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

Figure 2

Computational complexity comparison (accumulated complexity for the sequential rank-adaptive LSE or MLR with terminal model order 𝑁=2^𝑚) for Hermitian matrix inversion using conventional Cholesky factorization 𝑅(ℂ⁻¹_Chol,seq(𝑁)) and our proposed new dyadic recursion 𝑅(ℂ⁻¹_new,seq(𝑁)).