Efficient Rank-Adaptive Least-Square Estimation and Multiple-Parameter Linear Regression Using Novel Dyadically Recursive Hermitian Matrix Inversion

Figure 1

Computational complexity comparison (model order 𝑁=2^𝑝 is fixed for one-time LSE or MLR) for Hermitian matrix inversion using conventional Cholesky factorization 𝑅(ℂ⁻¹_Chol(𝑁)) and our proposed new dyadic recursion 𝑅(ℂ⁻¹_new(𝑁)).