Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods...

As a continuation of previous work of the first author with S. Ranjbar [26] on a special form of variational inequalities in Hadamard spaces, in this paper we study equilibrium problems in Hadamard spaces, which extend variational inequalities and many other problems in nonlinear analysis. In this paper, first we study the existence of solutions of equilibrium problems associated with pseudomon...

In this paper, we study the iterations of quasi $phi$-nonexpansive mappings and its applications in Banach spaces. At the first, we prove strong convergence of the sequence generated by the hybrid proximal point method to a common fixed point of a family of quasi $phi$-nonexpansive mappings.  Then, we give  applications of our main results in equilibrium problems.

In this note, a small gap is corrected in the proof of H.K. Xu [Theorem 3.3, A regularizationmethod for the proximal point algorithm, J. Glob. Optim. 36, 115–125 (2006)], and some strict restriction is removed also.

In the literature, there are a few researches for the proximal point algorithm (PPA) with some parameters designed in the metric proximal matrix, especially for the multi-objective optimization problems. Introducing some parameters to the PPA can make it more attractive and flexible. By using the unified framework of the classical PPA and constructing a parameterized proximal matrix, in this pa...

The MM principle is a device for creating optimization algorithms satisfying the ascent or descent property. The current survey emphasizes the role of the MM principle in nonlinear programming. For smooth functions, one can construct an adaptive interior point method based on scaled Bregman barriers. This algorithm does not follow the central path. For convex programming subject to nonsmooth co...

In this paper, we study the low-rank matrix minimization problem, where loss function is convex but nonsmooth and penalty term defined by cardinality function. We first introduce an exact continuous relaxation, that is, both problems have same minimizers optimal value. particular, a class of lifted stationary points relaxed problem show any local minimizer must be point. addition, derive lower ...

We study the convergence of the proximal algorithm applied to nonsmooth functions that satisfy the Lojasiewicz inequality around their generalized critical points. Typical examples of functions complying with these conditions are continuous semialgebraic or subanalytic functions. Following Lojasiewicz’s original idea, we prove that any bounded sequence generated by the proximal algorithm conver...

In this paper, proximal point algorithms for nonexpansive (sequences of nonexpansive) maps and maximal monotone operators are studied. A modification of Xu’s algorithm is given and a strong convergence result associated with it is proved when the error sequence is in `p for 1 ≤ p < 2. We also propose some other modifications of the celebrated Rockafellar’s algorithm which generate weak or stron...