对称正定流形上低复杂度子空间下降（英文版）

英文标题：Low-complexity subspace-descent over symmetric positive definite manifold中文摘要：提出了一种低复杂度的黎曼子空间下降算法，通过利用选择的子空间，并将更新写为迭代点的 Cholesky 因子和一个稀疏矩阵的乘积，避免了矩阵幂运算和密集矩阵乘法，能够高效地计算黎曼梯度，特别适用于协方差估计等领域。英文摘要：This work puts forth low-complexity Riemannian subspace descent algorithmsfor the minimization of functions over the symmetric positive definite (SPD)manifold. Different from the existing Riemannian gradient descent variants, theproposed approach utilizes carefully chosen subspaces that allow the update