Search Results - algorithms in high dimensions

Improved Nelder-Mead algorithm in high dimensions with adaptive parameters based on Chebyshev spacing points by Mehta, V.K.

“… The work presented here concerns such values of the Nelder-Mead algorithm's parameters that help improve the convergence and success rate of the algorithm in high dimensions…”
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Algorithms for Numerical Analysis in High Dimensions by Beylkin, Gregory, Mohlenkamp, Martin J.

“…Nearly every numerical analysis algorithm has computational complexity that scales exponentially in the underlying physical dimension…”
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An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension by Marnissi, Yosra, Chouzenoux, Emilie, Benazza-Benyahia, Amel, Pesquet, Jean-Christophe

“… However, when the parameter space is high-dimensional, the performance of stochastic sampling algorithms is very sensitive to existing dependencies between parameters…”
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Population Monte Carlo Algorithm in High Dimensions by Lee, Jeong Eun, McVinish, Ross, Mengersen, Kerrie

“… We examine the population Monte Carlo algorithm in a simplified setting, a single step of the general algorithm, and study a fundamental problem that occurs in applying importance sampling to high-dimensional problem…”
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Bi-quadratic polynomial approach for global convergent algorithm in high dimensions coefficient inverse problems by Wang, Quan-Fang

“… Its application to 1D/2D dimension cases is reported in a great deal literatures. An interesting question should be asked is how about the development of the convex approach to high dimension case…”
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Online Principal Component Analysis in High Dimension: Which Algorithm to Choose? by Cardot, Hervé, Degras, David

“…Principal component analysis (PCA) is a method of choice for dimension reduction…”
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Convergence properties of quantum evolutionary algorithms on high dimension problems by Wright, Joe, Jordanov, Ivan

“…We propose and investigate new rotation gates for two modified Quantum Inspired Evolutionary methods for solving high dimension optimisation problems…”
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A self-stabilizing MSA algorithm in high-dimension data stream by Kong, Xiangyu, Hu, Changhua, Han, Chongzhao

“… In this paper, we propose a self-stabilizing neural network learning algorithm for tracking minor subspace in high-dimension data stream…”
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A Simple Algorithm for Maximal Poisson-Disk Sampling in High Dimensions by Ebeida, Mohamed S., Mitchell, Scott A., Patney, Anjul, Davidson, Andrew A., Owens, John D.

“… The serial algorithm is provably bias‐free. For an output sampling of size n in fixed dimension d, we use a linear memory budget and empirical θ(n) runtime…”
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Bypassing sluggishness: SWAP algorithm and glassiness in high dimensions by Berthier, Ludovic, Charbonneau, Patrick, Kundu, Joyjit

“…The recent implementation of a swap Monte Carlo algorithm (SWAP) for polydisperse glass forming mixtures bypasses computational sluggishness and closes the gap between experimental and simulation timescales in physical dimensions d=2 and 3…”
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GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration by Xia, Wei, Shoemaker, Christine

“…This paper describes a new parallel global surrogate-based algorithm Global Optimization in Parallel with Surrogate (GOPS…”
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An Improved Dung Beetle Optimization Algorithm for High-Dimension Optimization and Its Engineering Applications by Wang, Xu, Kang, Hongwei, Shen, Yong, Sun, Xingping, Chen, Qingyi

“… This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory…”
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A distributed algorithm for high-dimension convex quadratically constrained quadratic programs by Chen, Run, Liu, Andrew L.

“…), which arise from a broad range of applications. While small to medium-sized convex QCQPs can be solved efficiently by interior-point algorithms, high-dimension problems pose significant challenges to traditional algorithms that are mainly designed…”
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Time–frequency imaging algorithm for high-speed spinning targets in two dimensions by Li, J., Qiu, C.-W., Zhang, L., Xing, M., Bao, Z., Yeo, T.-S.

“…This study focuses on the narrow-band radar imaging for high-speed spinning targets…”
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Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions by Pillai, Natesh S

“…The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm that incorporates the gradient of the logarithm of the target density in its proposal distribution…”
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Reliability of structures in high dimensions, part I: algorithms and applications by Koutsourelakis, P.S., Pradlwarter, H.J., Schuëller, G.I.

“… The resulting algorithm exhibits accelerated convergence…”
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Bi-criteria sublinear time algorithms for clustering with outliers in high dimensions by Huang, Jiawei, Liu, Wenjie, Ding, Hu

“… The key theoretical innovation lies in the sample complexity being independent of both input size and dimensionality, making it particularly effective for large-scale and high-dimensional datasets…”
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Protein-protein correlations based variable dimension expansion algorithm for high efficient serum biomarker discovery by Xie, Juanjuan, Zhang, Lei, Chen, Zhangwei, Hu, Anqi, Liu, Shanshan, Lu, Danbo, Xia, Yan, Qian, Juying, Yang, Pengyuan, Shen, Huali

“…In this study, we constructed a high specific and efficient serum biomarker discovery pipeline…”
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Influence of Optimization of the k-Means Algorithm with Genetic Algorithm on the Results of High Dimension Data Clustering by Ramadhana, Yulinda, Jambak, Muhammad Ihsan

“… Clustering k-means with random initial centroids and with initial centroids from genetic algorithm calculations are each tested on the data with dimension reduction and without dimension reduction…”
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LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network by Zhou, Ning, Zhang, Jianhui, Wang, Binqiang, Xiao, Jia

“… However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks…”
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