928 citations · 3.8k across the 59 of their papers we have counts for
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Distributed Big-Data Optimization via Block Communications
Ivano Notarnicola, Ying Sun, Gesualdo Scutari +1
We study distributed multi-agent large-scale optimization problems, wherein the cost function is composed of a smooth possibly nonconvex sum-utility plus a DC (Difference-of-Convex…
Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging
Ivano Notarnicola, Ying Sun, Gesualdo Scutari +1
In this paper, we study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex…
Distributed Nonconvex Multiagent Optimization Over Time-Varying Networks
Ying Sun, Gesualdo Scutari, Daniel Palomar
We study nonconvex distributed optimization in multiagent networks where the communications between nodes is modeled as a time-varying sequence of arbitrary digraphs. We introduce…
NEXT: In-Network Nonconvex Optimization
Paolo Di Lorenzo, Gesualdo Scutari
We study nonconvex distributed optimization in multi-agent networks with time-varying (nonsymmetric) connectivity. We introduce the first algorithmic framework for the distributed…
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization
Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev +1
We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex on…
Parallel Selective Algorithms for Big Data Optimization
Francisco Facchinei, Gesualdo Scutari, Simone Sagratella
We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The l…