Papers Distributed Optimization
“Distributed Optimization” 태그가 달린 논문 601편 · 필터 해제
Fast Adaptive Federated Bilevel Optimization
Bilevel optimization is a popular hierarchical model in machine learning, and has been widely applied to many machine learning tasks such as meta learning, hyperparameter learning and policy optimization. Although many b…
Bilevel OptimizationDistributed OptimizationMeta-LearningRepresentation LearningGradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing clients to perform multiple local gradient-type training steps before communication. In a recent breakthr…
Common Sense ReasoningDistributed OptimizationProvably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression
In federated learning, a large number of users are involved in a global learning task, in a collaborative way. They alternate local computations and two-way communication with a distant orchestrating server. Communicatio…
Distributed OptimizationFederated LearningDistributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the…
Distributed OptimizationModel Predictive ControlHybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some c…
Distributed OptimizationShuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems outperforming classical approaches. To gain such computational ad…
Distributed OptimizationCooperative Tuning of Multi-Agent Optimal Control Systems
This paper investigates the problem of cooperative tuning of multi-agent optimal control systems, where a network of agents (i.e. multiple coupled optimal control systems) adjusts parameters in their dynamics, objective …
Distributed OptimizationDistributed CPU Scheduling Subject to Nonlinear Constraints
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in te…
CPUDistributed OptimizationQuantizationSchedulingReal-Time Distributed Model Predictive Control with Limited Communication Data Rates
The application of distributed model predictive controllers (DMPC) for multi-agent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This w…
Distributed OptimizationModel Predictive ControlQuantizationDecentralized Optimization with Distributed Features and Non-Smooth Objective Functions
We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions. Such learning problems are not separable, i.e., the associated obj…
Distributed OptimizationConsensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs
This paper investigates the distributed Kalman filtering (DKF) from distributed optimization viewpoint. Motivated by the fact that Kalman filtering is a maximum a posteriori estimation (MAP) problem, which is a quadratic…
Distributed OptimizationMulti-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets
Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the unknown other generatio…
Deep Reinforcement LearningDistributed OptimizationMulti-agent Reinforcement Learningreinforcement-learning+1Coordinating Flexible Ramping Products with Dynamics of the Natural Gas Network
In electricity networks with high penetration levels of renewable resources, Flexible Ramping Products (FRPs) are among the utilized measures for dealing with the potential fluctuations in the net demand. This paper inve…
Distributed OptimizationPrivacy PreservingConvergence Theory of Generalized Distributed Subgradient Method with Random Quantization
The distributed subgradient method (DSG) is a widely discussed algorithm to cope with large-scale distributed optimization problems in the arising machine learning applications. Most exisiting works on DSG focus on ideal…
Distributed OptimizationQuantizationOnline Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline. Unlike many standa…
Distributed OptimizationModel Predictive ControlDistributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems
This paper considers the problem of characterizing the stability region of a large-scale networked system comprised of dissipative nonlinear subsystems, in a distributed and computationally tractable way. One standard ap…
Distributed OptimizationVariance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on the clients before parameter averaging. …
Distributed OptimizationFederated LearningSimultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet. We present an efficient motion planning framework f…
Distributed OptimizationMotion PlanningCan Competition Outperform Collaboration? The Role of Misbehaving Agents
We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave. In contrast to commonly adopted filtering str…
Distributed OptimizationOn the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network
Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most existing algorithms restrict …
BIG-bench Machine LearningBilevel OptimizationDistributed Optimization