Globally Convergent Variable Metric Method for Convex Nonsmooth Unconstrained Minimization1
نویسندگان
چکیده
منابع مشابه
Globally Convergent Cutting Plane Method for Nonconvex Nonsmooth Minimization
Nowadays, solving nonsmooth (not necessarily differentiable) optimization problems plays a very important role in many areas of industrial applications. Most of the algorithms developed so far deal only with nonsmooth convex functions. In this paper, we propose a new algorithm for solving nonsmooth optimization problems that are not assumed to be convex. The algorithm combines the traditional c...
متن کاملA globally convergent descent method for nonsmooth variational inequalities
We propose a descent method via gap functions for solving nonsmooth variational inequalities with a locally Lipschitz operator. Assuming monotone operator (not necessarily strongly monotone) and bounded domain, we show that the method with an Armijo-type line search is globally convergent. Finally, we report some numerical experiments.
متن کاملRandom Perturbation of the Variable Metric Method for Unconstrained Nonsmooth Nonconvex Optimization
We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations is introduced. An algorithm is propos...
متن کاملNumerical infinitesimals in a variable metric method for convex nonsmooth optimization
The objective of the paper is to evaluate the impact of the infinity computing paradigm on practical solution of nonsmooth unconstrained optimization problems, where the objective function is assumed to be convex and not necessarily differentiable. For such family of problems, the occurrence of discontinuities in the derivatives may result in failures of the algorithms suited for smooth problem...
متن کاملConvergent Subgradient Methods for Nonsmooth Convex Minimization
In this paper, we develop new subgradient methods for solving nonsmooth convex optimization problems. These methods are the first ones, for which the whole sequence of test points is endowed with the worst-case performance guarantees. The new methods are derived from a relaxed estimating sequences condition, which allows reconstruction of the approximate primal-dual optimal solutions. Our metho...
متن کاملذخیره 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 Optimization Theory and Applications
سال: 1999
ISSN: 0022-3239,1573-2878
DOI: 10.1023/a:1022650107080