Publications related to Control and Optimization of Batch Processes

Related publications (18)

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

EPFL2025

Advancing understanding and practical performance of machine learning interatomic potentials

Sergey Pozdnyakov

Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

EPFL2025

Efficient Algorithms for a Class of Stochastic Hidden Convex Optimization and Its Applications in Network Revenue Management

Yifan Hu

We study a class of stochastic nonconvex optimization in the form of min(x is an element of X) F(x) := E-xi[f (phi(x, xi))], that is, F is a composition of a convex function f and a random function phi. Leveraging an (implicit) convex reformulation via a v ...

INFORMS2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

Rui Yao

Deep neural networks (DNNs) have been increasingly applied in travel demand modeling because of their automatic feature learning, high predictive performance, and economic interpretability. Nevertheless, DNNs frequently present behaviorally irregular patte ...

PERGAMON-ELSEVIER SCIENCE LTD2024

Price Discrimination with Robust Beliefs

Thomas Alois Weber, Jun Han

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

Elsevier2023

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

IEEE2022

On Linear Optimization over Wasserstein Balls

Daniel Kuhn, Wolfram Wiesemann

Wasserstein balls, which contain all probability measures within a pre-specified Wasserstein distance to a reference measure, have recently enjoyed wide popularity in the distributionally robust optimization and machine learning communities to formulate an ...

2022

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Sadegh Farhadkhani, John Stephan

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

PMLR2022

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

Emmanuel Abbé

It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

ASSOC COMPUTING MACHINERY2022

MILP-based discrete sizing and topology optimization of truss structures: new formulation and benchmarking

Corentin Jean Dominique Fivet, Gennaro Senatore, Jan Friedrich Georg Brütting

Discrete sizing and topology optimization of truss structures subject to stress and displacement constraints has been formulated as a Mixed-Integer Linear Programming (MILP) problem. The computation time to solve a MILP problem to global optimality via a b ...

2022

A unified framework for Hamiltonian deep neural networks

Giancarlo Ferrari Trecate, Liang Xu, Clara Lucía Galimberti

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Plug-and-play Voltage Stabilization in Inverter-interfaced Microgrids via a Robust Control Strategy

Alireza Karimi

This paper proposes a decentralized control strategy for the voltage regulation of islanded inverter-interfaced micro- grids. We show that an inverter-interfaced microgrid under plug- and-play (PnP) functionality of distributed generations (DGs) can be cas ...

Institute of Electrical and Electronics Engineers2017

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

François Maréchal, Maziar Kermani

An optimization methodology based on Mixed Integer Linear Programming (MILP) has been developed for simultaneous optimization of water and energy (SOWE) in industrial processes. The superstructure integrates non-water process thermal streams and optimizes ...

2017

AC OPF in radial distribution networks – Part II: An augmented Lagrangian-based OPF algorithm, distributable via primal decomposition

Mario Paolone, Jean-Yves Le Boudec, Dan-Cristian Tomozei, Konstantina Christakou

In the first part of this two-part paper we show that the branch-flow convexification of the OPF problem is not exact and that the ADMM-based decomposition of the OPF fails to converge in specific scenarios. Therefore, there is a need to develop algorithms ...

Elsevier2017

Generalized Incremental Model Identification for Chemical Reaction Systems

Adrien Oulevey, Dominique Bonvin, Julien Léo Billeter, Diogo Filipe Mateus Rodrigues

Identification of kinetic models and estimation of kinetic parameters in chemical reaction systems can be done using Incremental Model Identification (IMI). By using IMI, it is possible to separate the effect of the different reactions and thus investigate ...

2017

A parametric augmented Lagrangian algorithm for real-time economic NMPC

Colin Neil Jones, Jean-Hubert Hours, Harsh Ambarishkumar Shukla

In this paper, a novel optimality-tracking algorithm for solving Economic Nonlinear Model Predictive Control (ENMPC) problems in real-time is presented. Developing online schemes for ENMPC is challenging, since it is unclear how convexity of the Quadratic ...

IEEE2016