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GPOPS-II (pronounced "GPOPS 2") is a general-purpose MATLAB software for solving continuous optimal control problems using hp-adaptive Gaussian quadrature collocation and sparse nonlinear programming.
Startup Optimizer uses research-based classifications to automatically detect unneeded startup items, while allowing all the important programs and services to load. Program Accelerator™ This feature uses patented calibration technology to realign program files for the fastest possible access.
Adaptive optimization is a technique in computer science that performs dynamic recompilation of portions of a program based on the current execution profile. With a simple implementation, an adaptive optimizer may simply make a trade-off between just-in-time compilation and interpreting instructions.
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The migration of traditional IT, in which IT directly controls IT purchasing, deployment, management, and use, to a cloud computing model holds a number of challenges. A paper titled "Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS," by researchers at the Cloud Computing Co-laboratory, School of Computer Science, University of St Andrews, raises several socio ...
Active-state power management (ASPM) is a power management mechanism for PCI Express devices to garner power savings while otherwise in a fully active state. Predominantly, this is achieved through active-state link power management; i.e., the PCI Express serial link is powered down when there is no traffic across it.
Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean ...
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).