Artikel
Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs
We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality employed previously is a very relaxed approximation with assumptions that do not generally hold. Computational results demonstrate on average over a 43% improvement in the bounds. As a byproduct, we provide an exact reformulation of the floor function in optimization models.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Optimization Letters ; ISSN: 1862-4480 ; Volume: 15 ; Year: 2020 ; Issue: 2 ; Pages: 327-336 ; Berlin, Heidelberg: Springer
- Klassifikation
-
Mathematik
- Thema
-
Chance-constrained optimization
Bonferroni inequalities
Union bounds
Stochastic optimization
Floor function
Linearization
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Singh, Bismark
- Ereignis
-
Veröffentlichung
- (wer)
-
Springer
- (wo)
-
Berlin, Heidelberg
- (wann)
-
2020
- DOI
-
doi:10.1007/s11590-020-01592-1
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Singh, Bismark
- Springer
Entstanden
- 2020
Ähnliche Objekte (12)
Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs
On two-stage convex chance constrained problems
Approximating Perpetuities
Approximating data and statistical procedures. I. Approximating data
Approximating Data and Statistical Procedures - I. Approximating Data
A finite branch and bound algorithm for two-stage stochastic integer programs
Approximating least fixpoints
Approximating solution structure
Nonconvex constrained optimization by a filtering branch and bound
Nonconvex constrained optimization by a filtering branch and bound
LMBOPT : a limited memory method for bound-constrained optimization
Performance bounds for co-/sparse box constrained signal recovery
Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs
On two-stage convex chance constrained problems
Approximating Perpetuities
Approximating data and statistical procedures. I. Approximating data
Approximating Data and Statistical Procedures - I. Approximating Data
A finite branch and bound algorithm for two-stage stochastic integer programs
Approximating least fixpoints
Approximating solution structure
Nonconvex constrained optimization by a filtering branch and bound
Nonconvex constrained optimization by a filtering branch and bound
LMBOPT : a limited memory method for bound-constrained optimization
Performance bounds for co-/sparse box constrained signal recovery
Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs
On two-stage convex chance constrained problems
Approximating Perpetuities
Approximating data and statistical procedures. I. Approximating data
Approximating Data and Statistical Procedures - I. Approximating Data
A finite branch and bound algorithm for two-stage stochastic integer programs
Approximating least fixpoints
Approximating solution structure
Nonconvex constrained optimization by a filtering branch and bound
Nonconvex constrained optimization by a filtering branch and bound
LMBOPT : a limited memory method for bound-constrained optimization