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Linearithmic function

A linearithmic function has growth proportional to the product of an input size and its logarithm.

How 2 Manning books explain it

Advanced Algorithms and Data Structures

Linearithmic time is a running-time complexity proportional to the product of the input size and its logarithm.

How it is used
Analyzes the cost of constructing a balanced k-d tree from a known set of points.
How deep it goes
Derives O(n log(n)) from a divide-and-conquer recurrence and pairs it with O(n) space.
Examples
Uses median-based k-d tree construction and concrete point partitions to illustrate the recurrence.
What you can do after
Evaluate/understand when recursive tree construction has O(n log(n)) time and O(n) space.

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Grokking Data Structures

Linearithmic growth is growth proportional to the product of input size and its logarithm.

How it is used
Classifies algorithm running times and memory growth as input sizes increase.
How deep it goes
Defines O(n * log(n)) and compares its acceleration with logarithmic, linear, quadratic, and exponential growth.
Examples
Uses mergesort and heapsort as linearithmic examples, contrasting them with quadratic selection sort.
What you can do after
Evaluate algorithm choices by comparing linearithmic growth with slower and faster asymptotic classes.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.