RT Journal Article
T1 An exact dynamic programming approach to segmented isotonic regression
A1 Bucarey, Víctor
A1 Labbé, Martine
A1 Morales-González, Juan Miguel
A1 Pineda-Morente, Salvador
K1 Análisis de regresión
AB This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the maximum number of steps it can be composed of and on the minimum step length. Our algorithm relies on dynamic programming and is built on the basis that said curve-fitting task can be tackled as a shortest-path type of problem. Numerical results on synthetic and realistic data sets reveal that our algorithm is able to provide the globally optimal monotone stepwise curve fit for samples with thousands of data points in less than a few hours. Furthermore, the algorithm gives a certificate on the optimality gap of any incumbent solution it generates. From a practical standpoint, this piece of research is motivated by the roll-out of smart grids and the increasing role played by the small flexible consumption of electricity in the large-scale integration of renewable energy sources into current power systems. Within this context, our algorithm constitutes an useful tool to generate bidding curves for a pool of small flexible consumers to partake in wholesale electricity markets.
PB Elsevier
YR 2021
FD 2021
LK https://hdl.handle.net/10630/24006
UL https://hdl.handle.net/10630/24006
LA eng
NO Víctor Bucarey, Martine Labbé, Juan M. Morales, Salvador Pineda, An exact dynamic programming approach to segmented isotonic regression, Omega, Volume 105, 2021, 102516, ISSN 0305-0483, https://doi.org/10.1016/j.omega.2021.102516
NO This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 755705). This work was also supported in part by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF) through project ENE2017-83775-P. Martine Labbé has been partially supported by the Fonds de la Recherche Scientifique - FNRS under Grant(s) no PDR T0098.18.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026