Publications related to Splitting Methods for Distributed Optimization and Control

Control and Optimization of Batch Chemical Processes

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch index. Accordingly, the control and optim ...

Butterworth-Heinemann2017

Asynchronous updates for stochastic gradient descent

Alberto Silvio Chiappa

Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...

2017

Interior Point Decomposition for Multi-Agent Optimization

Colin Neil Jones, Altug Bitlislioglu, Ivan Pejcic

In this paper we present the application of the interior-point decomposition (IPD) method, which was originally formulated for stochastic programming, to optimization problems involving multiple agents that are coupled through constraints and objectives. I ...

2017

Decentralized exact coupled optimization

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

This work develops an exact converging algorithm for the solution of a distributed optimization problem with partially-coupled parameters across agents in a multi-agent scenario. In this formulation, while the network performance is dependent on a collecti ...

2017

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Colin Neil Jones, Milan Korda, Georgios Stathopoulos

This work presents an algorithmic scheme for solving the infinite-time constrained linear quadratic regulation problem. We employ an accelerated version of a popular proximal gradient scheme, commonly known as the Forward-Backward Splitting (FBS), and prov ...

Institute of Electrical and Electronics Engineers2017

Distributed Optimization with Arbitrary Local Solvers

Martin Jaggi, Chenxin Ma

With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing high ...

Taylor & Francis Ltd2017

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic

The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...

2016

Memetic Viability Evolution for Constrained Optimization

Dario Floreano, Andrea Maesani, Giovanni Iacca

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

We discuss optimal load sharing of parallel gas compressors in the presence of plant-model mismatch. We formulate this problem as a static real-time optimization task and propose to tackle it by means of modifier adaptation. Under mild assumptions, the cho ...

Ieee2016

Splitting Methods for Distributed Optimization and Control

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Control and Optimization of Batch Chemical Processes

Asynchronous updates for stochastic gradient descent

Interior Point Decomposition for Multi-Agent Optimization

Decentralized exact coupled optimization

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Distributed Optimization with Arbitrary Local Solvers

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Memetic Viability Evolution for Constrained Optimization

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation

Asynchronous updates for stochastic gradient descent

Control and Optimization of Batch Chemical Processes

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Decentralized exact coupled optimization

Interior Point Decomposition for Multi-Agent Optimization

Memetic Viability Evolution for Constrained Optimization

Distributed Optimization with Arbitrary Local Solvers

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Optimal Load Sharing of Parallel Compressors via Modifier Adaptation