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  2. Dynamic time warping - Wikipedia

    en.wikipedia.org/wiki/Dynamic_time_warping

    In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an ...

  3. Iterative closest point - Wikipedia

    en.wikipedia.org/wiki/Iterative_Closest_Point

    Iterative closest point. Iterative closest point (ICP) [1][2][3][4] is an algorithm employed to minimize the difference between two clouds of points. ICP is often used to reconstruct 2D or 3D surfaces from different scans, to localize robots and achieve optimal path planning (especially when wheel odometry is unreliable due to slippery terrain ...

  4. 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.

  5. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    From a dynamic programming point of view, Dijkstra's algorithm for the shortest path problem is a successive approximation scheme that solves the dynamic programming functional equation for the shortest path problem by the Reaching method. [7] [8] [9] In fact, Dijkstra's explanation of the logic behind the algorithm, [10] namely Problem 2.

  6. Algorithm - Wikipedia

    en.wikipedia.org/wiki/Algorithm

    In mathematics and computer science, an algorithm (/ ˈælɡərɪðəm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to perform a computation. [ 1 ] Algorithms are used as specifications for performing calculations and data processing.

  7. Proximal policy optimization - Wikipedia

    en.wikipedia.org/wiki/Proximal_Policy_Optimization

    v. t. e. Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, [1] and had become the default reinforcement learning algorithm at the US artificial intelligence company OpenAI. [2]

  8. Simplex algorithm - Wikipedia

    en.wikipedia.org/wiki/Simplex_algorithm

    Simplex algorithm. In mathematical optimization, Dantzig 's simplex algorithm (or simplex method) is a popular algorithm for linear programming. [1] The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. [2] Simplices are not actually used in the method, but one interpretation of it is that it ...

  9. Longest-processing-time-first scheduling - Wikipedia

    en.wikipedia.org/wiki/Longest-processing-time...

    Longest-processing-time-first scheduling. Longest-processing-time-first (LPT) is a greedy algorithm for job scheduling. The input to the algorithm is a set of jobs, each of which has a specific processing-time. There is also a number m specifying the number of machines that can process the jobs. The LPT algorithm works as follows: