != -1){
						checkmax(f[v][max(x, a[v])][max(y, b[v])], f[u][x][y] + a[v] * b[v]);
						checkmax(g[v], f[u][x][y]);
					}
					if(g_a[u][x][y] != -1){
						checkmax(g_a[v][max(x, a[v])][y], g_a[u][x][y] + a[v] * b[v]);
						if(!y){
							checkmax(g[v], g_a[u][x][y]);
							checkmax(f[v][x][b[v]], g_a[u][x][y] + a[v] * b[v]);
						}
					}
					if(g_b[u][x][y] != -1){
						checkmax(g_b[v][max(x, b[v])][y], g_b[u][x][y] + a[v] * b[v]);
						if(!y){
							checkmax(g[v], g_b[u][x][y]);
							checkmax(f[v][a[v]][x], g_b[u][x][y] + a[v] * b[v]);
						}
					}
				}
				for(int x = 0 ; x <= n * k ; x ++) if(dp[u][x] != make_pair(-1, -1)){
					int y = dp[u][x].first, z = dp[u][x].second;
					checkmax(dp[v][x], make_pair(y, z + a[v] * b[v]));
				}
			}
			else if(opt[v] == 2){
				for(int x = 0 ; x <= k ; x ++) for(int y = 0 ; y <= k ; y ++){
					if(f[u][x][y] != -1) checkmax(f[v][x][y], f[u][x][y] + a[v] * y);
					if(g_a[u][x][y] != -1 && y >= a[v]) checkmax(g_a[v][x][y - a[v]], g_a[u][x][y]);
					if(g_b[u][x][y] != -1) checkmax(g_b[v][x][y], g_b[u][x][y] + a[v] * x);
				}
				for(int x = 0 ; x <= n * k ; x ++) if(dp[u][x] != make_pair(-1, -1)){
					int y = dp[u][x].first, z = dp[u][x].second;
					checkmax(dp[v][x + a[v]], make_pair(y, z));
				}
			}
			else if(opt[v] == 3){
				for(int x = 0 ; x <= k ; x ++) for(int y = 0 ; y <= k ; y ++){
					if(f[u][x][y] != -1) checkmax(f[v][x][y], f[u][x][y] + x * b[v]);
					if(g_a[u][x][y] != -1) checkmax(g_a[v][x][y], g_a[u][x][y] + x * b[v]);
					if(g_b[u][x][y] != -1 && y >= b[v]) checkmax(g_b[v][x][y - b[v]], g_b[u][x][y]);
				}
				for(int x = 0 ; x <= n * k ; x ++) if(dp[u][x] != make_pair(-1, -1)){
					int y = dp[u][x].first, z = dp[u][x].second;
					checkmax(dp[v][x], make_pair(y + b[v], z));
				}
			}
			else{
				for(int x = 0 ; x <= k ; x ++) for(int y = 0 ; y <= k ; y ++){
					if(f[u][x][y] != -1) checkmax(f[v][x][y], f[u][x][y] + w[v]);
					if(g_a[u][x][y] != -1) checkmax(g_a[v][x][y], g_a[u][x][y] + w[v]);
					if(g_b[u][x][y] != -1) checkmax(g_b[v][x][y - b[v]], g_b[u][x][y] + w[v]);
				}
				for(int x = 0 ; x <= n * k ; x ++) if(dp[u][x] != make_pair(-1, -1)){
					int y = dp[u][x].first, z = dp[u][x].second;
					checkmax(dp[v][x], make_pair(y, z + w[v]));
				}
			}
		}
	}
	ll ans = f[n][0][0];
	for(int x = 0 ; x <= n * k ; x ++) if(dp[n][x] != make_pair(-1, -1)){
		int y = dp[n][x].first, z = dp[n][x].second;
		checkmax(ans, lim * x * y + z);
	}
	printf("%lld", ans);
	return 0;
}

1. orzdevinwang

2. tourist

3. Benq

4. Qingyu

5. jqdai0815

1. Rapturelike

2. FlyBell

3. rainboy

4. RollingPanda

5. WanYuanShenWanDe

A. H_stove

B. tourist

C. ksun48

D. ooOAM

E. RanRankeainie

F. i_will_be_less_than_blue

G. jiangly (after the contest)

A. Azaml

B. Shirotsume

C. viveksingh912

D. kaiboy

E. FlyBell

F. Rapturelike

G. rainboy