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[CodeChef] Starters 250 — Subsequence 1: Chains, not cuts

Published 6 August 2026

SUB1 (Subsequence 1) gives you an array \(A\) and lets \(f(A)\) be the largest \(L\) such that \(1, 2, \dots, L\) appears in \(A\) as a subsequence. You may cut \(A\) into any number of contiguous pieces \(A_1 + A_2 + \dots + A_K\) and you want to maximise \(\sum_i f(A_i)\). You choose \(K\) too.

Rating ~1749. The interesting part is not the final code — it’s twelve lines — but how the problem stops looking like a partitioning problem.

The trap: thinking about cut points

My first framing was the obvious one. Let \(dp[i]\) be the answer for the prefix ending at \(i\), and transition by choosing where the last piece begins:

\[dp[i] = \max_{j < i}\ \big(dp[j] + f(A[j{+}1 \ldots i])\big)\]

That’s \(O(N^2)\) transitions, and each \(f\) of a segment needs its own structure to evaluate — a BIT where I add each value and look for the largest prefix that’s fully covered, so \(O(N^2 \log N)\) overall. Correct, and hopeless against \(N \le 2\cdot10^5\).

The instinct at this point is to speed up “evaluate \(f\) on a segment.” That’s the trap. The right move is to notice that the cut points don’t matter at all.

The reframing: you’re selecting elements, not choosing cuts

Look at what the answer is actually made of. In the sample [2, 1, 1, 2, 1, 3, 4] the answer is 5, realised by the split [2,1] + [1,2,1,3,4]. But what contributes is only the elements 1 and 1,2,3,4 — a subsequence of \(A\) made of consecutive runs \(1,2,\dots,L_1\) then \(1,2,\dots,L_2\), and so on.

Claim: the answer equals the maximum of \(\sum_j L_j\) over all ways to pick disjoint, index-ordered chains \(1,2,\dots,L_j\) as a subsequence of \(A\).

Both directions are one line each:

  • Any selection is achievable. Given chains \(C_1, \dots, C_k\) in index order, cut \(A\) immediately after the last element of each chain. Piece \(j\) then contains \(C_j\) entirely, so \(f(A_j) \ge |C_j|\).
  • Any split gives a selection. Whatever \(f(A_j)\) is, it’s witnessed by an actual chain inside \(A_j\), and those chains are disjoint and ordered.

So the cut points were never a real degree of freedom. They’re free to place after the fact. What’s left is a pure left-to-right selection problem, which is a much friendlier shape.

Two observations that kill the search space

\(f\) grows by at most 1 per element. Appending one element to an array either extends the chain by exactly one or leaves it alone. This is what makes the last piece cheap to reason about: if the last element \(a_i\) is used at all, it closes a chain whose top value is exactly \(a_i\). Not more, not less.

Among all chains closing at \(i\) with value \(v = a_i\), take the shortest one. All of them pay you the same \(v\). A shorter chain starts later, which leaves a longer prefix to the left, and \(dp\) is non-decreasing — so the latest-starting chain dominates every other. No tie-breaking, no comparison needed. This is a clean exchange argument, and it’s the entire reason a greedy array replaces the BIT.

Together: at index \(i\) there is exactly one candidate transition worth trying.

The DP

Let largest_chain_start[v] be the largest index at which a chain \(1,2,\dots,v\) can begin, among chains fully contained in the prefix seen so far. Then:

\[dp[i] = \max\big(dp[i-1],\; a_i + dp[\texttt{largest\_chain\_start}[a_i] - 1]\big)\]

and when \(v = a_i\) occurs, the latest chain reaching \(v-1\) gets extended: largest_chain_start[v] = largest_chain_start[v-1].

Two invariants make this safe:

  • largest_chain_start[v] is only touched when \(v\) actually occurs, because any chain topping out at \(v\) must physically end on an occurrence of \(v\).
  • largest_chain_start[v] <= largest_chain_start[v-1] always holds, and largest_chain_start[v-1] is non-decreasing over time, so the assignment never moves an index backwards. A plain array suffices; nothing ever needs undoing.

The case \(v = 1\) is genuinely special: a fresh chain extends nothing, so it can begin at any position. Whenever \(a_i = 1\), taking it as a one-element chain is always an option (\(dp[i] \ge 1 + dp[i-1]\)), and the latest place a chain \(1\) can begin is \(i\) itself, so largest_chain_start[1] moves up to \(i\).

def solve():
    n = int(input())
    a = list(map(int, input().split()))

    dp = [0]*(n+1)
    largest_chain_start = [0]*(n+1)

    for i in range(1, n+1):
        dp[i] = dp[i-1]

        cur = a[i-1]
        # find the chain which has largest starting point and ends with cur-1
        if largest_chain_start[cur-1] > 0:
            prev_idx = largest_chain_start[cur-1] - 1
            dp[i] = max(dp[i], cur + dp[prev_idx])

            # now update chain for cur
            largest_chain_start[cur] = largest_chain_start[cur-1]

        else:
            # now we cannot extend any previous chain unless cur == 1
            if cur == 1:
                dp[i] = max(dp[i], 1+dp[i-1])

        # what if it's 1 ? then we are at start of a fresh chain
        if cur == 1:
            largest_chain_start[cur] = i


    # print(dp, ' dp')
    print(dp[n])


tc = int(input())
for _ in range(tc):
    solve()

\(O(N)\) time, \(O(N)\) memory. From \(O(N^2 \log N)\), and the BIT disappeared entirely.

What I’d take away

The speedup didn’t come from a better data structure. It came from noticing that the quantity I was struggling to compute — \(f\) of an arbitrary segment — was never something I needed. Once the “maximum over splits” was re-read as “maximum over selections of chains,” the segment queries evaporated, and the two greedy facts (\(f\) increases by at most 1; shortest closing chain dominates) reduced \(N\) candidate transitions per index to one.

Worth keeping as a general prompt: when a DP transition needs an expensive query, check whether the thing being queried is actually forced. Sometimes the structure you’re paying a log factor to maintain is a structure the optimal solution never uses.