Randomized Kaczmarz method with adaptive stepsizes for inconsistent linear systems
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We investigate the randomized Kaczmarz method that adaptively updates the stepsize using readily available information for solving inconsistent linear systems. A novel geometric interpretation is provided which shows that the proposed method can be viewed as an orthogonal projection method in some sense. We prove that this method converges linearly in expectation to the unique minimum Euclidean norm least-squares solution of the linear system. Numerical experiments are given to illustrate the theoretical results.
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