.Net: Sorting in data structure

Comparison of algorithms

In this table, n is the number of records to be sorted. The columns "Average" and "Worst" give the time complexity in each case, under the assumption that the length of each key is constant, and that therefore all comparisons, swaps, and other needed operations can proceed in constant time. "Memory" denotes the amount of auxiliary storage needed beyond that used by the list itself, under the same assumption. These are all comparison sorts. The run time and the memory of algorithms could be measured using various notations like theta, omega, Big-O, small-o, etc. The memory and the run times below are applicable for all the 5 notations.

Comparison sorts
Name	Best	Average	Worst	Memory	Stable	Method	Other notes
Quicksort	$\mathcal{} n \log n$	$\mathcal{} n \log n$	$\mathcal{} n^2$	$\mathcal{} \log n$	Depends	Partitioning	Quicksort is usually done in place with O(log(n)) stack space.^{[citation needed]} Most implementations are unstable, as stable in-place partitioning is more complex. Naïve variants use an O(n) space array to store the partition.^{[citation needed]}
Merge sort	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	Depends; worst case is $\mathcal{} n$	Yes	Merging	Used to sort this table in Firefox [2].
In-place Merge sort	$\mathcal{} -$	$\mathcal{} -$	$\mathcal{} {n \left( \log n \right)^2}$	$\mathcal{} {1}$	Yes	Merging	Implemented in Standard Template Library (STL): [3]; can be implemented as a stable sort based on stable in-place merging: [4]
Heapsort	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} {1}$	No	Selection
Insertion sort	$\mathcal{} n$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	Yes	Insertion	O(n + d), where d is the number of inversions
Introsort	$\mathcal{} n \log n$	$\mathcal{} n \log n$	$\mathcal{} n \log n$	$\mathcal{} \log n$	No	Partitioning & Selection	Used in SGI STL implementations
Selection sort	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	No	Selection	Stable with O(n) extra space, for example using lists [5]. Used to sort this table in Safari or other Webkit web browser [6].
Timsort	$\mathcal{} {n}$	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} n$	Yes	Insertion & Merging	$\mathcal{} {n}$ comparisons when the data is already sorted or reverse sorted.
Shell sort	$\mathcal{} n$	$\mathcal{} n (\log n)^2$ or $\mathcal{} n^{3/2}$	Depends on gap sequence; best known is $\mathcal{} n (\log n)^2$	$\mathcal{} 1$	No	Insertion
Bubble sort	$\mathcal{} n$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	Yes	Exchanging	Tiny code size
Binary tree sort	$\mathcal{} n$	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} n$	Yes	Insertion	When using a self-balancing binary search tree
Cycle sort	—	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	No	Insertion	In-place with theoretically optimal number of writes
Library sort	—	$\mathcal{} {n \log n}$	$\mathcal{} n^2$	$\mathcal{} n$	Yes	Insertion
Patience sorting	—	—	$\mathcal{} n \log n$	$\mathcal{} n$	No	Insertion & Selection	Finds all the longest increasing subsequences within O(n log n)
Smoothsort	$\mathcal{} {n}$	$\mathcal{} {n \log n}$	$\mathcal{} {n \log n}$	$\mathcal{} {1}$	No	Selection	An adaptive sort - $\mathcal{} {n}$ comparisons when the data is already sorted, and 0 swaps.
Strand sort	$\mathcal{} n$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} n$	Yes	Selection
Tournament sort	—	$\mathcal{} n \log n$	$\mathcal{} n \log n$			Selection
Cocktail sort	$\mathcal{} n$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	Yes	Exchanging
Comb sort	$\mathcal{} n$	$\mathcal{} n \log n$	$\mathcal{} n^2$	$\mathcal{} {1}$	No	Exchanging	Small code size
Gnome sort	$\mathcal{} n$	$\mathcal{} n^2$	$\mathcal{} n^2$	$\mathcal{} {1}$	Yes	Exchanging	Tiny code size
Bogosort	$\mathcal{} n$	$\mathcal{} n \cdot n!$	$\mathcal{} {n \cdot n! \to \infty}$	$\mathcal{} {1}$	No	Luck	Randomly permute the array and check if sorted.
Slowsort	$\Omega\left(n^{ \frac{\log_2(n)}{(2+\epsilon)}}\right)$	—	—	—	No	Selection	Remarkably inefficient sorting algorithm [7]

The following table describes integer sorting algorithms and other sorting algorithms that are not comparison sorts. As such, they are not limited by a $\Omega\left( {n \log n} \right)$ lower bound. Complexities below are in terms of n, the number of items to be sorted, k, the size of each key, and d, the digit size used by the implementation. Many of them are based on the assumption that the key size is large enough that all entries have unique key values, and hence that n << 2^k, where << means "much less than."

Non-comparison sorts
Name	Best	Average	Worst	Memory	Stable	n << 2^k	Notes
Pigeonhole sort	—	$\;n + 2^k$	$\;n + 2^k$	$\;2^k$	Yes	Yes
Bucket sort (uniform keys)	—	$\;n+k$	$\;n^2 \cdot k$	$\;n \cdot k$	Yes	No	Assumes uniform distribution of elements from the domain in the array.^[2]
Bucket sort (integer keys)	—	$\;n+r$	$\;n+r$	$\;n+r$	Yes	Yes	r is the range of numbers to be sorted. If r = $\mathcal{O}\left( {n} \right)$ then Avg RT = $\mathcal{O}\left( {n} \right)$ ^[3]
Counting sort	—	$\;n+r$	$\;n+r$	$\;n+r$	Yes	Yes	r is the range of numbers to be sorted. If r = $\mathcal{O}\left( {n} \right)$ then Avg RT = $\mathcal{O}\left( {n} \right)$ ^[2]
LSD Radix Sort	—	$\;n \cdot \frac{k}{d}$	$\;n \cdot \frac{k}{d}$	$\mathcal{} n$	Yes	No	^[3]^[2]
MSD Radix Sort	—	$\;n \cdot \frac{k}{d}$	$\;n \cdot \frac{k}{d}$	$\mathcal{} n + \frac{k}{d} \cdot 2^d$	Yes	No	Stable version uses an external array of size n to hold all of the bins
MSD Radix Sort	—	$\;n \cdot \frac{k}{d}$	$\;n \cdot \frac{k}{d}$	$\frac{k}{d} \cdot 2^d$	No	No	In-Place. k / d recursion levels, 2^d for count array
Spreadsort	—	$\;n \cdot \frac{k}{d}$	$\;n \cdot \left( {\frac{k}{s} + d} \right)$	$\;\frac{k}{d} \cdot 2^d$	No	No	Asymptotics are based on the assumption that n << 2^k, but the algorithm does not require this.

The following table describes some sorting algorithms that are impractical for real-life use due to extremely poor performance or a requirement for specialized hardware.

Name	Best	Average	Worst	Memory	Stable	Comparison	Other notes
Bead sort	—	N/A	N/A	—	N/A	No	Requires specialized hardware
Simple pancake sort	—	$\mathcal{} n$	$\mathcal{} n$	$\mathcal{} {\log n}$	No	Yes	Count is number of flips.
Spaghetti (Poll) sort	$\mathcal{} n$	$\mathcal{} n$	$\mathcal{} n$	$\mathcal{} n^2$	Yes	Polling	This A linear-time, analog algorithm for sorting a sequence of items, requiring O(n) stack space, and the sort is stable. This requires $n$ parallel processors. Spaghetti sort#Analysis
Sorting networks	—	$\mathcal{} {\log n}$	$\mathcal{} {\log n}$	$\mathcal{} {n \cdot \log (n)}$	Yes	No	Requires a custom circuit of size $\mathcal{O}\left( n \cdot \log (n) \right)$

Additionally, theoretical computer scientists have detailed other sorting algorithms that provide better than $\mathcal{O}\left( {n \log n} \right)$ time complexity with additional constraints, including:

Han's algorithm, a deterministic algorithm for sorting keys from a domain of finite size, taking $\mathcal{O}\left( {n \log \log n} \right)$ time and $\mathcal{O}\left( {n} \right)$ space.^[4]
Thorup's algorithm, a randomized algorithm for sorting keys from a domain of finite size, taking $\mathcal{O}\left( {n \log \log n} \right)$ time and $\mathcal{O}\left( {n} \right)$ space.^[5]
An integer sorting algorithm taking $\mathcal{O}\left( {n \sqrt{\log \log n}} \right)$ expected time and $\mathcal{O}\left( {n} \right)$ space

.Net

Friday 30 March 2012

Sorting in data structure

Comparison of algorithms

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