In this chapter, we will study algorithms for both two-stage as well as multi-stage stochastic mixed-integer programs. We present stagewise (resource-directive) decomposition methods for two-stage models, and s
de Valpine P, Turek D, Paciorek CJ, Anderson-Bergman C, Lang DT, Bodik R (2017). "Pro- gramming with models: writing statistical algorithms for general model structures with NIMBLE." Journal of Computational and Graphical Statistics, 26(2), 403-413....
3.1.2 Programming models and languages Programming models are supposed to explain how a program will be executed while the parallel computer model is meant to be an abstraction of the hardware. The programming model acts as a bridge between algorithms and actual implementations in software. Parallel...
This feedback modifies the internal recurrent weights in the RNN, allowing it to store and modify a history of its states and inputs to store and run algorithms. Hence, the closed-loop architecture is no longer solely a function approximator. We consider the same RNN as in Fig. 1a, ...
New Algorithms and Python Bindings Overview The Accelerate Computer Vision and Image Processing using VPI 1.1 webinar (Registration Required) discusses the new algorithms and Python support included in VPI-1.1 as part of JetPack 4.6. See the Python support (Registration required) VPI and PyTorch Int...
Research data refers to the results of observations or experimentation that validate research findings, which may also include software, code, models, algorithms, protocols, methods and other useful materials related to the project. Please read our guidelines on sharing research data for more inform...
Due to its high performance, Java is a suitable language for developing ETL jobs and performing data tasks that require big storage and complex processing requirements, like machine learning algorithms. 5. Julia Julia can be considered a data science rising star. Despite being one of the youngest...
Non-convex optimization is an active area of research in optimization with one of the goals being to establish complexity guarantees for finding approximate stationary points, see the review [40] and the references therein. Two large groups of algorithms that allow one to achieve this goal are fi...
(consumed memory, latency, and data traffic). Call graphs have, for example, been used in[153]. The authors developed partitioning algorithms that work on the consumption graph of the running applications. Both static and dynamic partitioning strategies were considered. For the static case, the ...
Unlike Hadoop, Spark uses Resilient Distributed Datasets (RDDs) which implement in-memory data structures used to cache intermediate data across a set of nodes. Since RDDs can be kept in memory, algorithms can iterate over RDD data many times very efficiently. In addition, Spark provides many ...