Welcome at CORE !
The Center for Operations Research and Econometrics (CORE) is an interdisciplinary research center of UCLouvain.
In 2010, CORE became one of the "poles" of LIDAM, a UCL Louvain research institute association researchers from four different research entities: CORE, IRES, ISBA and LFIN.
CORE follows three objectives. The first one is the development of scientific research in the fields of economics, econometrics, operations research and quantitative and economic geography. The second objective is the training of young researchers at the doctoral and postdoctoral stages of their career. The third objective is the promotion of local and international scientific exchanges and collaborations.
Upcoming Events at CORE
OR Seminar - Benedetto Manca
Benedetto Manca (University of Cagliari)
give a presentation on :
Random Projections for Mathematical Programming.
Abstract :
Random projections are random linear maps which approximately preserve geometrical quantities. The Johson-Lindenstrauss Lemma states that there exist random projections with a relatively small number of rows which preserve the pairwise Euclidean distances among a set of points.
In the past years several results on the application of random projections to Mathematical Programming instances are being obtained, showing that it is possible to obtain an approximate formulation with less decision variables and constraints and therefore easier to solve.
In this talk I will introduce the generic framework of random projections for Mathematical Programming formulations and I will present more in details some results in the case of Linear Programming, Quadratic Programming and the Minimum Sum of Squares problem.
OR Seminar - Alessio Trivella
Alessio Trivella (University of Twente)
give a presentation on :
Network dual reoptimization policies and bounds for managing energy real options.
Abstract :
Energy operations and investments are managed with real option models that embed timing and/or switching decisions, where the former and latter types of decisions change irreversibly and reversibly, respectively, the status of the real asset. Optimizing the flexibility in these models requires solving a finite-horizon Markov decision process with finite endogenous states but a high-dimensional and continuous exogenous state space, which is typically intractable and requires heuristic approaches. Least squares Monte Carlo (LSM) is a popular approximate dynamic programming method for this purpose while a known forecast-based reoptimization heuristic (FRH) is not well suited to handle timing decisions. We develop a network dual reoptimization framework that leverages network flow algorithms and employs a novel class of partial information relaxations to overcome this shortcoming. Our framework provides both a new policy and a dual bound and we establish that: (i) the decisions taken by our policy are equivalent to solving a scenario-based two-stage stochastic program but computationally more efficient since scenarios are solved independently; and (ii) our dual bound is provably tighter than the known penalized perfect information bound. Numerical experiments on two energy real options dealing with commodity production and vehicle fleet upgrade show that our policy outperforms FRH by 10-20% and LSM by to 2-5%, while our dual bound improves by less than 1%.
