Inexact subgradient methods for quasi-convex optimization problems
نویسندگان
چکیده
منابع مشابه
Inexact subgradient methods for quasi-convex optimization problems
In this paper, we consider a generic inexact subgradient algorithm to solve a nondifferentiable quasi-convex constrained optimization problem. The inexactness stems from computation errors and noise, which come from practical considerations and applications. Assuming that the computational errors and noise are deterministic and bounded, we study the effect of the inexactness on the subgradient ...
متن کامل"Efficient" Subgradient Methods for General Convex Optimization
A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified error of optimality. Feasibility is maintained with a linesearch at each iteration, avoiding the need for orthogonal projections onto the feasible region (an ...
متن کاملPrimal-dual subgradient methods for convex problems
In this paper we present a new approach for constructing subgradient schemes for different types of nonsmooth problems with convex structure. Our methods are primaldual since they are always able to generate a feasible approximation to the optimum of an appropriately formulated dual problem. Besides other advantages, this useful feature provides the methods with a reliable stopping criterion. T...
متن کاملSubgradient methods for huge-scale optimization problems
We consider a new class of huge-scale problems, the problems with sparse subgradients. The most important functions of this type are piece-wise linear. For optimization problems with uniform sparsity of corresponding linear operators, we suggest a very efficient implementation of subgradient iterations, which total cost depends logarithmically in the dimension. This technique is based on a recu...
متن کاملSubgradient methods for convex minimization
Many optimization problems arising in various applications require minimization of an objective cost function that is convex but not di erentiable. Such a minimization arises, for example, in model construction, system identi cation, neural networks, pattern classi cation, and various assignment, scheduling, and allocation problems. To solve convex but not di erentiable problems, we have to emp...
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: European Journal of Operational Research
سال: 2015
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2014.05.017