An interior-point method for large constrained discrete ill-posed problems

Ill-posed problems are numerically underdetermined. It is therefore often beneficial to impose known properties of the desired solution, such as nonnegativity, during the solution process. This paper proposes the use of an interior-point method in conjunction with truncated iteration for the solution of large-scale linear discrete ill-posed problems with box constraints. An estimate of the error in the data is assumed to be available. Numerical examples demonstrate the competitiveness of this approach.