Output-Sensitive Complexity

Context: FIT2004_MOC · a bound that names the output size alongside the input size — the Week 1 range-reporting example that separates Linear Search from Binary Search + scan

Quick Revision

• 🎯 Objective: when an algorithm must emit $W$ items, quote $\Theta (f(N)+W)$ ➔ the search term and the reporting term are separate and neither absorbs the other.
• ⚡ Key Constraint: $W$ is a free parameter, not a constant — dropping it is the mark-loss; $O(\log N)$ for a range report is simply false.

📝 Core

1. The problem shape

• Setup ➔ sorted array of $N$ integers, bounds $X<Y$; print every element strictly between them. Let $W$ be how many are printed.
• Why $W$ must appear ➔ printing is work: $W$ outputs cost $\Theta (W)$ regardless of how cleverly they were found.
• Two free parameters ➔ $N$ and $W$ can grow independently ($0\le W\le N$), so by the free-parameter rule in Algorithmic Complexity both belong in the bound.

2. The two algorithms

• Algo 1 — linear scan ➔ walk index $0\to N-1$, test each against $(X,Y)$, print on a hit ➔ $\Theta (N)$ scanning $+\Theta (W)$ printing $=\Theta (N+W)=\Theta (N)$ (since $W\le N$).
• Algo 2 — binary search then scan ➔ Binary Search for the smallest element $>X$ ($\Theta (\log N)$), then scan forward printing until an element $\ge Y$ is reached ($\Theta (W)$) ➔ $\Theta (\log N+W)$.
• Where the win is ➔ Algo 2 never touches the $N-W$ out-of-range elements to the left of $X$; Algo 1 touches all of them.

3. Optimality and the flip point

• Lower bound ➔ any correct algorithm is $\Omega (W)$ (it must emit $W$ items) and $\Omega (\log N)$ (comparison-based location in a sorted array) ⟹ Algo 2 is optimal, its $O$ meets the problem’s $\Omega$.
• When it flips ➔ at $W=\Theta (N)$ (most of the array in range) both are $\Theta (N)$ and the binary search buys nothing; the gap is largest when $W=\Theta (1)$ — $\Theta (\log N)$ vs $\Theta (N)$.
• Generalises ➔ the same accounting governs any reporting problem (all pairs within distance $d$, all matches of a pattern); asymptotic effort is split into locate $+$ report.

⚠️ Common Mistakes

• 💡 Quoting Algo 2 as $O(\log N)$ ➔ ignores the print loop; the answer is $\Theta (\log N+W)$ and the missing term is exactly what is being tested.
• 💡 Absorbing $W$ into $N$ “because $W\le N$” ➔ legal for Algo 1 (where $\Theta (N+W)=\Theta (N)$), fatal for Algo 2 — collapsing $\Theta (\log N+W)$ to $\Theta (N)$ discards the whole point of the algorithm.
• 💡 Binary-searching for $X$ itself ➔ $X$ need not be present; search for the first element greater than $X$ (a boundary/predecessor search), or the scan starts in the wrong place.

🧠 Active Recall

Both algorithms print the same $W$ values, yet only one is called optimal. On what grounds?

• Hint: Compare each upper bound against the problem’s lower bound, not against each other.

Answer

• Short answer: The problem is $\Omega (\log N+W)$ — you must locate the boundary and you must emit $W$ items. Algo 2 achieves $O(\log N+W)$, matching it; Algo 1’s $\Theta (N)$ does not.
• Why: Optimal means $O$ meets $\Omega$ ➔ per Big-O Notation, a $\Theta$ that coincides with the problem’s intrinsic lower bound cannot be improved by any algorithm; Algo 1 wastes $\Theta (N-W)$ comparisons on elements it can prove are out of range.

Why is $W$ allowed in an asymptotic bound at all, when a capped parameter like $arr[i]<2^{32}$ is not?

• Hint: What can grow without limit as the instance grows?

Answer

• Short answer: $W$ is unbounded — it ranges over $\mathrm{0..}N$ and grows with the instance — so it is a legitimate free parameter; a capped quantity contributes $\Theta (1)$ and is discarded by $\Theta$.
• Why: Asymptotics describe growth ➔ Algorithmic Complexity’s rule is to list which parameters may grow before quoting a bound; $W$ passes that test, $2^{32}$ does not.