351 citations · 1.2k across the 14 of their papers we have counts for
16 papers
Stochastic Approximation for Expectation Objective and Expectation Inequality-Constrained Nonconvex Optimization
Francisco Facchinei, Vyacheslav Kungurtsev
Stochastic Approximation has been a prominent set of tools for solving problems with noise and uncertainty. Increasingly, it becomes important to solve optimization problems wherei…
Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions
Loris Cannelli, Francisco Facchinei, Gesualdo Scutari +1
We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is know…
Diminishing Stepsize Methods for Nonconvex Composite Problems via Ghost Penalties: from the General to the Convex Regular Constrained Case
Francisco Facchinei, Vyacheskav Kungurtsevb, Lorenzo Lampariello +1
In this paper we first extend the diminishing stepsize method for nonconvex constrained problems presented in [4] to deal with equality constraints and a nonsmooth objective functi…
Decentralized Dictionary Learning Over Time-Varying Digraphs
Amir Daneshmand, Ying Sun, Gesualdo Scutari +2
This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph. This formulation is r…
Ghost Penalties in Nonconvex Constrained Optimization: Diminishing Stepsizes and Iteration Complexity
Francisco Facchinei, Vyacheslav Kungurtsev, Lorenzo Lampariello +1
We consider nonconvex constrained optimization problems and propose a new approach to the convergence analysis based on penalty functions. We make use of classical penalty function…
Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization. Part II: Complexity and Numerical Results
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev +1
We present complexity and numerical results for a new asynchronous parallel algorithmic method for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth…
Distributed Dictionary Learning
Amir Daneshmand, Gesualdo Scutari, Francisco Facchinei
The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. Thi…
Asynchronous Parallel Algorithms for Nonconvex Optimization
Loris Cannelli, Francisco Facchinei, Vyacheslav Kungurtsev +1
We propose a new asynchronous parallel block-descent algorithmic framework for the minimization of the sum of a smooth nonconvex function and a nonsmooth convex one, subject to bot…
Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applications
Gesualdo Scutari, Francisco Facchinei, Lorenzo Lampariello +2
In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex (smooth) objective function, subject to nonconvex constraints, b…
Parallel and Distributed Methods for Nonconvex Optimization-Part I: Theory
Gesualdo Scutari, Francisco Facchinei, Lorenzo Lampariello +1
In this two-part paper, we propose a general algorithmic framework for the minimization of a nonconvex smooth function subject to nonconvex smooth constraints. The algorithm solves…
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…
Flexible Parallel Algorithms for Big Data Optimization
Francisco Facchinei, Simone Sagratella, Gesualdo Scutari
We propose a decomposition framework for the parallel optimization of the sum of a differentiable function and a (block) separable nonsmooth, convex one. The latter term is typical…
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
Gesualdo Scutari, Francisco Facchinei, Peiran Song +2
We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multiuser…
Real and Complex Monotone Communication Games
Gesualdo Scutari, Francisco Facchinei, Jong-Shi Pang +1
Noncooperative game-theoretic tools have been increasingly used to study many important resource allocation problems in communications, networking, smart grids, and portfolio optim…
Distributed Power Allocation with Rate Constraints in Gaussian Parallel Interference Channels
Jong-Shi Pang, Gesualdo Scutari, Francisco Facchinei +1
This paper considers the minimization of transmit power in Gaussian parallel interference channels, subject to a rate constraint for each user. To derive decentralized solutions th…