Aggregation and garbage collection for online optimization

Alexander Ek, Maria Garcia de la Banda, Andreas Schutt, Peter J. Stuckey, Guido Tack

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Online optimization approaches are popular for solving optimization problems where not all data is considered at once, because it is computationally prohibitive, or because new data arrives in an ongoing fashion. Online approaches solve the problem iteratively, with the amount of data growing in each iteration. Over time, many problem variables progressively become realized, i.e., their values were fixed in the past iterations and they can no longer affect the solution. If the solving approach does not remove these realized variables and associated data and simplify the corresponding constraints, solving performance will slow down significantly over time. Unfortunately, simply removing realized variables can be incorrect, as they might affect unrealized decisions. This is why this complex task is currently performed manually in a problem-specific and time-consuming way. We propose a problem-independent framework to identify realized data and decisions, and remove them by summarizing their effect on future iterations in a compact way. The result is a substantially improved model performance.

Original languageEnglish
Title of host publication26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings
EditorsHelmut Simonis
Place of PublicationCham Switzerland
Number of pages17
ISBN (Electronic)9783030584757
ISBN (Print)9783030584740
Publication statusPublished - 2020
EventInternational Conference on Principles and Practice of Constraint Programming 2020 - Louvain-la-Neuve, Belgium
Duration: 7 Sep 202011 Sep 2020
Conference number: 26th (Proceedings) (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Principles and Practice of Constraint Programming 2020
Abbreviated titleCP2020
Internet address

Cite this