All Years Seminars
[INMA] 2018-02-13 (14:00) : An Asynchronous Distributed Algorithm for Solving Stochastic Unit Commitment
At Euler building (room A.002)
Speaker :
Ignacio Aravena (UCL)
Abstract :
We present an asynchronous algorithm for solving the stochastic unit commitment (SUC) problem using decomposition. The algorithm is motivated by large differences in run times observed among subproblems, which can result in inefficient use of distributed computing resources by synchronous parallel algorithms. Dual iterations are performed asynchronously using a block-coordinate subgradient descent method which allows performing block-coordinate updates using delayed information, while candidate primal solutions are recovered from the solutions of scenario subproblems using heuristics. The asynchronous algorithm is implemented following the master-slave paradigm and using MPI to handle communication among processes. We conduct numerical experiments using a high performance computing cluster for two-stage SUC instances of (i) the Western Electricity Coordinating Council (WECC) system with up to 1000 scenarios and of (ii) the Central Western European (CWE) system with up to 120 scenarios. The algorithm provides solutions to all problems within 2% of optimality in at most 23 minutes for WECC and 98 minutes for CWE, and solutions within 1% of optimality in at most 63 minutes for WECC and 133 minutes for CWE. Moreover, we find that an equivalent synchronous parallel algorithm would leave processors idle up to 80.4% of the time, an observation which underscores the need for designing asynchronous optimization schemes in order to fully exploit distributed computing on real world applications.
[INMA] 2018-02-06 (14:00) : Robust and adaptive control of reaching movements in human
At Euler building (room A.002)
Speaker :
Crevecoeur, Frédéric
Abstract :
Neural plasticity and flexible representations of reach dynamics allows humans and animals to adapt to a wide range of disturbances applied to the limb such as robotic force fields, Coriolis force fields, and even changes in gravity. This ability has been almost exclusively described across trials, characterizing the gradual improvement of movement performances through learning curves. However, it remains unknown how we control movements in the presence of unexpected disturbances. Two candidate hypotheses are motivated by engineering techniques: robust (Hoo) and adaptive control can be used to mitigate the impact of model errors. Our experimental results show that healthy humans may implement these two strategies. We found that the presence of uncertainty in reach dynamics evoked changes in control consistent with a robust control model. Furthermore, within movement feedback corrections for unexpected dynamics evoked online adaptation of the ongoing motor command. These two components of movement execution may provide a powerful framework for linking control over time with learning over trials.
[INMA] 2018-01-16 (14:00) : Robust model predictive control
At Euler building (room A.002)
Speaker :
Sasa V. Rakovic
Abstract :
Model predictive control (MPC) is an advanced control technique that employs an open-loop online optimization in order to take
account of system dynamics, constraints and control objectives and to obtain the best current control action. Robust MPC (RMPC)
is an improved MPC form that is robust against the bounded uncertainty. RMPC employs a generalized prediction framework that
allows for a meaningful optimization of, and over, the set of possible system behaviours effected by the uncertainty. A real intricacy
in RMPC arises due to the facts that the exact RMPC provides strong structural properties but it is computationally unwieldy,
while the conventional MPC is not necessarily robust even though it is computationally convenient.
The seminar focuses on novel RMPC methods, developed through my research investigations and collaborations, that address
effectively the fundamental challenge of reaching a meaningful compromise between the quality of guaranteed structural properties
and the associated computational complexity. In particular, the talk discusses flexible and efficiently optimizable parameterizations
as well as tube MPC, which lead to synthesis methods that are theoretically sound (i.e., they guarantee a-priori strong structural
properties) and computationally efficient (i.e., they have a manageable computational complexity that is close enough to that of
the conventional MPC synthesis).
[INMA] 2018-01-12 (14:00) : Inexact Linear Solves for Model Reduction
At Euler building (room A.002)
Speaker :
Kapil Ahuja (IIT Indore, India)
Abstract : Dynamical systems arise in varied fields of science and technology. Dimension of such systems is usually large. Hence, simulations using them is cumbersome. Model reduction of a large dynamical system gives us a smaller dynamical system that mimics the input-output behavior of the original dynamical system.
With the increase in the dimension of the original dynamical system, the model reduction process itself becomes a hard task. The reason behind this is that most model reduction algorithms require solution of linear systems of equations, which traditionally are solved by direct methods that scale badly with increase in the dimension. This is the main motivation for using iterative methods for solution of linear systems of equations arising in model reduction algorithms.
Iterative methods are inexact in nature. That is, they solve a linear system up to a certain tolerance. Hence, studying stability of model reduction algorithms with respect to the error introduced by inexact solves is an important task. In this talk, I will discuss this kind of backward stability of popular model reduction algorithms for first-order bilinear and second-order linear dynamical systems.
[INMA] 2017-12-19 (14:00) : Data recovery from parsimonious observations: From compressed sensing to deep learning
At Euler building (room A.002)
Speaker :
Nikos Deligiannis (VUB)
Abstract :
The problem of recovering high-dimensional data from parsimonious, low-dimensional measurements is linked to a plethora of applications ranging from big data collection in smart environments to the enhancement of multimodal imaging sensors. The problem has been studied from various angles from the signal processing and machine learning communities, including compressed sensing - which enables the acquisition of a sparse (or compressible) signal using far less measurements than the classical Shannon-Nyquist theory - and matrix completion - which is essentially matrix factorisation with low-rank constraints. In the first part of the talk, we will present approaches to regularise such problems using constraints that encode the similarity of the data we wish to recover with observed, correlated (multimodal) data; while in the second part, we will present deep learning formulations that address the problem. Special focus will be put on learning good designs for the data collection mechanisms so as to reduce the associated complexity of the task. Finally, we will present several applications of our frameworks in multimodal image recovery, computer vision, and data recovery from internet-of-things devices measuring the air pollution.
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