Almost Sure Convergence of the Kaczmarz Algorithm with Random Measurements

The Kaczmarz algorithm is an iterative method for reconstructing a signal x∈ℝd from an overcomplete collection of linear measurements yn=〈x,φn〉, n≥1. We prove quantitative bounds on the rate of almost sure exponential convergence in the Kaczmarz algorithm for suitable classes of random measurement vectors $\{\varphi_{n}\}_{n=1}^{\infty} \subset {\mathbb {R}}^{d}$. Refined convergence results are given for the special case when each φn has i.i.d. Gaussian entries and, more generally, when each φn/∥φn∥ is uniformly distributed on $\mathbb{S}^{d-1}$. This work on almost sure convergence complements the mean squared error analysis of Strohmer and Vershynin for randomized versions of the Kaczmarz algorithm.

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