We propose a general greedy algorithm for binary de Bruijn sequences, called Generalized Prefer-Opposite algorithm, and its modifications. By identifying specific feedback functions and initial states, we demonstrate ...
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We propose a general greedy algorithm for binary de Bruijn sequences, called Generalized Prefer-Opposite algorithm, and its modifications. By identifying specific feedback functions and initial states, we demonstrate that most previously-known greedy algorithms that generate binary de Bruijn sequences are particular cases of our algorithm.
The greedy algorithm to produce n-dimensional subspaces X-n to approximate a compact set F contained in a Hilbert space was introduced in the context of reduced basis method in [12,13]. The same algorithm works for a ...
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The greedy algorithm to produce n-dimensional subspaces X-n to approximate a compact set F contained in a Hilbert space was introduced in the context of reduced basis method in [12,13]. The same algorithm works for a general Banach space and in this context was studied in [4]. In this paper we study the case F subset of L-p. If Kolmogorov diameters d(n)(F) of F decay as n(-alpha) we give an almost optimal estimate for the decay of sigma(n) := dist(F,X-n). We also give some direct estimates of the form sigma(n) <= C(n)d(n)(F). (C) 2014 Published by Elsevier Inc.
We study the greedy independent set algorithm on sparse Erdos-Renyi random graphs G(n,c/n). A large deviation principle was recently established by Bermolen et al. in 2020, however, the proof and rate function are som...
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We study the greedy independent set algorithm on sparse Erdos-Renyi random graphs G(n,c/n). A large deviation principle was recently established by Bermolen et al. in 2020, however, the proof and rate function are somewhat involved. Upper bounds for the rate function were obtained earlier by Pittel in 1982. Using discrete calculus, we identify the optimal trajectory realizing a given large deviation and obtain the rate function in a simple closed form. In particular, we show that Pittel's bounds are sharp. The proof is brief and elementary. We think the methods presented here will be useful in analyzing the tail behavior of other random processes.
This paper proposes a tracklet-based algorithm for online multiple-target tracking. The algorithm performs tracking in three steps: (1) tracklet initialization, (2) tracklet refinement, and (3) tracklet association. G...
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ISBN:
(纸本)9781479983391
This paper proposes a tracklet-based algorithm for online multiple-target tracking. The algorithm performs tracking in three steps: (1) tracklet initialization, (2) tracklet refinement, and (3) tracklet association. Given detection responses, tracklets are initialized by finding a near-optimum path in the min-cost flow network using a greedy-based algorithm. Based on an appearance-based model, the tracklets are refined so that the detection responses within the tracklet become more homogeneous. Finally, the tracklets are linked based on a novel affinity measure, then by optimizing a min-cost flow network with links, the final tracks are generated. For real-time multi-target tracking, every step is processed in a segment-wise manner. On popular public datasets and strictly in an online fashion, the proposed multi-target tracking algorithm performed comparable to that of many state-of-the-art algorithms.
The fundamental objectives of locating facilities can be summarized into three categories. The first category refers to those designed to cover demand within a specified time or distance. This objective gives rise to ...
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ISBN:
(纸本)9780987214355
The fundamental objectives of locating facilities can be summarized into three categories. The first category refers to those designed to cover demand within a specified time or distance. This objective gives rise to location problems which are known as the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). The LSCP seeks to locate the minimum number of facilities required to 'cover' all demand or population in an area. The MCLP is to locate a predetermined number of facilities to maximize the demand or population that is covered. The second category refers to those designed to minimize maximum distance. This results in a location problem known as the p-center problem which addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category refers to those designed to minimize the average weighted distance or time. This objective leads to a location problem known as the p-median problem. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The p-median problem is a typical combinatorial optimization problem with many practical applications such as location of warehouses, schools, health centers, shops etc. greedy algorithms are the simplest algorithms to design however it is not easy to understand its capability and limitations. A greedy algorithm solves a global optimization problem by making a sequence of locally optimal decisions. That is a greedy algorithm always chooses the next step of an algorithm that is locally optimal. For example for Facility Location Problem we will consider the facilities for which decisions regarding locally optimal locations will be made. The decisions that are made regarding where to locate successive facilities by a greedy method are permanent. That is the greedy algorithms
In the recent years, there has been a steady increase in the use of electrical vehicles (EV). Their further adoption is becoming more dependent on the quality of service provided by the charging infrastructure. In thi...
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ISBN:
(纸本)9781665479080
In the recent years, there has been a steady increase in the use of electrical vehicles (EV). Their further adoption is becoming more dependent on the quality of service provided by the charging infrastructure. In this paper, the focus is on optimizing the charging infrastructure from the point of minimizing the service drop modelled using the standard M/M/c/c loss queue. To be exact, a mathematical model is proposed for the problem of optimizing capacities at individual stations in an EV charging network. The novelty is in considering the relation of capacity of a charging station to its arrival rate. Due to the non-linearity of the problem, a greedy algorithm combined with a local search is developed for finding near optimal configurations of the system. The new model is evaluated using real-world data for population density and existing charging infrastructure for metropolitan areas. The conducted computational experiments, show that charging networks optimized using the proposed model, significantly better reflect the state-on-the-ground than standardly used models, while maintaining a low service drop rate.
We consider optimal sensor placement for a family of linear Bayesian inverse problems characterized by a deterministic hyper-parameter. The hyper-parameter describes distinct configurations in which measurements can b...
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We consider optimal sensor placement for a family of linear Bayesian inverse problems characterized by a deterministic hyper-parameter. The hyper-parameter describes distinct configurations in which measurements can be taken of the observed physical system. To optimally reduce the uncertainty in the system's model with a single set of sensors, the initial sensor placement needs to account for the non-linear state changes of all admissible configurations. We address this requirement through an observability coefficient which links the posteriors' uncertainties directly to the choice of sensors. We propose a greedy sensor selection algorithm to iteratively improve the observability coefficient for all configurations through orthogonal matching pursuit. The algorithm allows explicitly correlated noise models even for large sets of candidate sensors, and remains computationally efficient for high-dimensional forward models through model order reduction. We demonstrate our approach on a large-scale geophysical model of the Perth Basin, and provide numerical studies regarding optimality and scalability with regard to classic optimal experimental design utility functions.
This letter studies the problem of minimizing increasing set functions, equivalently, maximizing decreasing set functions, over the base matroid. This setting has received great interest, since it generalizes several ...
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This letter studies the problem of minimizing increasing set functions, equivalently, maximizing decreasing set functions, over the base matroid. This setting has received great interest, since it generalizes several applied problems including actuator and sensor placement problems in control, task allocation problems, video summarization, and many others. We study two greedy heuristics, namely, the forward and reverse greedy. We provide two novel performance guarantees for the approximate solutions obtained by them depending on both submodularity ratio and curvature. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
In this article, a greedy reduced basis algorithm is proposed for the solution of structural acoustic systems with parameter and implicit frequency dependence. The underlying equations of linear time-harmonic elastody...
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In this article, a greedy reduced basis algorithm is proposed for the solution of structural acoustic systems with parameter and implicit frequency dependence. The underlying equations of linear time-harmonic elastodynamics and acoustics are discretized using the finite element and boundary element method, respectively. The solution within the parameter domain is determined by a linear combination of reduced basis vectors. This basis is generated iteratively and given by the responses of the structural acoustic system at certain parameter samples. A greedy approach is followed by evaluating the next basis vector at the parameter sample which is currently approximated worst. The algorithm runs on a small training set which bounds the memory requirements and allows applications to large-scale problems with high-dimensional parameter domains. The computational efficiency of the proposed scheme is illustrated based on two numerical examples: a point-excited spherical shell submerged in water and a satellite structure subject to a diffuse sound pressure field excitation.
This paper proposes a new technique of state estimation (SE) for electric power systems. In the proposed scheme, the Phasor Measurement Units (PMU) are first placed optimally using greedy algorithm for cost reduction,...
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ISBN:
(纸本)9781467385886
This paper proposes a new technique of state estimation (SE) for electric power systems. In the proposed scheme, the Phasor Measurement Units (PMU) are first placed optimally using greedy algorithm for cost reduction, while complete observability of system is also obtained. The SE uses a linear measurement model to obtain the estimated states directly, without any iteration, thereby improves the quality of the estimated data base. To reveal the efficacy of the proposed scheme it has been tested on standard IEEE 5-bus, I4-bus, 30-bus, 57-bus and 118-Bus test systems and the test results are presented.
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