Convergence analysis of a parallel projection algorithm for solving convex feasibility problems
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Open access via publisher
Date
2016Type
Journal Article
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The convex feasibility problem (CFP) is a classical problem in nonlinear analysis. In this paper, we propose an inertial parallel projection algorithm for solving CFP. Different from the previous algorithms, the proposed method introduces a sequence of parameters and uses the information of last two iterations at each step. To prove its convergence in a simple way, we transform the parallel algorithm to a sequential one in a constructed product space. Preliminary experiments are conducted to demonstrate that the proposed approach converges faster than the general extrapolated algorithms.
Citation
Dang, Y. and Meng, F. and Sun, J. 2016. Convergence analysis of a parallel projection algorithm for solving convex feasibility problems. Numerical Algebra, Control and Optimization. 6 (4): pp. 505519.
Source Title
Numerical Algebra, Control and Optimization
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Department of Mathematics and Statistics
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