Regularization Using a Parameterized Trust Region Subproblem

We present a new method for regularization of ill-conditioned problems that extends the traditional trust-region approach. Ill-conditioned problems arise, for example, in image restoration or mathematical processing of medical data, and involve matrices that are very ill-conditioned. The method makes use of the L-curve and L-curve maximum curvature criterion as a strategy recently proposed to find a good regularization parameter. We describe the method and show its application to an image restoration problem. We also provide a MATLAB code for the algorithm. Finally, a comparison to the CGLS approach is given and analyzed, and future research directions are proposed

Multi-Stage Investment Decision under Contingent Demand for Networking Planning

Telecommunication companies, such as Internet and cellular service providers, are seeing rapid and uncertain growth of traffic routed through their networks. It has become a challenge for these companies to make optimal decisions for equipment purchase that simultaneously satisfy the uncertain future demand while minimizing investment cost. This pape