Numerical Linear Algebra with Applications · 2020 · 11 citations · 21 references
ComputingComputer ScienceAlgorithmsOptimizationScientific ComputingConvergence AnalysisError EstimationIterative SolversMatrix ComputationNumerical AlgorithmsNumerical ConvergenceConvergenceError PropagationIterative Methods For Linear SystemsLeast Squares ProblemsLinear System SolversNoise PropagationRandomized Matrix AlgorithmsUpper Bounds
Summary The row iterative method is popular in solving the large‐scale ill‐posed problems due to its simplicity and efficiency. In this work we consider the randomized row iterative (RRI) method to tackle this issue. First, we present the semiconvergence analysis of RRI method for the overdetermined and inconsistent system, and derive upper bounds for the noise error propagation in the iteration vectors. To achieve a least squares solution, we then propose an extended version of the RRI (ERRI) method, which in fact can converge in expectation to the solution of the overdetermined or underdetermined, consistent or inconsistent systems. Finally, some numerical examples are given to demonstrate the convergence behaviors of the RRI and ERRI methods for these types of linear system.
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Improved Approximation Algorithms for Large Matrices via Random Projections
Tamás Sarlós · 2006 · 691 citations