Unité
Recherche en systèmes, automatique et optimisation
LaboratoirePublications associées (32)
Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024Augmented Lagrangian Methods for Provable and Scalable Machine Learning
Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
EPFL2023Data-Driven Control and Optimization under Noisy and Uncertain Conditions
Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...
EPFL2023Multi-robot task allocation for safe planning against stochastic hazard dynamics
Maryam Kamgarpour, Orcun Karaca
We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...
2023Memory of Motion for Initializing Optimization in Robotics
Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...
EPFL2022Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond
Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...
EPFL2021Exploiting structure of chance constrained programs via submodularity
Maryam Kamgarpour, Tony Alan Wood
We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario ...
2019Kernel methods and Model predictive approaches for Learning and Control
Data-driven modeling and feedback control play a vital role in several application areas ranging from robotics, control theory, manufacturing to management of assets, financial portfolios and supply chains. Many such problems in one way or another are rela ...
EPFL2019Model-based predictive control methods for distributed energy resources in smart grids.
This thesis develops optimization-based techniques for the control of distributed energy resources to provide multiple services to the power network. It is divided into three parts.
The first part of this thesis focuses on the development of a framework f ...
EPFL2019New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials
Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...
EPFL2018