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PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
Differential dynamic programming (DDP) is an optimal control algorithm of the trajectory optimization class. The algorithm was introduced in 1966 by Mayne [ 1 ] and subsequently analysed in Jacobson and Mayne's eponymous book. [ 2 ]
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...
Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]
TD-Lambda is a learning algorithm invented by Richard S. Sutton based on earlier work on temporal difference learning by Arthur Samuel. [11] This algorithm was famously applied by Gerald Tesauro to create TD-Gammon, a program that learned to play the game of backgammon at the level of expert human players.
For example, LOBPCG implementations, [9] [10] follow, [8] [11] separating hard locking, i.e. a deflation by restriction, where the locked eigenvectors serve as a code input and do not change, from soft locking, where the locked vectors do not participate in the typically most expensive iterative step of computing the residuals, however, fully ...
A problem set, sometimes shortened as pset, [1] is a teaching tool used by many universities. Most courses in physics, math, engineering, chemistry, and computer science will give problem sets on a regular basis. [2] They can also appear in other subjects, such as economics.
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. [8] [9] [10] In fact, Dijkstra's explanation of the logic behind the algorithm, [11] namely Problem 2.