Ill-posed linear inverse problems with box constraints: A new convex optimization approach

Consider the linear equation $\mathbf{A}\mathbf{x}=\mathbf{y}$, where $\mathbf{A}$ is a $k\times N$-matrix, $\mathbf{x}\in \mathcal{K}\subset {\mathbb{R}}^N$ and $\mathbf{y}\in {\mathbb{R}}^M$ a given vector. When $\mathcal{K}$ is a convex set and $M≠N$ this is a typical ill-posed, linear inverse problem with convex constraints. Here we propose a new way to solve this problem when $\mathcal{K}={\prod }_j[a_j,b_j]$. It consists of regarding $\mathbf{A}\mathbf{x}=\mathbf{y}$ as the constraint of a convex minimization problem, in which the objective (cost) function is the dual of a moment generating function. This leads to a nice minimization problem and some interesting comparison results. More importantly, the method provides a solution that lies in the interior of the constraint set $\mathcal{K}$. We also analyze the dependence of the solution on the data and relate it to the Le Chatellier principle.