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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] The algorithm uses locally-quadratic models of the dynamics and cost functions, and displays quadratic convergence ...
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 ...
This problem can be seen as a generalization of the linear assignment problem. [2] In words, the problem can be described as follows: An instance of the problem has a number of agents (i.e., cardinality parameter) and a number of job characteristics (i.e., dimensionality parameter) such as task, machine, time interval, etc. For example, an ...
An early example of answer set programming was the planning method proposed in 1997 by Dimopoulos, Nebel and Köhler. [3] [4] Their approach is based on the relationship between plans and stable models. [5] In 1998 Soininen and Niemelä [6] applied what is now known as answer set programming to the problem of product configuration. [4]
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
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.
For example, if we already know the values of F 41 and F 40, we can directly calculate the value of F 42. Some programming languages can automatically memoize the result of a function call with a particular set of arguments, in order to speed up call-by-name evaluation (this mechanism is referred to as call-by-need).
As in the decision case, a problem in the #CSP is defined by a set of relations. Each problem takes a Boolean formula as input and the task is to compute the number of satisfying assignments. This can be further generalized by using larger domain sizes and attaching a weight to each satisfying assignment and computing the sum of these weights.