A scenario-based optimization approach is proposed to design gasoline blending recipes under uncertainty. The proposed scheme considers the nonlinear mixing law of octane, uses Monte Carlo sampling-based method to sim...
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ISBN:
(数字)9781538682661
ISBN:
(纸本)9781538682678
A scenario-based optimization approach is proposed to design gasoline blending recipes under uncertainty. The proposed scheme considers the nonlinear mixing law of octane, uses Monte Carlo sampling-based method to simulate parametric uncertainties, and employs a sequential algorithm with bound tightening to obtain a γ-global optimal solution. This framework offers three advantages: First, incorporating the nonlinear functions into the chance-constrained optimization will improve predictions on the key properties of the blend. Second, by accounting for uncertainty in the blending process, a solution with best expected quality under several possible conditions can be achieved. Third, the sequential algorithm with bound tightening determines the γ-global optimal solution faster than directly using state-of-the-art optimization software. A case study with nine feedstocks and two products is presented to demonstrate the effectiveness of the proposed method.
In this paper, we propose a novel floorplanning algorithm based on simulated annealing on GPUs. Simulated annealing is an inherently sequential algorithm, far from the typical programs suitable for Single Instruction ...
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ISBN:
(纸本)9781612849133
In this paper, we propose a novel floorplanning algorithm based on simulated annealing on GPUs. Simulated annealing is an inherently sequential algorithm, far from the typical programs suitable for Single Instruction Multiple Data (SIMD) style concurrency in a GPU. We propose a fundamentally different approach of exploring the floorplan solution space, where we evaluate concurrent moves on a given floorplan. We illustrate several performance optimization techniques for this algorithm on GPUs. Compared to the sequential algorithm, our techniques achieve 6-160X speedup for a range of MCNC and GSRC benchmarks, while delivering comparable or better solution quality.
An important task of the radar data processing is target track loss detection, after which it is reset. It acquires special significance at small signal-to-noise ratio. The presence of plots amplitude information allo...
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ISBN:
(数字)9781728197135
ISBN:
(纸本)9781728197142
An important task of the radar data processing is target track loss detection, after which it is reset. It acquires special significance at small signal-to-noise ratio. The presence of plots amplitude information allows to increase the efficiency of target detection and target tracking in the presence of interference. In the work a sequential algorithm for target track loss detection using the plots amplitude information based on the Shiryaev rule is obtained. Moreover, the method of probabilistic data association is used to estimate target's movement parameters at small signal-to-noise ratio. The analysis of the obtained algorithm efficiency is performed on a model example of target tracking due to the survey radar which measures range and range rate using statistical modeling.
We present, in this paper, a hybrid algorithm which makes use of Time Warp between clusters of LPs and a sequential algorithm within the cluster. Time Warp is, of course, traditionally implemented between individual L...
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ISBN:
(纸本)9780818671203
We present, in this paper, a hybrid algorithm which makes use of Time Warp between clusters of LPs and a sequential algorithm within the cluster. Time Warp is, of course, traditionally implemented between individual LPs. The algorithm was implemented in a digital logic simulator, and its performance compared to that of Time *** upon this platform we develop a family of three checkpointing algorithms, each of which occupies a different point in the spectrum of possible trade-offs between memory usage and execution time. The algorithms were implemented on several digital logic circuits and their speed, number of states saved and maximal memory consumption were compared to those of Time Warp. One of the algorithms saved between 35 and 50% of the maximal memory consumed by Time Warp (depending upon the number of processors used), while the other two decreased the maximal usage up to 30%. The latter two algorithms exhibited a speed comparable to Time Warp, while the first algorithm was 30-60% *** algorithms are also simpler to implement than optimal checkpointing algorithms.
This paper presents an implementation of the Jacobi power flow algorithm to be run on a single instruction multiple data (SIMD) unit processor. The purpose is to be able to solve a large number of power flows in paral...
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ISBN:
(纸本)9781424487790
This paper presents an implementation of the Jacobi power flow algorithm to be run on a single instruction multiple data (SIMD) unit processor. The purpose is to be able to solve a large number of power flows in parallel as quickly as possible. This well-known algorithm was modified taking into account the characteristics of the SIMD architecture. The results show a significant speed-up of the algorithm compared to the time required to solve the algorithm in a conventional CPU, even when a more efficient sequential algorithm, such as the Newton-Raphson, is used. The accuracy of the performance has been validated with the results of the IEEE-118 standard network.
Motivated by our recent extension of the Two-Stage sequential algorithm (eTSSO), we propose an adaptation of the framework in Pasupathy et al. (2015) for the study of convergence of kriging-based procedures. Specifica...
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ISBN:
(纸本)9781467397414
Motivated by our recent extension of the Two-Stage sequential algorithm (eTSSO), we propose an adaptation of the framework in Pasupathy et al. (2015) for the study of convergence of kriging-based procedures. Specifically, we extend the proof scheme in Pasupathy et al. (2015) to the class of kriging-based simulation-optimization algorithms. In particular, the asymptotic convergence and the convergence rate of eTSSO are investigated by interpreting the kriging-based search as a stochastic recursion. We show the parallelism between the two paradigms and exploit the deterministic counterpart of eTSSO, the more famous Efficient Global Optimization (EGO) procedure, in order to derive eTSSO structural properties. This work represents a first step towards a general proof framework for the asymptotic convergence and convergence rate analysis of meta-model based simulation-optimization.
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