We consider the linearly constrained separable convex minimization problem whose
objective function is separable into m individual convex functions with nonoverlapping …

The total variation (TV) model is attractive in that it is able to preserve sharp attributes in
images. However, the restored images from TV-based methods do not usually stay in a …

This paper focuses on some customized applications of the proximal point algorithm (PPA)
to two classes of problems: the convex minimization problem with linear constraints and a …

Network coordinate (NC) systems provide a lightweight and scalable way for predicting the
distances, ie, round-trip latencies among Internet hosts. Most existing NC systems embed …

Image restoration is the operation of obtaining a high-quality image from a corrupt/noisy
image and is widely used in many applications such as Magnetic Resonance Imaging (MRI) …

We propose two approximate versions of the first-order primal-dual algorithm (PDA) to solve
a class of convex-concave saddle point problems. The introduced approximate criteria are …

In this paper, we consider the problem of image restoration with Tikhonov regularization as a
convex constrained minimization problem. Using a Kronecker decomposition of the blurring …

Image restoration is a computationally intensive problem as a large number of pixel values
have to be determined. Since the pixel values of digital images can attain only a finite …

In this paper, we study a first-order inexact primal-dual algorithm (I-PDA) for solving a class
of convex-concave saddle point problems. The I-PDA, which involves a relative error …

Monotonicity and nonmonotonicity play a key role in studying the global convergence and
the efficiency of iterative schemes employed in the field of nonlinear optimization, where …