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In contrast to sequential updates, parallel updates are not guaranteed to converge.
Fig. 3 Sequential updates using a naive approach, proceeding from bottom right to top left.
Furthermore, an unusual alternative interpretation of sequential updates can be found in the EnKF literature.
We propose two CRF optimization techniques, using parallel and sequential updates, and compare them in detail.
Without initialization, sequential updates lead to better results than parallel ones.
We propose two efficient filtering techniques to solve the mean-field approximation, using parallel and sequential updates.
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In addition we propose a sequential updating strategy to an orthogonal array based Latin hypercube sample.
Our method takes advantage of the GPU Single Instruction, Multiple Data (SIMD) architectures, and adopts the sequential updating scheme of Metropolis algorithm.
This strategy of using the best deterministic process information coupled with sequential updating can be successfully applied to many chemical and biochemical processing problems including batch beer fermentation.
Through extensive numerical investigations, we herein first shed light on issues relating to the construction of canonical LRA with a particular greedy algorithm involving a sequential updating of the polynomial coefficients along separate dimensions.
The proposed algorithm combines (a) convex optimization for the determination of optimal weights on a support set, (b) sequential updating of this support using local optimization, and (c) finding new support candidates using properties of the directional derivative of ϕ.
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