A Lavrentiev prox-regularization method for optimal control problems with pointwise state constraints is introduced where both the objective function and the constraints are regularized. The convergence of the controls generated by the iterative Lavrentiev prox-regularization algorithm is studied. For a sequence of regularization parameters that converges to zero, strong convergence of the gene...

Image restoration is an ill-posed inverse problem, which has been introduced the regularization method to suppress over-amplification. In this paper, we propose to apply the iterative regularization method to the image restoration problem and present a nested iterative method, called iterative conjugate gradient regularization method. Convergence properties are established in detail. Based on [...

Edge detection attempts to reconstruct 3-D physical edges from a 2-D image. It is therefore an ill-posed problem, and a regularization procedure is required to convert it into a well-posed problem. This procedure introduces a regularization parameter for adjusting the extent of the regularization eeect: the larger the value of is, the stronger the regularization eeect. Therefore, could be inter...

Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficiently. However, the accuracy of such methods can be very sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. Herein, we propose a maximum a posteriori prob...