Convergence analysis of the Gauss–Newton method for convex inclusion and convex-composite optimization problems

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Convergence Analysis of the Gauss-newton Method for Convex Inclusion Problems and Convex Composite Optimization

Using the convex process theory we study the convergence issues of the iterative sequences generated by the Gauss-Newton method for the convex inclusion problem defined by a cone C and a Fréchet differentiable function F (the derivative is denoted by F ′). The restriction in our consideration is minimal and, even in the classical case (the initial point x0 is assumed to satisfy the following tw...

متن کامل

Majorizing Functions and Convergence of the Gauss--Newton Method for Convex Composite Optimization

We introduce a notion of quasi regularity for points with respect to the inclusion F (x) ∈ C, where F is a nonlinear Fréchet differentiable function from Rv to Rm. When C is the set of minimum points of a convex real-valued function h on Rm and F ′ satisfies the L-average Lipschitz condition of Wang, we use the majorizing function technique to establish the semilocal linear/quadratic convergenc...

متن کامل

On convergence of the Gauss-Newton method for convex composite optimization

The local quadratic convergence of the Gauss-Newton method for convex composite optimization f = h ◦ F is established for any convex function h with the minima set C, extending Burke and Ferris’ results in the case when C is a set of weak sharp minima for h.

متن کامل

Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems

We propose an adaptive parameter balancing scheme in a variational framework where a convex composite energy functional that consists of data fidelity and regularization is optimized. In our adaptive parameter balancing, the relative weight is assigned to each term of the energy for indicating its significance to the total energy, and is automatically determined based on the data fidelity measu...

متن کامل

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization

We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize the sum of composite convex functions, where each term in the sum is a private cost function belonging to a node, and only nodes connected by an edge can directly communicate with each other. This optimization model abstracts a number of applications in distributed sensing and machine learning. We show that a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Mathematical Analysis and Applications

سال: 2012

ISSN: 0022-247X

DOI: 10.1016/j.jmaa.2011.11.062