| Article ID: | iaor20116946 |
| Volume: | 150 |
| Issue: | 2 |
| Start Page Number: | 360 |
| End Page Number: | 378 |
| Publication Date: | Aug 2011 |
| Journal: | Journal of Optimization Theory and Applications |
| Authors: | Xu Hong-Kun |
| Keywords: | programming: convex |
It is well known that the gradient‐projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this article, we first provide an alternative averaged mapping approach to the GPA. This approach is operator‐oriented in nature. Since, in general, in infinite‐dimensional Hilbert spaces, GPA has only weak convergence, we provide two modifications of GPA so that strong convergence is guaranteed. Regularization is also applied to find the minimum‐norm solution of the minimization problem under investigation.