GNOM - Grup d'Optimització Numèrica i Modelització
El treball del grup comprèn l'optimització numèrica, la modelització de problemes resolubles amb optimització, i l'aplicació de tècniques adients a problemes de l'enginyeria, la indústria, la logística i les tecnologies de la informació. L'optimització numèrica inclou l'estudi de les propietats del problemes estudiats, l'anàlisi de nous algorismes d'optimització (lineal, no lineal, contínua, entera i combinatòria), el desenvolupament de procediments numèrics per a la seva implementació computacional eficient i la comparació amb algorismes previs, si existeixen. La part de modelització implica la cerca de formulacions escaients que permetin la resolució de problemes reals, sovint de gran dimensió. Els problemes reals en que el grup està treballant actualment són: protecció i privacitat de dades estadístiques; i generació òptima d'electricitat en un entorn multimercat.
The group works on both the numerical optimisation and the modelling of problems that can be solved through optimisation. Its work on numerical optimisation includes the analysis of new optimisation algorithms (linear, nonlinear, continuous and integer) and their convergence, the development of numerical procedures for their efficient computational implementation, and the comparison to existing algorithms. The work on modelling focuses on the solution of real problems, which are often large-scale, by finding an equivalentmathematical formulation leading to an optimisation problem, and applying a suitable solver, that has either been specially developed or is commercially available. The modelling languages that are available are used.
The group works on both the numerical optimisation and the modelling of problems that can be solved through optimisation. Its work on numerical optimisation includes the analysis of new optimisation algorithms (linear, nonlinear, continuous and integer) and their convergence, the development of numerical procedures for their efficient computational implementation, and the comparison to existing algorithms. The work on modelling focuses on the solution of real problems, which are often large-scale, by finding an equivalentmathematical formulation leading to an optimisation problem, and applying a suitable solver, that has either been specially developed or is commercially available. The modelling languages that are available are used.
Collections in this community
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Articles de revista [96]
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Llibres [1]
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Reports de recerca [35]
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Working papers [1]
Recent Submissions
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Measuring the effectiveness of online problem solving for improving academic performance in a probability course
(2022-01-13)
Article
Restricted access - publisher's policyResearch on impact in student achievement of online homework systems compared to traditional methods is ambivalent. Methodological issues in the study design, besides of technological diversity, can account for this ... -
El objetivo guía el diseño y el análisis
(Elsevier, 2021-12-01)
Article
Open Access -
El valor P está obsoleto
(Elsevier, 2021-12-01)
Article
Open AccessEn esta píldora seguimos las guías de publicación1 y la declaración de la American Statistical Association (ASA)2. Pretendemos convencerle de que no use el valor de P (o P-value). No es tarea fácil. Pero seamos sinceros: ... -
Uso e interpretación de los intervalos de confianza
(Elsevier, 2021-12-01)
Article
Open AccessInterprete los intervalos de confianza como informativos de1: los valores poblacionales compatibles con los resultados observados; y2 la magnitud de la incertidumbre restante tras el estudio. Y si no existe un proceso de ... -
On solving large-scale multistage stochastic problems with a new specialized interior-point approach
(2021-12)
Research report
Open AccessA novel approach based on a specialized interior-point method (IPM) is presented for solving large-scale stochastic multistage continuous optimization problems, which represent the uncertainty in strategic multistage and ... -
New interior-point approach for one- and two-class linear support vector machines using multiple variable splitting
(2021-12)
Research report
Open AccessMultiple variable splitting is a general technique for decomposing problems by using copies of variables and additional linking constraints that equate their values. The resulting large optimization problem can be solved ... -
A specialized interior-point algorithm for huge minimum convex cost flows in bipartite networks
(Elsevier, 2021-05)
Article
Open AccessThe computation of the Newton direction is the most time consuming step of interior-point methods. This direction was efficiently computed by a combination of Cholesky factorizations and conjugate gradients in a specialized ... -
Recent advances in optimization techniques for statistical tabular data protection
(Elsevier, 2012-01-16)
Article
Open AccessOne of the main services of National Statistical Agencies (NSAs) for the current Information Society is the dissemination of large amounts of tabular data, which is obtained from microdata by crossing one or more categorical ... -
A new interior-point approach for large separable convex quadratic two-stage stochastic problems
(2020-11-03)
Article
Open AccessTwo-stage stochastic models give rise to very large optimization problems. Several approaches havebeen devised for efficiently solving them, including interior-point methods (IPMs). However, usingIPMs, the linking columns ... -
Stabilized Benders methods for large-scale combinatorial optimization, with application to data privacy
(2020-07)
Article
Open AccessThe Cell Suppression Problem (CSP) is a very large Mixed-IntegerLinear Problem arising in statistical disclosure control. However, CSPhas the typical structure that allows application of the Benders de-composition, which ... -
An online optimization-based procedure for the assignment of airplane seats
(Springer, 2021)
Article
Open AccessDue to the large number of air flights these days, all procedures involved in their operational management should be carefully optimized. This work presents a novel approach to the seat assignment problem, which focuses ... -
Using a stabilized Benders algorithm for cell suppression
(John Wiley & sons, 2019)
Conference report
Restricted access - publisher's policyThe cell suppression problem (CSP) is one of the most widely applied post-tabular protection methods for tabular data. Given a set of sensitive cells to be protected, its aim is to find set of additional cells whose removal ...
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