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**Iterative methods for the split feasibility problem in infinite-dimensional Hilbert spaces.**
*(English)*
Zbl 1213.65085

The author discusses iterative methods for solving the split feasibility problem (SFP), i.e., to find a point \(x^*\) in a infinite-dimensional Hilbert space \(\mathcal H_1\) such that \(x^* \in C\) and \(Ax^*\in Q\). Here \(C\) and \(Q\) are nonempty closed convex subsets of \(\mathcal H_1\), and \(A\) is a bounded linear operator from \(\mathcal H_1\) into another infinite-dimensional Hilbert space \(\mathcal H_2\). Such problems have been considered as models in phase retrievals, medical image reconstruction and recently also in the intensity-modulated radiation therapy.

In this paper the SFP problem is reformulated as a minimization problem to enable approximations via gradient-projection methods. Equivalently, the SFP problem is also considered as a fixed point equation so that iterative methods with adaptive relaxation factors can be used to approximate the solution as a fixed point. Both weak convergence to the original solution and strong convergence to the regularized solution are established.

In this paper the SFP problem is reformulated as a minimization problem to enable approximations via gradient-projection methods. Equivalently, the SFP problem is also considered as a fixed point equation so that iterative methods with adaptive relaxation factors can be used to approximate the solution as a fixed point. Both weak convergence to the original solution and strong convergence to the regularized solution are established.

Reviewer: Zhen Mei (Toronto)

### MSC:

65J22 | Numerical solution to inverse problems in abstract spaces |

47J25 | Iterative procedures involving nonlinear operators |

47J06 | Nonlinear ill-posed problems |

49N45 | Inverse problems in optimal control |

15A29 | Inverse problems in linear algebra |