:laughing:

Matrix mulitply python a1 = np.arange(2) # [0,1] (2,) a2 = np.arange(6).reshape(2,3) # [[1,2,3][4,5,6]] (2,3) a1@a2 > array([3, 4, 5]) # (3,) a2[None,:]@b > array([[3, 4, 5]]) # (1,3) a1 * a2 >array([[0, 3], [0, 4], [0, 5]]) Q: what's the order of a a2 @ a2.T: a1 a2 @ a2.T = (a1 * a2 ) @ a2.T 实质，将系数a1平均分给a2的列向量上 Julia CPP Cholesky Decompostion The cholesky decompostion is exclusively defined for symmetric or Hermitian positive definite matrices, A = LL*. Python: 1. chol_xx_cov = np.linalg.cholesky(x_cov) 2. s1,v1,d1 = np.linalg.svd(x_cov) v1 = np.clip(v1,a_min = 1e-8,a_max = None) q1 = np.linalg.qr(np.sqrt(v1)[:,None] * d1)[-1] chol_xx_cov = q1.T Julia: CPP