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  2. Dynamic time warping - Wikipedia

    en.wikipedia.org/wiki/Dynamic_time_warping

    DP matching is a pattern-matching algorithm based on dynamic programming (DP), which uses a time-normalization effect, where the fluctuations in the time axis are modeled using a non-linear time-warping function. Considering any two speech patterns, we can get rid of their timing differences by warping the time axis of one so that the maximal ...

  3. Viterbi algorithm - Wikipedia

    en.wikipedia.org/wiki/Viterbi_algorithm

    The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path —that results in a sequence of observed events. This is done especially in the context of Markov information sources and hidden Markov models (HMM).

  4. Tomasulo's algorithm - Wikipedia

    en.wikipedia.org/wiki/Tomasulo's_algorithm

    Appearance. Tomasulo's algorithm is a computer architecture hardware algorithm for dynamic scheduling of instructions that allows out-of-order execution and enables more efficient use of multiple execution units. It was developed by Robert Tomasulo at IBM in 1967 and was first implemented in the IBM System/360 Model 91 ’s floating point unit.

  5. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    The Manhattan metric corresponds to a machine that adjusts first one coordinate, and then the other, so the time to move to a new point is the sum of both movements. The maximum metric corresponds to a machine that adjusts both coordinates simultaneously, so the time to move to a new point is the slower of the two movements.

  6. Flow-shop scheduling - Wikipedia

    en.wikipedia.org/wiki/Flow-shop_scheduling

    Flow Shop Ordonnancement. Flow-shop scheduling is an optimization problem in computer science and operations research.It is a variant of optimal job scheduling.In a general job-scheduling problem, we are given n jobs J 1, J 2, ..., J n of varying processing times, which need to be scheduled on m machines with varying processing power, while trying to minimize the makespan – the total length ...

  7. k-nearest neighbors algorithm - Wikipedia

    en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

    k. -nearest neighbors algorithm. In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] It is used for classification and regression. In both cases, the input consists of the k closest training ...

  8. Golden-section search - Wikipedia

    en.wikipedia.org/wiki/Golden-section_search

    The golden-section search is a technique for finding an extremum (minimum or maximum) of a function inside a specified interval. For a strictly unimodal function with an extremum inside the interval, it will find that extremum, while for an interval containing multiple extrema (possibly including the interval boundaries), it will converge to one of them.

  9. Paxos (computer science) - Wikipedia

    en.wikipedia.org/wiki/Paxos_(computer_science)

    Paxos (computer science) Paxos is a family of protocols for solving consensus in a network of unreliable or fallible processors. Consensus is the process of agreeing on one result among a group of participants. This problem becomes difficult when the participants or their communications may experience failures.