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Some fun in C and Python (standard algorithms and data structures)
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README.md

octo-barnacle

Some fun in C and Python (standard algorithms and data structures)

In an attempt to just practice implementing algorithms and data structure with which I am familiar, but likely haven't implemented in a sufficiently long time, I've created this repository to hold them as examples.

Planned algorithms and data structures

  • sorting
    • optimized merge sort (just use insertion towards the bottom)
    • heap sort (?maybe)
  • graphs
    • breadth first search
    • representation
    • minimum spanning trees (yay, greedy)
  • trees
    • B-tree in progress
    • red-black tree
    • AVL tree (maybe)
    • splay tree (maybe)
  • lists
    • skip lists
  • dynamic programming
    • edit distance
    • longest common sequence

Implemented

  • sorting
    • merge sort
    • insertion sort
    • counting sort
    • quicksort
  • trees
    • binary search tree
    • trie
  • graphs
    • depth first search (done with bst)
  • lists
    • linked lists
    • sorted linked list
  • tables
    • hashtable
  • dynamic programming
    • fibonacci
    • knapsack (0-1)

Notes

sorting algorithm Best Case Average Case Worst Case Notes
mergesort O(nlgn) O(nlgn) O(nlgn) It builds the new array separately using O(n) space
insertion sort O(n) O(n**2) O(n**2) If the list is sorted or mostly this is fast.
counting sort O(n+k) O(n+k) O(n+k) k is highest value possible, because it builds the counting table
quicksort O(nlgn) O(nlgn) O(n**2) You can force O(nlgn) worst case by randomizing the input. Traditionally isn't great on sorted input, however, by different partition schemes you can get it to O(n) if presorted.
data structure to build insert search delete Notes
bst O(nlgn) O(lgn) O(lgn) O(lgn) This tree can break down to O(n) search performance if the input is sorted, and O(n**2) to build it in initially
b-tree O(nlogn) O(logn) O(logn) O(logn) The base of the logarithm is the maximum children per block.
trie O(nm) O(m) O(m) O(m) m is the item length in pieces.
linked list O(n) O(1) O(n) O(1) Building the list requires simply appending or prepending items, inserting is therefore constant, however, deleting assumes you've already found it, so it's constant time.
sorted linked list O(n**2) O(n) O(n) O(1) Every node you insert might need to go to the end, there are ways to optimize against sorted input such as using a doubly-linked list and keeping track of the median value.
hashtable O(n+m) O(1) O(1) O(1) m is the size of the table. These really are amortized values because occasionally we'll need to grow or shrink the table. Also if we're using a data structure to handle collisions, there can be some extra work there but it can be kept minimal by a good hashing function.

Study

  • need to review network flow
  • need to review hardware requirements
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