Non-convex and non-smooth optimization plays an important role in machine learning. Proximal gradient method is one of the most important methods for solving the nonconvex and non-smooth problems, where a proximal operator need to be solved exactly for each step. However, in a lot of problems the proximal operator does not have an analytic solution, or is expensive to obtain an exact solution. ...

Agreement algorithms can be categorized in different ways. One categorization of such algorithms is based on whether the final decisions are exact or inexact. In inexact algorithms, also known as approximate agreement algorithms, each node produces its final decision that may not be necessarily the exact same decision value produced by a different non-faulty node. Most studies on such algorithm...

In this paper, we consider some nonlinear inexact Uzawa methods for iteratively solving linear saddle-point problems. By means of a new technique, we first give an essential improvement on the convergence results of Bramble-Paschiak-Vassilev for a known nonlinear inexact Uzawa algorithm. Then we propose two new algorithms, which can be viewed as a combination of the known nonlinear inexact Uzaw...

Inexact Newton regularization methods have been proposed by Hanke and Rieder for solving nonlinear ill-posed inverse problems. Every such a method consists of two components: an outer Newton iteration and an inner scheme providing increments by regularizing local linearized equations. The method is terminated by a discrepancy principle. In this paper we consider the inexact Newton regularizatio...

The parallel edge-based SUPG/PSPG finite element formulation applied to 3D steady incompressible Navier-Stokes equations is presented. The highly coupled velocitypressure nonlinear system of equations is solved with an inexact Newton-like method. The locally linear system of equations originated by the inexact nonlinear method is solved with a nodal block diagonal preconditioned GMRES solver. M...

Within the framework of affine scaling trust-region methods for bound constrained problems, we discuss the use of a inexact dogleg method as a tool for simultaneously handling the trust-region and the bound constraints while seeking for an approximate minimizer of the model. Focusing on bound-constrained systems of nonlinear equations, an inexact affine scaling method for large scale problems, ...

In this paper, we survey some of the latest development in using inexact Newton-like methods for solving inverse eigenvalue problems. These methods require the solutions of nonsymmetric and large linear systems. One can solve the approximate Jacobian equation by iterative methods. However, iterative methods usually oversolve the problem in the sense that they require far more (inner) iterations...

The semismooth Newton method is a nonsmooth Newton-type method applied to a suitable reformulation of the complementarity problem as a nonlinear and nonsmooth system of equations. It is one of the standard methods for solving these kind of problems, and it can be implemented in an inexact way so that all linear systems of equations have to be solved only inexactly. However, from a practical poi...

A local convergence analysis of Inexact Newton’s method with relative residual error tolerance for finding a singularity of a differentiable vector field defined on a complete Riemannian manifold, based on majorant principle, is presented in this paper. We prove that under local assumptions, the inexact Newton method with a fixed relative residual error tolerance converges Q -linearly to a sing...

In this paper we study the global convergence behavior of a class of composite–step trust–region SQP methods that allow inexact problem information. The inexact problem information can result from iterative linear systems solves within the trust–region SQP method or from approximations of first–order derivatives. Accuracy requirements in our trust– region SQP methods are adjusted based on feasi...