6,455 research outputs found

    Dependent randomized rounding for clustering and partition systems with knapsack constraints

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    Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that no single demographic group should be over-represented among the cluster-centers. This, and much more general clustering problems, can be formulated with "knapsack" and "partition" constraints. We develop new randomized algorithms targeting such problems, and study two in particular: multi-knapsack median and multi-knapsack center. Our rounding algorithms give new approximation and pseudo-approximation algorithms for these problems. One key technical tool, which may be of independent interest, is a new tail bound analogous to Feige (2006) for sums of random variables with unbounded variances. Such bounds are very useful in inferring properties of large networks using few samples

    Fast, large volume, GPU enabled simulations for the Ly-alpha forest: power spectrum forecasts for baryon acoustic oscillation experiments

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    High redshift measurements of the baryonic acoustic oscillation scale (BAO) from large Ly-alpha forest surveys represent the next frontier of dark energy studies. As part of this effort, efficient simulations of the BAO signature from the Ly-alpha forest will be required. We construct a model for producing fast, large volume simulations of the Ly-alpha forest for this purpose. Utilising a calibrated semi-analytic approach, we are able to run very large simulations in 1 Gpc^3 volumes which fully resolve the Jeans scale in less than a day on a desktop PC using a GPU enabled version of our code. The Ly-alpha forest spectra extracted from our semi-analytical simulations are in excellent agreement with those obtained from a fully hydrodynamical reference simulation. Furthermore, we find our simulated data are in broad agreement with observational measurements of the flux probability distribution and 1D flux power spectrum. We are able to correctly recover the input BAO scale from the 3D Ly-alpha flux power spectrum measured from our simulated data, and estimate that a BOSS-like 10^4 deg^2 survey with ~15 background sources per square degree and a signal-to-noise of ~5 per pixel should achieve a measurement of the BAO scale to within ~1.4 per cent. We also use our simulations to provide simple power-law expressions for estimating the fractional error on the BAO scale on varying the signal-to-noise and the number density of background sources. The speed and flexibility of our approach is well suited for exploring parameter space and the impact of observational and astrophysical systematics on the recovery of the BAO signature from forthcoming large scale spectroscopic surveys.Comment: 16 pages, 11 figures, accepted to MNRA

    A fuzzy goal programming approach to solving decentralized bi-level multi-objective linear fractional programming problems

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    This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method

    A REVIEW OF APPLICATIONS OF MULTIPLE - CRITERIA DECISION-MAKING TECHNIQUES TO FISHERIES

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    Management of public resources, such as fisheries, is a complex task. Society, in general, has a number of goals that it hopes to achieve from the use of public resources. These include conservation, economic, and social objectives. However, these objectives often conflict, due to the varying opinions of the many stakeholders. It would appear that the techniques available in the field of multiple-criteria decision-making (MCDM) are well suited to the analysis and determination of fisheries management regimes. However, to date, relatively few publications exist using such MCDM methods compared to other applicational fields, such as forestry, agriculture, and finance. This paper reviews MCDM applied to fishery management by providing an overview of the research published to date. Conclusions are drawn regarding the success and applicability of these techniques to analyzing fisheries management problems.Resource /Energy Economics and Policy,

    Spectral Efficiency and Energy Efficiency Tradeoff in Massive MIMO Downlink Transmission with Statistical CSIT

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    As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.Comment: Typos corrected. 14 pages, 7 figures. Accepted for publication on IEEE Transactions on Signal Processing. arXiv admin note: text overlap with arXiv:2002.0488

    Approximating the Permanent with Fractional Belief Propagation

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    We discuss schemes for exact and approximate computations of permanents, and compare them with each other. Specifically, we analyze the Belief Propagation (BP) approach and its Fractional Belief Propagation (FBP) generalization for computing the permanent of a non-negative matrix. Known bounds and conjectures are verified in experiments, and some new theoretical relations, bounds and conjectures are proposed. The Fractional Free Energy (FFE) functional is parameterized by a scalar parameter γ[1;1]\gamma\in[-1;1], where γ=1\gamma=-1 corresponds to the BP limit and γ=1\gamma=1 corresponds to the exclusion principle (but ignoring perfect matching constraints) Mean-Field (MF) limit. FFE shows monotonicity and continuity with respect to γ\gamma. For every non-negative matrix, we define its special value γ[1;0]\gamma_*\in[-1;0] to be the γ\gamma for which the minimum of the γ\gamma-parameterized FFE functional is equal to the permanent of the matrix, where the lower and upper bounds of the γ\gamma-interval corresponds to respective bounds for the permanent. Our experimental analysis suggests that the distribution of γ\gamma_* varies for different ensembles but γ\gamma_* always lies within the [1;1/2][-1;-1/2] interval. Moreover, for all ensembles considered the behavior of γ\gamma_* is highly distinctive, offering an emprirical practical guidance for estimating permanents of non-negative matrices via the FFE approach.Comment: 42 pages, 14 figure
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