We describe several features of parallel or distributed asynchronous iterative algorithms such as unbounded delays, possible out of order messages or flexible communication. We concentrate on the concept of macroiteration sequence which was introduced in order to study the convergence or termination of asynchronous iterations. A survey of asynchronous iterations for convex optimization problems is also presented. Finally, a new result of convergence for parallel or distributed asynchronous iterative algorithms with flexible communication for convex optimization problems and machine learning is proposed
Bibliography: leaf 4."November, 1983." Caption title.ONR contract ONR/N00014-77-C-0532 (NR-041-519)b...
We present a parallelized primal-dual algorithm for solving constrained convex optimization problems...
In many large-scale optimization problems arising in the context of machine learning the decision va...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
This thesis proposes and analyzes several first-order methods for convex optimization, designed for ...
International audienceWe develop and analyze an asynchronous algorithm for distributed convex optimi...
International audienceWe develop and analyze an asynchronous algorithm for distributed convex optimi...
We develop and analyze an asynchronous algorithm for distributed convex optimization when the object...
We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of t...
AbstractThe concept of flexible communication permits one to model efficient asynchronous iterations...
We introduce novel convergence results for asynchronous iterations which appear in the analysis of p...
Two characteristics that make convex decomposition algorithms attractive are simplicity of operation...
Bibliography: p. 28-29."November 1984."" ONR/N00014-77-C-532" " NSF-ECS-8217668"John N. Tsitsiklis, ...
Bibliography: leaf 4."November, 1983." Caption title.ONR contract ONR/N00014-77-C-0532 (NR-041-519)b...
We present a parallelized primal-dual algorithm for solving constrained convex optimization problems...
In many large-scale optimization problems arising in the context of machine learning the decision va...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
International audienceWe describe several features of parallel or distributed asynchronous iterative...
This thesis proposes and analyzes several first-order methods for convex optimization, designed for ...
International audienceWe develop and analyze an asynchronous algorithm for distributed convex optimi...
International audienceWe develop and analyze an asynchronous algorithm for distributed convex optimi...
We develop and analyze an asynchronous algorithm for distributed convex optimization when the object...
We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of t...
AbstractThe concept of flexible communication permits one to model efficient asynchronous iterations...
We introduce novel convergence results for asynchronous iterations which appear in the analysis of p...
Two characteristics that make convex decomposition algorithms attractive are simplicity of operation...
Bibliography: p. 28-29."November 1984."" ONR/N00014-77-C-532" " NSF-ECS-8217668"John N. Tsitsiklis, ...
Bibliography: leaf 4."November, 1983." Caption title.ONR contract ONR/N00014-77-C-0532 (NR-041-519)b...
We present a parallelized primal-dual algorithm for solving constrained convex optimization problems...
In many large-scale optimization problems arising in the context of machine learning the decision va...