Search results for "Search algorithm"
showing 10 items of 73 documents
Quantum search by parallel eigenvalue adiabatic passage
2008
We propose a strategy to achieve the Grover search algorithm by adiabatic passage in a very efficient way. An adiabatic process can be characterized by the instantaneous eigenvalues of the pertaining Hamiltonian, some of which form a gap. The key to the efficiency is based on the use of parallel eigenvalues. This allows us to obtain non-adiabatic losses which are exponentially small, independently of the number of items in the database in which the search is performed.
Orbit determination and errors of a star catalogue
1995
Abstract We obtain Ceres orbit taking into account all perturbations and applying correction of phase effect to observations. More than 3000 observations have been used and the results agree with other determinations. We have also investigated the influence of systematic errors of a star catalogue on the observations and its effect on final elements. To that aim, a simulation procedure has been applied to Ceres observations, including different laws for errors of star catalogue versus spherical coordinates. The best relations have been applied to real observations of Ceres, in order to obtain elements and star catalogue corrections. Preliminary results of these calculations are presented.
Grover’s Search with Faults on Some Marked Elements
2018
Grover’s algorithm is a quantum query algorithm solving the unstructured search problem of size [Formula: see text] using [Formula: see text] queries. It provides a significant speed-up over any classical algorithm [3]. The running time of the algorithm, however, is very sensitive to errors in queries. Multiple authors have analysed the algorithm using different models of query errors and showed the loss of quantum speed-up [2, 6]. We study the behavior of Grover’s algorithm in the model where the search space contains both faulty and non-faulty marked elements. We show that in this setting it is indeed possible to find one of marked elements in [Formula: see text] queries. We also analyze…
Parallel Random Search and Tabu Search for the Minimal Consistent Subset Selection Problem
1998
The Minimal Consistent Subset Selection (MCSS) problem is a discrete optimization problem whose resolution for large scale instances requires a prohibitive processing time. Prior algorithms addressing this problem are presented. Randomization and approximation techniques are suitable to face the problem, then random search and meta-heuristics are proposed and consequently Tabu Search strategies are applied and evaluated. Parallel computing helps to reduce processing time and/or produce better results; different approaches for designing parallel tabu search are analyzed.
Combined IT and power supply infrastructure sizing for standalone green data centers
2021
International audience; In this work, we propose a two-step methodology for designing and sizing a data center solely powered by local renewable energy. The first step consists in determining the necessary IT equipment for processing a given IT workload composed of batch and service tasks. We propose an adapted binary search algorithm and prove its optimality to find the minimum number of servers to handle the IT workload. When the IT sizing is computed, the second step consists in defining the supplying electrical infrastructure using wind turbines and photovoltaic panels as primary sources. Batteries and a hydrogen system are added as secondary sources for short- and long-term energy stor…
Notice of Violation of IEEE Publication Principles: Reinforcement learning for P2P searching
2005
For a peer-to-peer (P2P) system holding a massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple reinforcement learni…
A hybrid multi-objective optimization algorithm for content based image retrieval
2013
Abstract Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retriev…
A genetic approach for adding QoS to distributed virtual environments
2007
Distributed virtual environment (DVE) systems have been designed last years as a set of distributed servers. These systems allow a large number of remote users to share a single 3D virtual scene. In order to provide quality of service in a DVE system, clients should be properly assigned to servers taking into account system throughput and system latency. The latter one is composed of both network and computational delays. This highly complex problem is known as the quality of service (QoS) problem. In this paper, we study the implementation of a genetic algorithm (GA) for solving the QoS problem in DVE systems. Performance evaluation results show that, due to its ability of both finding goo…
A Scatter Search Algorithm for the Split Delivery Vehicle Routing Problem
2008
In this chapter we present a metaheuristic procedure constructed for the special case of the Vehicle Routing Problem in which the demands of clients can be split, i.e., any client can be serviced by more than one vehicle. The proposed algorithm, based on the scatter search methodology, produces a feasible solution using the minimum number of vehicles. The quality of the obtained results is comparable to the best results known up to date on a set of instances previously published in the literature.
An A* Based Semantic Tokenizer for Increasing the Performance of Semantic Applications
2013
Semantic Applications (SAs) makes use of ontolo- gies and their performance can depend on the syntactic labels of the modeled entities; even if several approaches have been devised to formalize ontologies, no formal approaches have been devised for naming their constituents, which look as long word concatenations without any particular separation. We present a novel semantic tokenizer that finds the sub-words through an application of the A* based search algorithm; the A* functions rely on a set of linguistic criteria and on the meta-cognitive perspective of the activity of reading.