``Efficient” Subgradient Methods for General Convex Optimization
نویسندگان
چکیده
منابع مشابه
"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 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 کاملRandomized Block Subgradient Methods for Convex Nonsmooth and Stochastic Optimization
Block coordinate descent methods and stochastic subgradient methods have been extensively studied in optimization and machine learning. By combining randomized block sampling with stochastic subgradient methods based on dual averaging ([22, 36]), we present stochastic block dual averaging (SBDA)—a novel class of block subgradient methods for convex nonsmooth and stochastic optimization. SBDA re...
متن کاملMirror descent and nonlinear projected subgradient methods for convex optimization
The mirror descent algorithm (MDA) was introduced by Nemirovsky and Yudin for solving convex optimization problems. This method exhibits an e3ciency estimate that is mildly dependent in the decision variables dimension, and thus suitable for solving very large scale optimization problems. We present a new derivation and analysis of this algorithm. We show that the MDA can be viewed as a nonline...
متن کاملInexact 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 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: SIAM Journal on Optimization
سال: 2016
ISSN: 1052-6234,1095-7189
DOI: 10.1137/15m1027371