This inequality does not seem to have been noticed before but may be used for accelerating algorithms for D-optimum design, or for the determination of minimum covering ellipsoids, as used, e.g., in ellipsoidal trimming.
Given that most PCs these days have a multi-core CPU and a GPU card, our methodology will be very helpful for accelerating algorithms in a cost-effective manner.
The accelerated algorithm was successfully applied to several test examples with artificial vibration data.
Based on a unique "one-iteration" paradigm, it enables accelerated algorithm development.
We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays.
An accelerated algorithm for the resolution of the coupled Schrödinger/Poisson system, with open boundary conditions, is presented.
The new computing technologies (SIMD, DSP, multi-cores) can greatly accelerate algorithm processing, but require rethinking these algorithms by optimizing the parallelism.
This paper presents the details of the proposed accelerated algorithm, having a time complexity of O(n log n), that decreases training time significantly without decreasing prediction accuracy.
This adapted number of microsteps will result in an accelerated algorithm (aFE-MD-HMM) obtaining the same accuracy of results at significantly reduced computational cost.
