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  2. Subset sum problem - Wikipedia

    en.wikipedia.org/wiki/Subset_sum_problem

    The subset sum problem (SSP) is a decision problem in computer science.In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely . [1]

  3. Maximum subarray problem - Wikipedia

    en.wikipedia.org/wiki/Maximum_subarray_problem

    Maximum subarray problems arise in many fields, such as genomic sequence analysis and computer vision.. Genomic sequence analysis employs maximum subarray algorithms to identify important biological segments of protein sequences that have unusual properties, by assigning scores to points within the sequence that are positive when a motif to be recognized is present, and negative when it is not ...

  4. Subadditivity - Wikipedia

    en.wikipedia.org/wiki/Subadditivity

    The expected length of the longest common subsequence is a super-additive function of , and thus there exists a number , such that the expected length grows as . By checking the case with n = 1 {\displaystyle n=1} , we easily have 1 k < γ k ≤ 1 {\displaystyle {\frac {1}{k}}<\gamma _{k}\leq 1} .

  5. Subsequence - Wikipedia

    en.wikipedia.org/wiki/Subsequence

    The longest common subsequence of sequences 1 and 2 is: LCS (SEQ 1,SEQ 2) = CGTTCGGCTATGCTTCTACTTATTCTA. This can be illustrated by highlighting the 27 elements of the longest common subsequence into the initial sequences: SEQ 1 = A CG G T G TCG T GCTATGCT GA T G CT G ACTTAT A T G CTA SEQ 2 = CGTTCGGCTAT C G TA C G TTCTA TT CT A T G ATT T CTA A

  6. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    The table C shown below, which is generated by the function LCSLength, shows the lengths of the longest common subsequences between prefixes of and . The i {\displaystyle i} th row and j {\displaystyle j} th column shows the length of the LCS between X 1.. i {\displaystyle X_{1..i}} and Y 1.. j {\displaystyle Y_{1..j}} .

  7. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    var m := map(0 → 0, 1 → 1) function fib(n) if key n is not in map m m[n] := fib(n − 1) + fib(n − 2) return m[n] This technique of saving values that have already been calculated is called memoization ; this is the top-down approach, since we first break the problem into subproblems and then calculate and store values.

  8. Subnet (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Subnet_(mathematics)

    The key difference is that subnets can use the same point in the net multiple times and the indexing set of the subnet can have much larger cardinality. Using the more general definition where we do not require monotonicity, a sequence is a subnet of a given sequence, if and only if it can be obtained from some subsequence by repeating its ...

  9. Algorithmically random sequence - Wikipedia

    en.wikipedia.org/wiki/Algorithmically_random...

    To pick out a subsequence, first pick a binary function , such that given any binary string :, it outputs either 0 or 1. If it outputs 1, then we add + to the subsequence, else we continue. In this definition, some admissible rules might abstain forever on some sequences, and thus fail to pick out an infinite subsequence.