kubernetes/vendor/golang.org/x/exp/slices/zsortordered.go

// insertionSortOrdered sorts data[a:b] using insertion sort.
func insertionSortOrdered[E constraints.Ordered](data []E, a, b int) { … }

// siftDownOrdered implements the heap property on data[lo:hi].
// first is an offset into the array where the root of the heap lies.
func siftDownOrdered[E constraints.Ordered](data []E, lo, hi, first int) { … }

func heapSortOrdered[E constraints.Ordered](data []E, a, b int) { … }

// pdqsortOrdered sorts data[a:b].
// The algorithm based on pattern-defeating quicksort(pdqsort), but without the optimizations from BlockQuicksort.
// pdqsort paper: https://arxiv.org/pdf/2106.05123.pdf
// C++ implementation: https://github.com/orlp/pdqsort
// Rust implementation: https://docs.rs/pdqsort/latest/pdqsort/
// limit is the number of allowed bad (very unbalanced) pivots before falling back to heapsort.
func pdqsortOrdered[E constraints.Ordered](data []E, a, b, limit int) { … }

// partitionOrdered does one quicksort partition.
// Let p = data[pivot]
// Moves elements in data[a:b] around, so that data[i]<p and data[j]>=p for i<newpivot and j>newpivot.
// On return, data[newpivot] = p
func partitionOrdered[E constraints.Ordered](data []E, a, b, pivot int) (newpivot int, alreadyPartitioned bool) { … }

// partitionEqualOrdered partitions data[a:b] into elements equal to data[pivot] followed by elements greater than data[pivot].
// It assumed that data[a:b] does not contain elements smaller than the data[pivot].
func partitionEqualOrdered[E constraints.Ordered](data []E, a, b, pivot int) (newpivot int) { … }

// partialInsertionSortOrdered partially sorts a slice, returns true if the slice is sorted at the end.
func partialInsertionSortOrdered[E constraints.Ordered](data []E, a, b int) bool { … }

// breakPatternsOrdered scatters some elements around in an attempt to break some patterns
// that might cause imbalanced partitions in quicksort.
func breakPatternsOrdered[E constraints.Ordered](data []E, a, b int) { … }

// choosePivotOrdered chooses a pivot in data[a:b].
//
// [0,8): chooses a static pivot.
// [8,shortestNinther): uses the simple median-of-three method.
// [shortestNinther,∞): uses the Tukey ninther method.
func choosePivotOrdered[E constraints.Ordered](data []E, a, b int) (pivot int, hint sortedHint) { … }

// order2Ordered returns x,y where data[x] <= data[y], where x,y=a,b or x,y=b,a.
func order2Ordered[E constraints.Ordered](data []E, a, b int, swaps *int) (int, int) { … }

// medianOrdered returns x where data[x] is the median of data[a],data[b],data[c], where x is a, b, or c.
func medianOrdered[E constraints.Ordered](data []E, a, b, c int, swaps *int) int { … }

// medianAdjacentOrdered finds the median of data[a - 1], data[a], data[a + 1] and stores the index into a.
func medianAdjacentOrdered[E constraints.Ordered](data []E, a int, swaps *int) int { … }

func reverseRangeOrdered[E constraints.Ordered](data []E, a, b int) { … }

func swapRangeOrdered[E constraints.Ordered](data []E, a, b, n int) { … }

func stableOrdered[E constraints.Ordered](data []E, n int) { … }

// symMergeOrdered merges the two sorted subsequences data[a:m] and data[m:b] using
// the SymMerge algorithm from Pok-Son Kim and Arne Kutzner, "Stable Minimum
// Storage Merging by Symmetric Comparisons", in Susanne Albers and Tomasz
// Radzik, editors, Algorithms - ESA 2004, volume 3221 of Lecture Notes in
// Computer Science, pages 714-723. Springer, 2004.
//
// Let M = m-a and N = b-n. Wolog M < N.
// The recursion depth is bound by ceil(log(N+M)).
// The algorithm needs O(M*log(N/M + 1)) calls to data.Less.
// The algorithm needs O((M+N)*log(M)) calls to data.Swap.
//
// The paper gives O((M+N)*log(M)) as the number of assignments assuming a
// rotation algorithm which uses O(M+N+gcd(M+N)) assignments. The argumentation
// in the paper carries through for Swap operations, especially as the block
// swapping rotate uses only O(M+N) Swaps.
//
// symMerge assumes non-degenerate arguments: a < m && m < b.
// Having the caller check this condition eliminates many leaf recursion calls,
// which improves performance.
func symMergeOrdered[E constraints.Ordered](data []E, a, m, b int) { … }

// rotateOrdered rotates two consecutive blocks u = data[a:m] and v = data[m:b] in data:
// Data of the form 'x u v y' is changed to 'x v u y'.
// rotate performs at most b-a many calls to data.Swap,
// and it assumes non-degenerate arguments: a < m && m < b.
func rotateOrdered[E constraints.Ordered](data []E, a, m, b int) { … }