A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

2014 ◽  
Author(s):  
Koji Nakano
Keyword(s):  
Dynamic Programming ◽  
Parallel Algorithm ◽  
Hierarchical Memory ◽  
Time Optimal

PARALLEL SYNTENIC ALIGNMENTS

2003 ◽  
Vol 13 (04) ◽  
pp. 689-703 ◽  
Author(s):  
NATSUHIKO FUTAMURA ◽  
SRINIVAS ALURU ◽  
XIAOQIU HUANG
Keyword(s):  
Parallel Algorithm ◽  
Dna Sequences ◽  
Genomic Dna ◽  
Sequential Algorithm ◽  
Base Pairs ◽  
Conserved Genes ◽  
Alignment Problem ◽  
Time Optimal ◽  
High Degree ◽  
Gene Rich Region

Given two genomic DNA sequences, the syntenic alignment problem is to compute an ordered list of subsequences for each sequence such that the corresponding subsequence pairs exhibit a high degree of similarity. Syntenic alignments are useful in comparing genomic DNA from related species and in identifying conserved genes. In this paper, we present a parallel algorithm for computing syntenic alignments that runs in [Formula: see text] time, where m and n are the respective lengths of the two genomic sequences, and p is the number of processors used. Our algorithm is time optimal with respect to the corresponding sequential algorithm and can use [Formula: see text] processors, where n is the length of the larger sequence. The space requirement of the algorithm is [Formula: see text] per processor. Using an implementation of this parallel algorithm, we report the alignment of a gene-rich region of human chromosome 12, namely 12p13 and its syntenic region in mouse chromosome 6 (both over 220,000 base pairs in length) in under 24 minutes on a 64-processor IBM xSeries cluster.

Online time-optimal path and trajectory planning for robotic multipoint assembly

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Meng Joo Er ◽  
Chen Guo
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Trajectory Planning ◽  
Optimal Path ◽  
Adaptive Dynamic Programming ◽  
Problem Model ◽  
Content Type ◽  
Markov Decision ◽  
Time Optimal ◽  
Planning Algorithm

Purpose The purpose of this paper is to propose an efficient path and trajectory planning method to solve online robotic multipoint assembly. Design/methodology/approach A path planning algorithm called policy memorized adaptive dynamic programming (PM-ADP) combines with a trajectory planning algorithm called adaptive elite genetic algorithm (AEGA) for online time-optimal path and trajectory planning. Findings Experimental results and comparative study show that the PM-ADP is more efficient and accurate than traditional algorithms in a smaller assembly task. Under the shortest assembly path, AEGA is used to plan the time-optimal trajectories of the robot and be more efficient than GA. Practical implications The proposed method builds a new online and efficient path planning arithmetic to cope with the uncertain and dynamic nature of the multipoint assembly path in the Cartesian space. Moreover, the optimized trajectories of the joints can make the movement of the robot continuously and efficiently. Originality/value The proposed method is a combination of time-optimal path planning with trajectory planning. The traveling salesman problem model of assembly path is established to transfer the assembly process into a Markov decision process (MDP). A new dynamic programming (DP) algorithm, termed PM-ADP, which combines the memorized policy and adaptivity, is developed to optimize the shortest assembly path. GA is improved, termed AEGA, which is used for online time-optimal trajectory planning in joints space.

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