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On a forward algorithm for a generalized production inventory problem

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 1999年第1-3期59卷 455-461页

作者： Rempala, R Polish Acad Sci Inst Math PL-00950 Warsaw Poland

A generalized production inventory problem is described in a deterministic optimal control framework. The problem of existence of regeneration points is considered and a forecast horizon for a given decision horizon is determined. It is proved that one can obtain an optimal control step by step on successive decision intervals by a forward algorithm. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： forward algorithm inventory optimal control regeneration points

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Accelerating forward algorithm for Stochastic Automata on Graphics Processing Units

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IEEE ACCESS 2020年 8卷 32270-32279页

作者： Sarwar, Muhammad Umer Hanif, Muhammad Kashif Talib, Ramzan Aziz, Muhammad Haris Govt Coll Univ Dept Comp Sci Faisalabad 38000 Pakistan Univ Engn & Technol Dept Ind Engn Taxila 47050 Pakistan

A stochastic automaton is a non-deterministic automata with input and output behavior which works serially and synchronously. Stochastic automata is being used in different application areas. For large state space and sequence lengths, performance of stochastic automata is a major concern. For this purpose, graphics processing units can be employed to improve the performance. In this study, a parallel version of inference algorithm for stochastic automata is designed. The parallel version is mapped to graphics processing unit using the dynamic parallelism. The performance of parallel version is compared with different realizations and parameters. Parallel implementation of inference algorithm achieved approximately speedup factor of 50 for 256 states.

关键词： Automata Stochastic processes Graphics processing units Heuristic algorithms Learning automata Inference algorithms Kernel Stochastic automata CUDA GPU forward algorithm parallelization

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On the forward algorithm for stopping problems on continuous-time Markov chains

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JOURNAL OF APPLIED PROBABILITY 2021年第4期58卷 1043-1063页

作者： Miclo, Laurent Villeneuve, Stephane CNRS Paris France Univ Toulouse Toulouse France Toulouse Sch Econ 1 Esplanade Univ F-31080 Toulouse 06 France

We revisit the forward algorithm, developed by Irle, to characterize both the value function and the stopping set for a large class of optimal stopping problems on continuous-time Markov chains. Our objective is to renew interest in this constructive method by showing its usefulness in solving some constrained optimal stopping problems that have emerged recently.

关键词： forward algorithm constrained optimal stopping Markov chains

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New symmetric subband forward algorithm based on simple variable step-sizes for speech enhancement 5

New symmetric subband forward algorithm based on simple vari...

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5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Bendoumia, Redha Djendi, Mohamed Guessoum, Abderrezak Univ Blida 1 Signal Proc & Image Lab LATSI Route SoumaaBP 270 Blida 09000 Algeria

ISBN: (纸本)9781538606865

In several previous works, the two-channel adaptive filtering algorithms have been proposed and combined with forward-and-backward blind source separation structures for acoustic noise reduction and speech enhancement. Recently in [1], we have proposed a new two-channel subband forward algorithm (2CSF) for acoustic noise reduction. The main drawback of this algorithm is its poor performance in steady state regime when the fixed step-size is selected large. In this paper, we propose two new versions of the 2CSF algorithm by introducing a simple variable step-size approach, which allow improving both the convergence speed and steady state values. The proposed modifications are based on efficient estimation of the optimal step-sizes of the cross-coupling filters. To confirm the good performance of the proposed variable step-size algorithms, we have carried out several simulations in very noisy situations.

关键词： Variable step-size Speech quality Output SNR Subband adaptive filter forward algorithm

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Improved GPU Implementations of the Pair-HMM forward algorithm for DNA Sequence Alignment 39

Improved GPU Implementations of the Pair-HMM Forward Algorit...

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39th IEEE International Conference on Computer Design ICCD)

作者： Li, Enliang Banerjee, Subho S. Huang, Sitao Iyer, Ravishankar K. Chen, Deming Univ Illinois Dept Elect & Comp Engn Champaign IL 61820 USA

ISBN: (纸本)9781665432191

With the rise of Next-Generation Sequencing (NGS) technology, clinical sequencing services become more accessible but are also facing new challenges. The surging demand motivates developments of more efficient algorithms for computational genomics and their hardware acceleration. In this work, we use GPU to accelerate the DNA variant calling and its related alignment problem. The Pair-Hidden Markov Model (Pair-HMM) is one of the most popular and compute-intensive models used in variant calling. As a critical part of the Pair-HMM, the forward algorithm is not only a computational but data-intensive algorithm. Multiple previous works have been done in efforts to accelerate the computation of the forward algorithm by the massive parallelization of the workload. In this paper, we bring advanced GPU implementations with various optimizations, such as efficient host-device communication, task parallelization, pipelining, and memory management, to tackle this challenging task. Our design has shown a speedup of 783X comparing to the Java baseline on Intel single-core CPU, 31.88X to the C++ baseline on IBM Power8 multicore CPU, and 1.53X - 2.21X to the previous state-of-the-art GPU implementations over various genomics datasets.

关键词： Pair-HMM forward algorithm CUDA implementation GPU acceleration Computational Genomics

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zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm

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BMC BIOINFORMATICS 2013年第1期14卷 1-11页

作者： Sand, Andreas Kristiansen, Martin Pedersen, Christian N. S. Mailund, Thomas Aarhus Univ Bioinformat Res Ctr Aarhus Denmark Aarhus Univ Dept Comp Sci DK-8000 Aarhus Denmark

Background: Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. Results: We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models. Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. Conclusions: We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://***/software/ziphmm/.

关键词： Hide Markov Model Input Sequence Hide State Alphabet Size forward algorithm

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An average-case sublinear forward algorithm for the haploid Li and Stephens model

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algorithmS FOR MOLECULAR BIOLOGY 2019年第1期14卷 11-11页

作者： Rosen, Yohei M. Paten, Benedict J. UCSC Genom Inst 1156 High St Santa Cruz CA 95064 USA NYU Sch Med 550 First Ave New York NY 10016 USA

BackgroundHidden Markov models of haplotype inheritance such as the Li and Stephens model allow for computationally tractable probability calculations using the forward algorithm as long as the representative reference panel used in the model is sufficiently small. Specifically, the monoploid Li and Stephens model and its variants are linear in reference panel size unless heuristic approximations are used. However, sequencing projects numbering in the thousands to hundreds of thousands of individuals are underway, and others numbering in the millions are *** make the forward algorithm for the haploid Li and Stephens model computationally tractable for these datasets, we have created a numerically exact version of the algorithm with observed average case sublinear runtime with respect to reference panel size k when tested against the 1000 Genomes *** show a forward algorithm which avoids any tradeoff between runtime and model complexity. Our algorithm makes use of two general strategies which might be applicable to improving the time complexity of other future sequence analysis algorithms: sparse dynamic programming matrices and lazy evaluation.

关键词： forward algorithm Haplotype Complexity Sublinear algorithms

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forward Slicing algorithm based Test Data Generation

Forward Slicing Algorithm based Test Data Generation

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3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT)

作者： Samuel, Philip Surendran, Anupama Cochin Univ Sci & Technol Div Informat Technol Kochi 682022 Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi 682022 Kerala India

ISBN: (纸本)9781424455379

In this paper we have suggested a new method for test data generation using dynamic forward slicing algorithm. Separating the suspicious parts of code from the original program will make the process of test data generation easier. In forward dynamic slicing, the slices produced are much smaller than the original program and the search space for testing is considerably reduced. In this paper, we discuss the forward algorithm and the advantages of using forward dynamic algorithm for test data generation. The test cases are generated by analyzing the constraints present in the slices constructed from forward dynamic slicing algorithm. In the proposed method, the program for which the test data to be generated is analyzed. forward slicing of the program is done with respect to all the variables whose value is changed during program execution. Slices obtained are then verified for statements which define certain constraints for test data generation. This helps to clearly define the limits of test data and remove the ambiguities about the value of the test data which is to be generated. The test data is then generated based on the constraints. In the proposed method, since forward algorithm is used, the execution traces need not be recorded and analyzing the constraints present in the slices will help in fault localization and debugging. This is one of the most important advantages of our method.

关键词： Test data generation static slicing dynamic slicing forward algorithm

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forward Slicing algorithm based Test Data Generation

Forward Slicing Algorithm based Test Data Generation

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Philip Samuel Anupama Surendran Information Technology Division Cochin University of Science & Technology Dept.of Computer Science Cochin University of Science & Technology

In this paper we have suggested a new method for test data generation using dynamic forward slicing *** the suspicious parts of code from the original program will make the process of test data generation *** forward dynamic slicing,the slices produced are much smaller than the original program and the search space for testing is considerably *** this paper,we discuss the forward algorithm and the advantages of using forward dynamic algorithm for test data *** test cases are generated by analyzing the constraints present in the slices constructed from forward dynamic slicing *** the proposed method,the program for which the test data to be generated is *** slicing of the program is done with respect to all the variables whose value is changed during program *** obtained are then verified for statements which define certain constraints for test data *** helps to clearly define the limits of test data and remove the ambiguities about the value of the test data which is to be *** test data is then generated based on the *** the proposed method,since forward algorithm is used,the execution traces need not be recorded and analyzing the constraints present in the slices will help in fault localization and *** is one of the most important advantages of our method.

关键词： Test data generation static slicing dynamic slicing forward algorithm

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Discretely observed Brownian motion governed by telegraph signal process: Estimation and application to finance

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STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES 2025年第1期28卷 1-17页

作者： Eada, Surya Teja Pozdnyakov, Vladimir Yan, Jun Univ Connecticut Dept Stat 215 Glenbrook Rd Storrs CT 06269 USA

Regime-switching models are commonly used in financial econometrics to capture changes in market dynamics over time. However, classic Markov regime-switching models cannot deal with irregularly spaced time series. To address this limitation, we propose a continuous-time regime-switching model with two states that can handle situations where data points are collected at non-equidistant times. The model employs a Brownian motion with state-specific drift and volatility and a telegraph signal process with exponential holding times to characterize unobserved states. We develop an estimation procedure for the model parameters using the hidden Markov model tools and occupation time results for telegraph signal process. Computationally effective exact likelihood evaluation is achieved with help of a recursive forward algorithm. Our simulation study validates the performance of our method. An analysis of estimates from regular and irregular grids for simulations that utilize trading days and calendar days was also added. In application to a collection of stock prices, we find that our model is competitive with a popular GARCH model.

关键词： BMT process Telegraph process forward algorithm Hidden Markov model Maximum likelihood estimation

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