Space complexity
Amount of memory space that an algorithm uses as a function of the input's size
The space complexity of an algorithm or a data structure is the amount of memory space required to solve an instance of the computational problem as a function of characteristics of the input. It is the memory required by an algorithm until it executes completely.
Nº Q2098905 ★
Common · Knowledge
Space complexity
Amount of memory space that an algorithm uses as a function of the input's size
The space complexity of an algorithm or a data structure is the amount of memory space required to solve an instance of the computational problem as a function of characteristics of the input. It is the memory required by an algorithm until it executes completely.
Last price
—
Floor price
—
7-day median
—
30-day sales
0
30-day range
—
In circulation
0
Price history
median
low – high
sales
No sales in this period
Show table
| Date | median | Low | High | sales |
|---|
Sales history
- Last sale
- —
- 30-day average
- —
- 30-day low
- —
- 30-day high
- —
- Sales 7d
- 0
- Sales 30d
- 0
No sales yet.
Anonymous sales: no buyer or seller shown. Figures count player-to-player sales only.
From Wikipedia
The space complexity of an algorithm or a data structure is the amount of memory space required to solve an instance of the computational problem as a function of characteristics of the input. It is the memory required by an algorithm until it executes completely. This includes the memory space used by its inputs, called input space, and any other (auxiliary) memory it uses during execution, which is called auxiliary space. Similar to time complexity, space complexity is often expressed asymptotically in big O notation, such as O ( n ) , {\displaystyle O(n),} O ( n log n ) , {\displaystyle O(n\log n),} O ( n α ) , {\displaystyle O(n^{\alpha }),} O ( 2 n ) , {\displaystyle O(2^{n}),} etc., where n is a characteristic of the input influencing space complexity.
Text: Wikipédia, CC BY-SA 4.0. ·
Related cards
Computational complexity
Measure of the amount of resources needed to run an algorithm or solve a computational problem
Nº Q5157286 ★★★
L (complexity)
Complexity class (logarithmic space)
Nº Q1192782 ★★
Hilbert space
Inner product space that is metrically complete; a Banach space whose norm induces an inner product (follows the parallelogram identity)
Nº Q190056 ★★★★
Kolmogorov complexity
Measure of algorithmic complexity
Nº Q1456811 ★★★
Master theorem (analysis of algorithms)
Method for analysis of algorithms
Nº Q922367 ★★
building complex
Set of related buildings
Nº Q1497364 ★