All Years Seminars
[INMA] 2024-11-26 (14h) : Forward electricity market with nonconvexities
At Euler building (room A.002)
Speaker :
Quentin Lété ((UCLouvain))
Abstract :
Thermal generators must decide whether to produce electricity well in advance, as starting up or shutting down operations require lead time. By participating in forward auctions, like day-ahead or intraday auctions, they can lock in prices, ensuring that they will not make a loss when committing to produce power in the future. Their participation leads to the auction turning nonconvex as thermal generators must communicate indivisible fixed costs associated with start-up and shut-down operations. How to price electricity in such a nonconvex auction is a long-standing debate, but a consensus seems to be emerging towards convex hull pricing. However, most existing literature studies the nonconvex forward market in isolation, ignoring the relationship with the convex real-time market. In this talk, we propose a model to study nonconvex pricing in two-settlement markets, accounting for both day-ahead and real-time markets with financial participants. We show on an illustrative example that, in this case, most of the theoretical properties of convex hull pricing (efficiency and loss opportunity costs minimization) cease to hold. We then present simulation results on a case study calibrated on the PJM market.
[INMA] 2024-11-19 (14h) : Large-scale Stochastic Optimization: Approximations and Distributed Methods
At Euler building (room A.002)
Speaker :
Ashish Cherukuri (University of Groningen)
Abstract :
This talk focuses on stochastic optimization problems defined over a network and explores data-driven distributionally robust (DR) solution methods to solve it. Specifically, we will look at chance-constrained optimization that finds application in generation planning problem and expectation minimization problem that is motivated by distributed optimization and federated learning. The DR formulations of the problem have attractive statistical guarantees but pose computational difficulties. The talk will provide algorithms to handle these challenges, paying special attention to the large-scale nature of the problem and the fact that the data about the uncertainty cannot be aggregated at one single location in the network. We will end the talk with future challenges and research directions.
[INMA] 2024-11-18 (10h) : Scalable high-order consensus for multi-agent coordination
At Euler building (room A.207)
Speaker :
Jonas Hansson (Lund University,Sweden)
Abstract :
In this talk, I will present a novel control design for vehicular formations, introducing an alternative approach to conventional consensus protocols for second and high-order systems. The design is motivated by the closed-loop system, which we construct to match the dynamics of first-order systems connected in series, and is therefore called serial consensus. Due to the design, the stability of the closed-loop can be easily proven. Perhaps more interestingly, the control design can also be adapted to achieve both scalable and robust stability and performance (string stability), which is particularly interesting for controlling large vehicular platoons. Noteworthy, this is achieved with a distributed controller that only uses local relative measurements. The theoretical findings will be illustrated through examples.
[INMA] 2024-11-12 (14h) : Null space gradient flows and applications to topology optimization
At Euler building (room A.002)
Speaker :
Florian Feppon ((KU Leuven))
Abstract :
In this seminar, I will present the "Null Space Optimizer" [1,2] which is an algorithm developed for solving nonlinear optimization programs with differentiable equality and inequality constraints.
The main principle of the algorithm is to discretize a dynamical system whose trajectories simultaneously and gradually correct the violation of the constraints while minimizing the objective function.
As a result, one of its appealing aspects comes from its relative independence to the need for tuning unintuitive algorithm parameters. I will show some applications of the algorithm to topology optimization
and discuss a recent extension that enables the optimizer to solve problems with both a large design spaces and many constraints with sparse jacobian matrix.
[1] Feppon, F., Allaire, G. and Dapogny, C. Null space gradient flows for constrained optimization with applications to shape optimization (2020). ESAIM: COCV, 26 9.
[2] Feppon F. Density based topology optimization with the Null Space Optimizer: a tutorial and a comparison (2024). Structural and Multidisciplinary Optimization, 67(4), 1-34.
[INMA] 2024-11-06 (15h) : Subsampled cubic regularization method for finite-sum minimization
At Euler building (room A.002)
Speaker :
Max.L.N. Gonçalves ((Universidade Federal de Goiás, Brazil))
Abstract :
In this talk, we propose and analyse a subsampled Cubic Regularization Method (CRM) for solving finite-sum optimization problems. The new method uses random subsampling techniques to approximate the functions, gradients and Hessians in order to reduce the overall computational cost of the CRM. Under suitable hypotheses, first- and second-order iteration-complexity bounds and global convergence analyses are presented. We also discuss the local convergence properties of the method. Numerical experiments are presented to illustrate the performance of the proposed scheme.
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Mathematical Engineering (INMA)
L4.05.01
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+32 10 47 80 36
secretaire-inma@uclouvain.be
Mon – Fri 9:00A.M. – 5:00P.M.
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