Solving large and sparse linear least-squares problems by conjugate gradient algorithms
Large and sparse least squares problems are transformed into equivalent problems which have a symmetric and positivedefinite matrix by a certain decomposition technique. The conjugate gradient algorithm (CG) is applied in the solution of the later problem. Numerical examples are performed with well-known test problems. The results illustrate that the CG algorithms applied to the modified problems is very efficient for some classes of problems.
scientific article; zbMATH DE number 4050860
scientific article