In this work, we propose a modified inexact Levenberg–Marquardt method with the descent
property for solving nonlinear equations. A novel feature of the proposed method is that one …

In the present paper, we investigate a linearized proximal algorithm (LPA) for solving a
convex composite optimization problem. Each iteration of the LPA is a proximal minimization …

This paper is devoted to the class of paraconvex functions and presents some of its
fundamental properties, characterization, and examples that can be used for their …

We propose an inexact linearized proximal algorithm with an adaptive stepsize, together
with its globalized version based on the backtracking line-search, to solve a convex …

Quasi-convex optimization acts a pivotal part in many fields including economics and
finance; the subgradient method is an effective iterative algorithm for solving large-scale …

In this paper, we propose a stochastic subgradient method to solve a nondifferentiable
constrained quasi-convex optimization problem. A unit noisy (unbiased) quasi-subgradient …

The feasibility problem is at the core of the modeling of many problems in various areas, and
the quasi-convex function usually provides a precise representation of reality in many fields …

In this paper, a subgradient projection method for quasiconvex minimization problems is
provided. By employing strong subdifferentials, it is proved that the generated sequence of …

We study properties and algorithms of a minimization problem of the maximum generalized
eigenvalue of symmetric-matrix-valued affine functions, which is nonsmooth and …

In this paper, we consider a type of the celebrated convex feasibility problem, named as split
quasi-convex feasibility problem (SQFP). The SQFP is to find a point in a sublevel set of a …