**Subtask 1: $N \leq 100$ ($O(N^5)$)**

For this problem, we can apply a brute force to simulate the process described in the problem statement for every $(K,L)$ pair. We can iterate through all $K$ length substrings, and find the lexicographic minimum out of all $L$ length substrings contained within. We can then count the number of unique "winning" gene positions and add it to a larger array.

Note that while the time complexity of this brute force is $O(N^5)$, there is a small fractional constant factor due to the restrictions on $K$ and $L$ and the number of valid substrings given $K$, so $N=100$ runs comfortably in the TL.

Benjamin Qi's Python code:

N = int(input()) S = input() cnt = [0] * (N + 1) for K in range(1, N + 1): for L in range(K, N + 1): marked = [0] * N for i in range(N - L + 1): best = None best_idx = None for j in range(i, i + L - K + 1): if best is None or S[j : j + K] < best: best = S[j : j + K] best_idx = j marked[best_idx] = True cnt[sum(marked)] += 1 for v in range(1, N + 1): print(cnt[v])

**Subtask 1 / 2: $N \leq 500$ ($O(N^4)$)**

For this subtask, we can switch how we think about this problem. Instead of determining how many "winning" gene positions a given $(K,L)$ pair has, we can instead focus on some position $p_i$ and determine which $(K,L)$ pairs that $p_i$ is a "winning" position for.

Let us fix some position $p_i$ and some length $L$. We want to determine what conditions on $K$ are necessary for $p_i$ to be a winning position. The first observation we can make is that $p_i$ is a winning position as long as there exists some window of $K$ characters where $S[p_i:p_i+L]$ is lexicographically minimal. Let $a_i$ be the maximum position such that $a_i < p_i$ and $S[a_i:a_i+L] \leq S[p_i:p_i+L]$ and let $b_i$ be the minimum positions such that $b_i > p_i$ and $S[b_i:b_i+L] < S[p_i:p_i+L]$ (note our first equation uses $\leq$ due to the tiebreaker condition favoring smaller indices).

We can always identify a window of $K$ characters where $p_i$ is winning as long as our window doesn't "include" the ranges $[a_i:a_i+L]$ and $[b_i:b_i+L]$. In other words, any $K$ where $K \leq (b_i+L)-a_i-2$ makes $p_i$ a winning position.

Now that we have identified how to determine valid $K$ given $p_i$ and $L$, we can create our solution from here. We can iterate over all $p_i$ and $L$. To determine $a_i$ and $b_i$, we can iterate over all $L$ length substrings until we identify $a_i$ or $b_i$ that satisfy the above conditions. If none exist, we can use $-1$ for $a_i$ and $N$ for $b_i$.

Since we iterate over the string to find $a_i$ and $b_i$ for every position $p_i$ and length $L$, and we call the substring function repeatedly, the time complexity of this solution is $O(N^4)$. However, this ends up running fast enough to pass subtask 2 in practice.

My Java code:

import java.io.PrintWriter; import java.util.Scanner; public class MinimizerNFourth { public static void main(String[] args){ Scanner sc = new Scanner(System.in); PrintWriter pw = new PrintWriter(System.out); int n = sc.nextInt(); String s = sc.next(); int[][] lk = new int[n+1][n+1]; for (int p = 0; p < n; p++){ for (int l = 1; l <= n; l++){ if (l+p > n) continue; int b = p+1; int a = p-1; while (b < n && compare(p,b,l,s) <= 0) b++; b = Math.min(n+1,b+l); while (a >= 0 && compare(p,a,l,s) < 0) a--; a++; for (int k = l; k <= Math.min(b-a-1,n); k++){ lk[l][k]++; } } } int[] count = new int[n+1]; for (int i = 0; i <= n; i++){ for (int j = i; j <= n; j++){ count[lk[i][j]]++; } } for (int i = 1; i <= n; i++){ pw.println(count[i]); } pw.close(); } static int compare(int i, int j, int k, String original){ return original.substring(i,Math.min(original.length(),i+k)).compareTo(original.substring(j,Math.min(original.length(),j+k))); } }

**Subtask 2 / Subtask 3 Partial: $O(N^3)$**

To improve on the last subtask, we want to improve our compare function. To do
this, we can precompute a *longest common prefix* (lcp) array that stores
for positions $i$ and $j$ the largest value $k$ such that
$S[i:i+k] = S[j:j+k]$. If we had this array, we could compare any two substrings
at positions $i$ and $j$ in $O(1)$ without using the substring function by
comparing the characters at $S[i+lcp[i][j]]$ and $S[j+lcp[i][j]]$ or returning a
tie if $lcp[i][j] \geq k$ (since we only take the first $k$ characters at both
positions.

To compute this array, we can naively compute this array by finding the lcp for every pair of indices.

Now that the cost of comparing strings is reduced, we need to improve the complexity of computing valid $K$ for every value of $L$. The first observation we make is that $a_i$ is monotonically decreasing for increasing value of $L$. Suppose this was not the case. This would mean that for some $L_1 < L_2$, $S[a_i:a_i+L_1] > S[p_i:p_i+L_1]$ but $S[a_i:a_i+L_2] \leq S[p_i:p_i+L_2]$. If one string is less than another string of equal length, this will continue to hold even if we extend both strings by a character, so we form a contradiction here.

Similarly we can make the same observation for $b_i$. Since it would be monotonically decreasing for increasing values of $L$, it would be monotonically increasing for decreasing values of $L$. Consequently, we can compute $a_i$ and $b_i$ for every value of $L$ in $O(N)$ using a pointer.

Finally, in our previous solution we used a for loop to set every value of $L \leq k \leq b_i+L-a_i-2$ as valid. Instead, we can use prefix sums to increment a range up to $b_i+L-a_i-2$ in $O(1)$. While most of our computation is quick, we are bottlenecked by the LCP computation, so this solution runs in $O(N^3)$.

Depending on your implementation, you might also be able to pass many of the last batch of test cases using a solution of this complexity.

My Java code:

import java.io.PrintWriter; import java.util.Scanner; public class MinimizerSlowLCP { public static void main(String[] args){ Scanner sc = new Scanner(System.in); PrintWriter pw = new PrintWriter(System.out); int n = sc.nextInt(); String s = sc.next(); int[][] lcp = new int[n+1][n+1]; int[][] deltas = new int[n+1][n+1]; for (int i = 0; i < n; i++){ for (int j = 0; j < n; j++){ int len = 0; int lp = i; int rp = j; while (lp < n && rp < n && s.charAt(lp++) == s.charAt(rp++)) len++; lcp[i][j] = len; } } for (int p = 0; p < n; p++){ int[] b = new int[n+1]; int[] a = new int[n+1]; int b_cand = p+1; for (int l = n; l >= 1; l--){ while (b_cand < n && compare(p,b_cand,l,lcp,s) <= 0) b_cand++; b[l] = Math.min(b_cand+l,n+1); } int a_cand = p-1; for (int l = 1; l <= n; l++){ while (a_cand >= 0 && compare(p,a_cand,l,lcp,s) < 0) a_cand--; a[l] = a_cand+1; } for (int l = 1; l <= n; l++){ if (l+p > n) continue; deltas[l][Math.min(b[l]-a[l]-1,n)]++; } } int[] count = new int[n+1]; for (int l = 1; l <= n; l++){ int acc = 0; for (int k = n; k >= l; k--){ acc += deltas[l][k]; count[acc]++; } } for (int i = 1; i <= n; i++){ pw.println(count[i]); } pw.close(); } static int compare(int i, int j, int k, int[][] lcp, String orig){ if (lcp[i][j] >= k) return 0; if (i+lcp[i][j] == orig.length()) return -1; if (j+lcp[i][j] == orig.length()) return 1; return Character.compare(orig.charAt(i+lcp[i][j]),orig.charAt(j+lcp[i][j])); } }

**Full Solution**

To speed up our solution to $O(N^2)$, we need to speed up our LCP array precomputation. However, instead of computing it naively, we can avoid extra computations by leveraging the following property. If we know $LCP[i+1][j+1]$, we can determine $LCP[i][j]$ depending on if character $i$ is equal to character $j$. This motivates working backwards to compute LCP, using the following recurrence.

lcp[i][j] = (S[i] == S[j]) + lcp[i+1][j+1]

My Java code:

import java.io.PrintWriter; import java.util.Scanner; public class Minimizer { public static void main(String[] args){ Scanner sc = new Scanner(System.in); PrintWriter pw = new PrintWriter(System.out); int n = sc.nextInt(); String s = sc.next(); int[][] lcp = new int[n+1][n+1]; int[][] deltas = new int[n+1][n+1]; for (int i = n-1; i >= 0; i--){ for (int j = n-1; j >= 0; j--){ if (s.charAt(i) == s.charAt(j)) lcp[i][j] = 1+lcp[i+1][j+1]; } } for (int p = 0; p < n; p++){ int[] b = new int[n+1]; int[] a = new int[n+1]; int b_cand = p+1; for (int l = n; l >= 1; l--){ while (b_cand < n && compare(p,b_cand,l,lcp,s) <= 0) b_cand++; b[l] = Math.min(b_cand+l,n+1); } int a_cand = p-1; for (int l = 1; l <= n; l++){ while (a_cand >= 0 && compare(p,a_cand,l,lcp,s) < 0) a_cand--; a[l] = a_cand+1; } for (int l = 1; l <= n; l++){ if (l+p > n) continue; deltas[l][Math.min(b[l]-a[l]-1,n)]++; } } int[] count = new int[n+1]; for (int l = 1; l <= n; l++){ int acc = 0; for (int k = n; k >= l; k--){ acc += deltas[l][k]; count[acc]++; } } for (int i = 1; i <= n; i++){ pw.println(count[i]); } pw.close(); } static int compare(int i, int j, int k, int[][] lcp, String orig){ if (lcp[i][j] >= k) return 0; if (i+lcp[i][j] == orig.length()) return -1; if (j+lcp[i][j] == orig.length()) return 1; return Character.compare(orig.charAt(i+lcp[i][j]),orig.charAt(j+lcp[i][j])); } }