Gauss quadrature rules are essential for numerical integration, especially in high-dimensional spaces. Traditional methods for computing these rules become computationally expensive and inefficient as the dimensionality increases. This article presents a novel fast algorithm for computing high-dimensional Gauss quadrature rules, significantly reducing computational complexity and improving efficiency. The proposed method leverages sparse grids, tensor decompositions, and adaptive strategies to handle the curse of dimensionality effectively.
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Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report
