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Interior Point Decomposition for Multi-Agent Optimization

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Dual Coupled Diffusion for Distributed Optimization with Affine Constraints

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

In this work, a distributed multi-agent optimization problem is studied where different subsets of agents are coupled with each other through affine constraints. Moreover, each agent is only aware of its own contribution to the constraints and only knows w ...

IEEE2018

Distributed Optimization and Control using Operator Splitting Methods

Georgios Stathopoulos

The significant progress that has been made in recent years both in hardware implementations and in numerical computing has rendered real-time optimization-based control a viable option when it comes to advanced industrial applications. At the same time, t ...

EPFL2018

Distributed Coupled Learning Over Adaptive Networks

Ali H. Sayed, Sulaiman A S A E Alghunaim

This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discov ...

IEEE2018

We propose a way to estimate the value function of a convex proximal minimization problem. The scheme constructs a convex set within which the optimizer resides and iteratively refines the set every time that the value function is sampled, namely every tim ...

2018

A coordinator-driven communication reduction scheme for distributed optimization using the projected gradient method

Colin Neil Jones, Georgios Stathopoulos

IEEE2018

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We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network in which the agents communicate with their neighbors and perform local computation. In the proposed algorithm, each agent can compute and comm ...

2018

Coalitional Control for Self-Organizing Agents

Coalitional control is concerned with the management of multi-agent systems where cooperation cannot be taken for granted (due to, e.g., market competition, logistics). This paper proposes a model predictive control (MPC) framework aimed at large-scale dyn ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Service Restoration in DG-Integrated Distribution Networks Using an Exact Convex OPF Model

Mario Paolone, Mostafa Nick, Hossein Sekhavatmanesh

The ever-increasing requirements for reliability and quality of supply suggest to enable the self-healing features of modern distribution networks. Within the context of Active Distribution Networks (ADNs), new, fast and efficient restoration strategies ca ...

Power Systems Computation Conference2018

Service Restoration in DG-Integrated Distribution Networks Using an Exact Convex OPF Model

Mario Paolone, Mostafa Nick, Hossein Sekhavatmanesh

IEEE2018

Sparsified SGD with Memory

Martin Jaggi, Sebastian Urban Stich, Jean-Baptiste Francis Marie Juliette Cordonnier

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. ...

2018