{"id":1614,"date":"2011-07-02T10:23:46","date_gmt":"2011-07-02T14:23:46","guid":{"rendered":"http:\/\/www.acarlstein.com\/?p=1614"},"modified":"2017-08-29T09:55:10","modified_gmt":"2017-08-29T13:55:10","slug":"list-of-common-algorithms-part-1","status":"publish","type":"post","link":"http:\/\/blog.acarlstein.com\/?p=1614","title":{"rendered":"List of Common Algorithms – Part 1"},"content":{"rendered":"

NOTIFICATION: These examples are provided for educational purposes. Using this code is under your own responsibility and risk. The code and information is given \u2018as is\u2019. I do not take responsibilities of how they are used.<\/p>\n

List of Common Algorithms – Part 2 ><\/a><\/strong><\/p>\n

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  1. Insertion sort:<\/strong> Efficient for sorting small number of elements. Remove one element at a time and insert it back into the correct position.<\/li>\n
  2. Merge sort:<\/strong> Divide-and-conquer sorting algorithm. Divide the problem into small sub-problems. Solve the sub-problems recursively. Combine the solution of the sub-problems into the solution for the original problem.<\/li>\n
  3. Bubble sort:<\/strong> Repeatedly swap adjacent elements in the right order.<\/li>\n
  4. Horner’s polynomial rule:<\/strong> A rule which the number of necessary multiplications and results in less numerical instability due to potential subtraction of one large number from another are reduced.<\/li>\n
  5. Permute-By-Sorting:<\/strong> Randomized algorithm in which the input is randomized by permuting the input array. This algorithm belong to the uniform random permutation algorithms.<\/li>\n
  6. Randomize-In-Place:<\/strong> This algorithm belong to the uniform random permutation algorithm.<\/li>\n
  7. Heapsort:<\/strong> Array in which elements can be viewed as a complete binary tree. Each node satisfy a heap property.<\/li>\n
  8. Max-Heapify:<\/strong> subroutine for manipulating max-heaps. Normally used for Heapsort.<\/li>\n
  9. Priority Queues:<\/strong> A set of data sequence in which each set of elements is associated with a value called key.<\/li>\n
  10. Quicksort:<\/strong> Divide-and-conquer sorting algorithm. Uses a partition algorithm in order to divided the array.<\/li>\n
  11. Random Sampling Quicksort:<\/strong> This version of the Quicksort algorithm performs a randomized-partition.<\/li>\n
  12. Hoare partition:<\/strong> This version of the Quicksort algorithm performs a partition with the use of two indices in which each begins at the extreme ends of the input array.<\/li>\n
  13. Stooge sort:<\/strong> Recursively sorting algorithm. Slower than Bubble sort.<\/li>\n
  14. Stack Depth:<\/strong> Also known as tail recursion. After the partition, first sub-array to be recursively shorted will be the left sub-array, then the right sub-array.<\/li>\n
  15. Median-of-3 partition:<\/strong> An improved version of the Randomized-Quicksort.<\/li>\n
  16. Fuzzy sorting of intervals:<\/strong> Produce a permutation of the intervals such that the existent must satisfy a rule.<\/li>\n
  17. Counting sort:<\/strong> For each input element, determine the number of elements less than the input. Assumes that the input consists of integers in a small range.<\/li>\n
  18. Radix sort:<\/strong> Used for card-sorting. All cards are organized into 80 columns.\u00a0 In each column a card is punched into one of twelve places. Then a sort is perform on a list of seven 3-digit numbers.<\/li>\n
  19. Bucket sort:<\/strong> Similar to counting sort however it assumes that the input is generated by a random process in which the numbers are distributed uniformly and independently over an interval [0,1).<\/li>\n
  20. Randomized-Select:<\/strong> This algorithm only works on one side of the partition instead of both as in Quicksort. This algorithm uses a Randomized-Partition.<\/li>\n
  21. Stacks:<\/strong> In a stack, the stack can implement a last-in, first-out (LIFO) policy or first-in, first-out (FIFO) policy.<\/li>\n
  22. Queue:<\/strong> An stack with FIFO property which have a head and a tail.<\/li>\n
  23. Enqueue:<\/strong> Insert operation on a queue.<\/li>\n
  24. Dequeue:<\/strong> Delete operation on a queue.<\/li>\n
  25. Linked-list:<\/strong> data structure in which each node is arranged in a linear order.<\/li>\n
  26. Binary Tree:<\/strong> A parent node which have a left and right child node.<\/li>\n
  27. Hash Tables:<\/strong> Generalization of a simpler notion of an ordinary array. It performs a direct addressing, applicable when allocating an array that has one position for every possible key is affordable.<\/li>\n
  28. Direct-Address Tables:<\/strong> A dynamic set is represented by an array or direct-address table in which each slot (or position) correspond to a key.<\/li>\n
  29. Open Addressing:<\/strong> Each table entry contain either an element of the dynamic set or none; therefore, we can say that all elements are being store in the hast table itself.<\/li>\n
  30. Double Hashing:<\/strong> A method of open addressing in which permutations produced have randomly even permutations.<\/li>\n
  31. Red-Black Trees:<\/strong> Binary tree in which each node have an extra bit of storage which is use to indicate the colour of the node: red or black.<\/li>\n
  32. Matrix-chain multiplication:<\/strong> Compute the product of a given sequence (chain) of n number of matrices.<\/li>\n
  33. Length of Less Common Denominator (LCS):<\/strong> Compute two sequences as input.<\/li>\n
  34. Bitonic Euclidean Travelling-Salesman Problem:<\/strong> Also known as TSP. Algorithm that help to simplify the problem of determining the shortest closed tour that connects a set of given points.<\/li>\n
  35. Viterbi Algorithm:<\/strong> Algorithm used for speech recognition. It help to solve a labelled graph made of\u00a0 a person speaking a restricted language.<\/li>\n
  36. Greedy Algorithm:<\/strong> It is an algorithm in which the best looking choice is made at the moment.<\/li>\n
  37. Fractional Knapsack Problem:<\/strong> Greedy algorithm for a problem related with items size, their price and storage.<\/li>\n
  38. Huffman Algorithm:<\/strong> Algorithm use for compressing data in an effective way.<\/li>\n
  39. B-Trees:<\/strong> Balanced search trees.<\/li>\n
  40. Binomial trees:<\/strong> ordered tree defined recursively.<\/li>\n
  41. Binomial heaps:<\/strong> Set of binomial trees which have binomial heap properties.<\/li>\n
  42. Fibonacci heaps:<\/strong> Collection of min-heap-ordered trees.<\/li>\n
  43. Unordered binomial tree:<\/strong> Binomial tree with a single node, and an unordered binomial tree consisted of two unordered binomial trees.<\/li>\n
  44. Disjoint-set operations:<\/strong> Maintenance of\u00a0 a collection of disjoint dynamic sets.<\/li>\n
  45. Disjoint-set forests:<\/strong> Set of rooted trees which in each of their nodes contain one member and each of these trees represent one set.<\/li>\n
  46. Union by rank:<\/strong> Linked-list representation of the weighted-union heuristic.<\/li>\n
  47. Path compression:<\/strong> simple and effective heuristic without changing any ranks.<\/li>\n
  48. Union by rank with path compression:<\/strong> Combination of union-by-rank and path-compression heuristic.<\/li>\n
  49. Off-line minimum problem:<\/strong> Maintenance of a dynamic set of elements from an specific domain.<\/li>\n
  50. Adjacency-list representation:<\/strong> representation of a graph using adjacent list for each vertex.<\/li>\n<\/ol>\n

    List of Common Algorithms – Part 2 ><\/a>
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