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  2. Subset sum problem - Wikipedia

    en.wikipedia.org/wiki/Subset_sum_problem

    Thus, each list can be generated in sorted form in time (/). Given the two sorted lists, the algorithm can check if an element of the first array and an element of the second array sum up to T in time (/). To do that, the algorithm passes through the first array in decreasing order (starting at the largest element) and the second array in ...

  3. Maximum subarray problem - Wikipedia

    en.wikipedia.org/wiki/Maximum_subarray_problem

    Each possible contiguous sub-array is represented by a point on a colored line. That point's y-coordinate represents the sum of the sample. Its x-coordinate represents the end of the sample, and the leftmost point on that colored line represents the start of the sample. In this case, the array from which samples are taken is [2, 3, -1, -20, 5, 10].

  4. Partition problem - Wikipedia

    en.wikipedia.org/wiki/Partition_problem

    In the subset sum problem, the goal is to find a subset of S whose sum is a certain target number T given as input (the partition problem is the special case in which T is half the sum of S). In multiway number partitioning , there is an integer parameter k , and the goal is to decide whether S can be partitioned into k subsets of equal sum ...

  5. k-way merge algorithm - Wikipedia

    en.wikipedia.org/wiki/K-way_merge_algorithm

    The k-way merge problem consists of merging k sorted arrays to produce a single sorted array with the same elements. Denote by n the total number of elements. n is equal to the size of the output array and the sum of the sizes of the k input arrays. For simplicity, we assume that none of the input arrays is empty.

  6. Skip list - Wikipedia

    en.wikipedia.org/wiki/Skip_list

    As described above, a skip list is capable of fast (⁡) insertion and removal of values from a sorted sequence, but it has only slow () lookups of values at a given position in the sequence (i.e. return the 500th value); however, with a minor modification the speed of random access indexed lookups can be improved to (⁡).

  7. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    This avoids recomputation; all the values needed for array q[i, j] are computed ahead of time only once. Precomputed values for (i,j) are simply looked up whenever needed. We also need to know what the actual shortest path is. To do this, we use another array p[i, j]; a predecessor array. This array records the path to any square s.

  8. Knapsack problem - Wikipedia

    en.wikipedia.org/wiki/Knapsack_problem

    The most common problem being solved is the 0-1 knapsack problem, which restricts the number of copies of each kind of item to zero or one. Given a set of items numbered from 1 up to , each with a weight and a value , along with a maximum weight capacity ,

  9. Longest increasing subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_increasing_subsequence

    Patience sortingSorting algorithm − an efficient technique for finding the length of the longest increasing subsequence Plactic monoid – monoid of positive integers modulo Knuth equivalence Pages displaying wikidata descriptions as a fallback − an algebraic system defined by transformations that preserve the length of the longest ...