A new stable quadratic minimization method on a half-space is presented. In case of normally solvable operators this method outperforms approximate solutions having the same optimal order accuracy as earlier methods for unconstrained problems
We study a class of regularization methods for solving least-squares ill-posed problem with a convex constraint. Convergence and convergence rate results are proven for the problems which satisfy so called power source condition. All the results are obtained under the assumptions that, instead of exact initial data, only their approximations are known.
In this paper we present some bounds of an approximate solution to variational and quasi-variational inequalities. The measures of errors can be used for construction of iterative and continuous procedures for solving variational (quasi-variational) inequalities and formulation of corresponding stopping rules. We will also present some methods based on linearization for solving quasi-variational inequalities.
This paper deals with the existence of solutions and the conditions for the strong convergence of minimizing sequences towards the set of solutions of the quadratic function minimization problem on the intersection of two ellipsoids in Hilbert space
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