In dimension one it is proved that the solution to a total variation-regularized least-squares problem is always a function which is "constant almost everywhere" , provided that the data are in a certain sense outside the range of the operator to be inverted. A similar, but weaker result is derived in dimension two.
We present a detailed proof of the density of the set in the space of test functions that vanish on some part of the boundary of a bounded domain .
In this paper we present a new hybrid method, called SASP method. We propose the hybridization of two methods, the simulated annealing (SA), which belong to the class of global optimization based on the principles of thermodynamics, and the descent method were we estimate the gradient using the simultaneous perturbation. This hybrid method gives better results. We use the Normal Boundary Intersection approach (NBI) based on the SASP method to solve...