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About 10,000 papers for “Algorithm”

Dijkstra's algorithm vs transit algorithm

Esther Yizhen. Chua · DR-NTU (Nanyang Technological University) · 2011

Shortest path problem is a problem that is relevant to a wide range of applications like traffic simulation and network routing. These applications play a huge part in our daily lives thus there is a need to explore algorithms which are faster and more efficient.
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\nThis project explores and implements Dijkstra’s algorithm and Transit algorithm.
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\nDijkstra’s algorithm is an algorithm that computes the shortest path between two nodes in a graph whose path costs are positive by iteratively visiting all the nodes that are closer to the source than the destination before eventually reaching the destination.
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\nTransit algorithm makes use of transit nodes as a mean to pre-process a road network where transit nodes are a set of nodes with the property that every non-local shortest path passes through at least one of these nodes. A path is deemed as non-local if its source and destination are more than 4 grid cells apart.
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\nComputation of the length of the shortest paths between each pair of transit nodes and between each node in the graph and its neighboring transit nodes are done thus allowing non-local shortest path queries to be answered in an extremely fast manner by combining information from a small number of lookup in a table.
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\nDijkstra’s algorithm, on average will take seconds for a random query while Transit algorithm is expected to have a worst case query processing time of about 10 microseconds for 99% of the queries on United States network which has around 24 million nodes and 29 million edges.

Cited by 0

Search algorithm and algorithm outputs.

Claire Chambers (777152), Gaiqing Kong (6808118), Kunlin Wei (284229) et al. · Figshare · 2019

<p>(<b>A</b>) Flowchart describing stages of search algorithm used to find segments of videos containing pairs of walkers. We (i) extracted frames from subsampled videos, (ii) fit pose to the frames using OpenPose, (iii) detected pairs from pose estimates, then (iv) identified sequences of frames that contained pairs of people, resulting in a set of pose estimates from frame sequences. (<b>B</b>) Example video frame containing a walking pair with overlaid pose estimates. A bounding box highlights the persons identified as a pair. (<b>C</b>) Examples of walking pairs with overlaid pose estimates.</p>

Cited by 0Open access

Decompression Using HIRAC Algorithm Algorithm

Muthukumar Murugesan, T. Ravichandran · 2012

Storage space ability and computational authority increases, pr ocessing and analyzing large volumes of database systems acts as a significant role in several areas of scientific research. Database compression and decompression is a susceptible problem in the database framework. Disk storage systems can often be the most expensive components of a database solution, even a small reduction in the storage subsystem can result in substantial cost savings for the entire database solution. When you have large amounts of data, the cost of the storage subsystem can easily exceed the cost of your data server. In this paper we proposed a new algorithm for database compression and decompression called HIRAC algorithm. If we compress the database, there is chance of hurting the data in the compressed database systems. Projected algorithm will manipulates each row in compressed database inside the file to extract the original database without losing any data.

Cited by 0

feiyunzhuiyancehng/LandTrendr-algorithm: GEE_LandTrendr algorithm

LIzengnan · Zenodo (CERN European Organization for Nuclear Research) · 2026

This project includes the full processing workflow implemented in Google Earth Engine (GEE), covering cloud masking, NBR calculation, annual optimal pixel compositing, and time series disturbance detection ands results using LandTrendr algorithms.

Cited by 0Open access

Algorithm Description TTComp02: Algorithm Description

Tomáš Müller · 2008

The implemented algorithm uses forward based search that extends a partial feasible solution towards a complete solution. The next solution is derived from the previous solution by allocating some event and removing conflicting events from the partial schedule. Every step is guided by a heuristic that aims minimal violation of soft constraints requirements. The algorithm works in iterations. It uses two basic data structures: a set of events that are not scheduled and a partial feasible timetable. At each iteration step, the algorithm tries to improve the current partial timetable towards a complete timetable. The timetabling starts with an empty partial timetable (which is a feasible timetable), i.e. all the events are in the list of non-scheduled events. Then it goes repeatedly from one partial solution to another feasible solution until all the events are scheduled. Look now what is going on in the iteration step. First, the algorithm selects one non-scheduled event and computes the locations where the event can be allocated (locations with the hard constraint are not assumed). Every location is described by a start time (the number of the slot for the event) and by a selected room. Then every location is evaluated using a heuristic function over the current partial timetable. Finally, the event is placed to the best location that may cause some conflicts with already scheduled events. Such conflicting events are removed from the timetable and they are put back to the list of non-scheduled activities. The below algorithm schema (Fig.1.) is parameterised by two functions: event selection and location selection. We can see these functions in a broader view of constraint programming as variable selection and value selection functions. Techniques used for activity and location selection and for escaping from a cycle are described in next sections. Fine-tuning Because for the given set of input instances, this algorithm finds a solution very quickly (several seconds). The rest of the available time is used for improving the found solution. This fine-tuning works again in iterations. The above described algorithm is repeatedly executed on the previous solution with several events

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Algorithm

Arthur Tatnall · Encyclopedia of Education and Information Technologies · 2020

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Algorithm

Brentjes, S. · Max Planck Digital Library · 2017

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algorithm

M. Rezapoor Mirsaleh, M. R. Meybodi, M. Rezapoor et al. · 2016

Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. A learning automata-based memetic algorithm

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Algorithm

Elliott Sohn, See Profile, John Chen et al. · 2013

All in-text\treferences\tunderlined\tin\tblue\tare\tlinked\tto\tpublications\ton\tResearchGate, letting you\taccess\tand\tread\tthem\timmediately.

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Algorithm

J. Arthur Snoke, Malcolm Sambridge · 2011

Constraints on the S wave velocity structure in a continental shield from surface wave data: Comparing linearized least squares inversion

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Algorithm

O. Alomair, A. Malallah, A. Elsharkawy et al. · 2015

Résumé — Prédiction de la pression miscibilite ́ minimum (MMP) du CO2 en utilisant un algorithme base ́ sur l’ACE (Alternating Conditional Expectation) — L’injection de gaz miscibles est une des méthodes les plus utilisées pour améliorer la récupération d’hydrocarbures (Enhanced Oil Recovery, EOR). En raison du coût important de cette technique, un haut degre ́ de précision est requis pour prédire le processus. Une telle précision comprend les paramètres de dépistage préliminaires pour le déplacement de gaz miscible, la pression minimale de miscibilité (Minimum Miscibility Pressure, MMP) et de la disponibilite ́ du gaz. Toutes les méthodes de mesure du MMP conventionnelles sont des processus consommateurs de temps requérant de ce fait des coûts importants. Par conséquent, afin de répondre aux demandes de réponses rapides du secteur, une approche non paramétrique basée sur l’algorithme ACE (Alternating Conditional Expectation) due a ̀ Brieman et Friedman [Brieman

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algorithm

Fabien Lauer, Gérard Bloch · 2008

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Algorithm

Mark C. Neyrinck, Nickolay Y. Gnedin, Andrew J. S. Hamilton · 2004

A crucial step in comparing N-body simulations to the observed galaxy distribution is to identify the possible sites of galaxy formation, called dark-matter haloes, in the simulations. Unfortunately, the concept of a dark-matter halo

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