Recap java algorithms

Main purpose of this project is to recap and re-implement common-used and well-known algorithms in Java and their properties as well as getting used to Maven, EditorConfig and GitHub actions/ workflows. Properties of implemented algorithms may differ from properties listed below. Some algorithms are plain implementations of common-known pseudocode.

Sorting

class	name	best-case	average-case	worst-case	description	in-place	stable
insertion	InsertionSort	$\Omega(n)$	$\Theta(n^2)$	$O(n^2)$		Yes	Yes
	ShellSort1	$\Omega(n * log(n))$	$\Theta(n * log(n))$	$O(n^2)$		Yes	No
selection	SelectionSort	$\Omega(n^2)$	$\Theta(n^2)$	$O(n^2)$		Yes	No
	HeapSort	$\Omega(n * log(n))$	$\Theta(n * log(n))$	$O(n * log(n))$		Yes	No
exchange	BubbleSort	$\Omega(n)$	$\Theta(n^2)$	$O(n^2)$		Yes	Yes
	CombSort1	$\Omega(n * log(n))$	$\Theta(n^2/2^p)$	$O(n^2)$	$p$ the number of increments	Yes	No
	CocktailSort	$\Omega(n)$	$\Theta(n^2)$	$O(n^2)$		Yes	Yes
	QuickSort	$\Omega(n * log(n))$	$\Theta(n * log(n))$	$O(n^2)$		Yes	No
	GnomeSort	$\Omega(n)$	$\Theta(n^2)$	$O(n^2)$		Yes	Yes
	BogoSort	$\Omega(n)$	$\Theta(n * n!)$	$infinitly$		Yes	No
merge	MergeSort	$\Omega(n * log(n))$	$\Theta(n * log(n))$	$O(n * log(n))$		No (uses auxillary storage)	Yes
distribution	CountingSort	$\Omega(n+k)$	$\Theta(n+k)$	$O(n+k)$	$k$ the range of the input interval	No (uses auxillary storage)	Yes
	BucketSort	$\Omega(n+b)$	$\Theta(n+b)$	$O(n^2)$	$b$ the number of buckets	No (uses auxillary storage)	No
concurrent	BitonicSort2	$\Omega(log^2(n))$	$\Theta(log^2(n))$	$O(log^2(n))$		No (depends on implementation)	No
hybrid
other	PancakeSort		$\Theta(n^2)$	$O(n^2)$		Yes	No

Searching

class	name	best-case	average-case	worst-case	description
generic (Searching)	LinearSearch	$\Omega(1)$	$\Theta(n)$	$O(n)$
	BinarySearch	$\Omega(1)$	$\Theta(log(n))$	$O(log(n))$
	TernarySearch	$\Omega(1)$	$\Theta(log_3(n))$	$O(log_3(n))$
	JumpSearch	$\Omega(1)$	$\Theta(\sqrt{n})$	$O(\sqrt{n})$
	ExponentialSearch	$\Omega(1)$	$\Theta(log(n))$	$O(log(n))$
	FibonacciSearch	$\Omega(1)$	$\Theta(log(n))$	$O(log(n))$
2D (Searching2D)	SaddlebackSearch	$\Omega(1)$	$\Theta(n+m)$	$O(n+m)$	$m$ the maximum of all rows length
integer (Finding)	InterpolationSearch	$\Omega(1)$	$\Theta(log(log(n)))$3	$O(n)$
	InterpolationSequentialSearch

Footnotes

1. Strictly, it depends on gap sequence/ size. ↩ ↩²

2. Denoted is the delay within the network of $O(n * log^2(n))$ comparators. ↩

3. E.g. the elements are uniformly distributed. ↩