Linked from

The 29 pages that link to Big O notation, each with the reason it gives.

• AlgorithmRelated: It summarizes how an algorithm’s resource use scales with input size.

• Asymptotic analysisRelated: It gives a standard way to bound growth while ignoring constant factors.

• Computational complexity theoryRelated: It is commonly used to state upper bounds on algorithms' resource requirements.

• Randomized algorithmRelated: Running-time comparisons often express randomized algorithms' expected or worst-case costs asymptotically.

• Time complexityRelated: It expresses an upper bound on an algorithm’s running-time growth.

• QuantifierRelated: Its formal definition quantifies over constants and input-size thresholds.

• Space complexityRelated: It commonly states upper bounds on an algorithm’s space requirements.

• Fast Fourier transformRelated: It compares the FFT's operation growth with direct DFT evaluation.

• Merge sortRelated: It expresses merge sort’s O(n log n) worst-case time growth.

• Analysis of algorithmsRelated: It summarizes an algorithm’s resource growth while suppressing constant factors and smaller terms.

• Asymptotic expansionRelated: It expresses the remainder bound after truncating an expansion.

• Polynomial timeRelated: It expresses polynomial running-time upper bounds while ignoring constant factors and lower-order terms.

• Taylor polynomialRelated: It summarizes how the error shrinks as the input approaches the expansion point.

• Binary search treeRelated: It expresses how tree height determines search and update costs.

• Sorting algorithmNarrower topic: It expresses how sorting time or memory scales as input size increases.

• Upper boundRelated: Asymptotic analysis uses upper bounds to compare algorithmic growth.

• Dynamic arrayRelated: It expresses the growth rates of indexing, appending, and inserting operations.

• In-place algorithmNarrower topic: In-place claims commonly bound extra space asymptotically rather than count every temporary variable.

• Master theoremRelated: The theorem's conclusions are commonly stated as Big O bounds.

• Red–black treeRelated: It expresses the logarithmic time bounds guaranteed by red–black trees.

• The Art of Computer ProgrammingRelated: The books use it to express resource bounds compactly.

• Savitch's theoremRelated: O(s(n)²) states the scale of the deterministic simulation's space use.

• Akra–Bazzi methodNarrower topic: The method’s conclusions are typically stated as asymptotic bounds.

• Riemann–von Mangoldt formulaRelated: The formula’s error estimate is commonly stated as \(O(\log T)\).

• Theory of computationRelated: It expresses how algorithmic resource use scales as input size increases.

• Blum axiomsRelated: Blum measures can be compared asymptotically to state resource bounds and speedups.

• Carlson's theoremRelated: Growth hypotheses are commonly stated as asymptotic bounds.

• Erdős–Pósa theoremRelated: The covering bound grows on the order of k log k, not merely k.

• Linear speedup theoremCompared with: Big-O deliberately erases the exact constant that linear speedup can reduce.