APLIKASI ALGORITMA BACKTRACKING UNTUK MENENTUKAN RUTE OPTIMAL DISTRIBUSI AIR ISI ULANG GONZALO DI KOTA AMBON

Distribution is a delivery of goods from an original area to the destination area, wherein the distribution, the problem of travel routes is very important because it can affect the time and cost of doing the distribution. The optimal route itself is a route that minimizes the distance and travel time. This research using the Backtracking Algorithm as part of the Traveling salesman problem method in finding the shortest route or minimum distance. In this research, the Backtracking algorithm is applied to search the minimum route for Gonzalo refill water distribution. The results obtained are the path with the shortest route in Ambon City, such as: Gonzalo - Jln. Karang Panjang - Jln. Pitu ina - Jln. Dr. Kayadoe - Terminal mardika - Jln. Wr. Supratman - Jln. A.Y. Patty - Jln. Said Commands - Jln. Pattimura - Jln A. Yani - Gonzalo, with a long of travel route is 15,301 Km

Параллельный алгоритм решения задачи об изоморфизме графов

In this paper, we offer an efficient parallel algorithm for solving the Graph Isomorphism Problem. Our goal is to construct a suitable vertex substitution or to prove the absence of such. The problem is solved for undirected graphs without loops and multiple edges, it is assumed that the graphs can be disconnected. e question of the existence or absence of an algorithm for solving this problem with polynomial complexity is currently open. Therefore, as for any time-consuming task, the question arises of accelerating its solution by parallelizing the algorithm. We used the RPM ParLib library developed by the author as the main tool to program the algorithm. This library allows us to develop effective applications for parallel computing on a local network in the .NET Framework. Such applications have the ability to generate parallel branches of computation directly during program execution and dynamically redistribute work between computing modules. Any language with support for the .NET Framework can be used as a programming language in conjunction with this library. For our experiments, we developed some C# applications using this library. The main purpose of these experiments was to study the acceleration achieved by recursive-parallel computing. Specially generated random regular graphs with varying degrees of vertices were used as initial data. A detailed description of the algorithm and its testing, as well as the results obtained, are also given in the paper.В данной работе предлагается параллельный алгоритм решения задачи об изоморфизме графов. Целевым результатом для нас выступает построение подходящей подстановки вершин, либо доказательство отсутствия таковой. Задача решается для неориентированных графов без петель и кратных ребер, допускается, что графы могут быть несвязными. Вопрос о существовании либо отсутствии алгоритма с полиномиальной трудоемкостью в настоящее время является открытым. Следовательно, как и для любой трудоемкой задачи, возникает вопрос об ускорении ее решения за счет распараллеливания алгоритма. Для организации параллельных вычислений автором использовалась библиотека RPM_ParLib, которая позволяет создавать параллельные приложения, работающие в локальной вычислительной сети под управлением среды исполнения .NET Framework. Библиотека поддерживает рекурсивно-параллельный стиль программирования и обеспечивает эффективное распределение работы и динамическую балансировку загрузки вычислительных модулей в процессе исполнения программы. Она может быть использована для приложений, написанных на любом языке программирования, поддерживаемом .NET Framework. Для решения нашей задачи и проведения численного эксперимента было разработано несколько приложений на языке C#. Целью эксперимента было исследование ускорения, достигаемого за счет рекурсивно-параллельной организации вычислений. В качестве исходных данных использовались специально сгенерированные случайные регулярные графы с различной степенью вершин. Подробное описание алгоритма и эксперимента, а также полученные результаты также приводятся в работе

MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

[Background]In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies. [Results]We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment. [Conclusions]MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns

Lorentzian Spectral Geometry with Causal Sets

We study discrete Lorentzian spectral geometry by investigating to what extent causal sets can be identified through a set of geometric invariants such as spectra. We build on previous work where it was shown that the spectra of certain operators derived from the causal matrix possess considerable but not complete power to distinguish causal sets. We find two especially successful methods for classifying causal sets and we computationally test them for all causal sets of up to $9$ elements. One of the spectral geometric methods that we study involves holding a given causal set fixed and collecting a growing set of its geometric invariants such as spectra (including the spectra of the commutator of certain operators). The second method involves obtaining a limited set of geometric invariants for a given causal set while also collecting these geometric invariants for small `perturbations' of the causal set, a novel method that may also be useful in other areas of spectral geometry. We show that with a suitably chosen set of geometric invariants, this new method fully resolves the causal sets we considered. Concretely, we consider for this purpose perturbations of the original causal set that are formed by adding one element and a link. We discuss potential applications to the path integral in quantum gravity.Comment: 20 pages, 4 figure

Filtering graphs to check isomorphism and extracting mapping by using the Conductance Electrical Model

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper presents a new method of filtering graphs to check exact graph isomorphism and extracting their mapping. Each graph is modeled by a resistive electrical circuit using the Conductance Electrical Model (CEM). By using this model, a necessary condition to check the isomorphism of two graphs is that their equivalent resistances have the same values, but this is not enough, and we have to look for their mapping to find the sufficient condition. We can compute the isomorphism between two graphs in O(N-3), where N is the order of the graph, if their star resistance values are different, otherwise the computational time is exponential, but only with respect to the number of repeated star resistance values, which usually is very small. We can use this technique to filter graphs that are not isomorphic and in case that they are, we can obtain their node mapping. A distinguishing feature over other methods is that, even if there exists repeated star resistance values, we can extract a partial node mapping (of all the nodes except the repeated ones and their neighbors) in O(N-3). The paper presents the method and its application to detect isomorphic graphs in two well know graph databases, where some graphs have more than 600 nodes. (C) 2016 Elsevier Ltd. All rights reserved.Postprint (author's draft

Fault recovery in distributed processing loop networks

A graph model is introduced to formalize the fault recovery process in distributed loop networks. This model is applicable to centralized as well as distributed recovery. Key fault tolerance and recovery parameters including redundancy, fault model, recovery time, and recovery strategy are characterized. Centralized recovery strategies for a given fault-tolerant loop network are presented and analyzed. A distributed recovery strategy, which depends on the cooperation of a set of processors, is given, and its application to a new class of fault-tolerant loop networksis evaluated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27151/1/0000145.pd