TY - GEN
T1 - A computationally efficient FPTAS for convex stochastic dynamic programs
AU - Halman, Nir
AU - Nannicini, Giacomo
AU - Orlin, James
PY - 2013
Y1 - 2013
N2 - We propose a computationally efficient Fully Polynomial-Time Approximation Scheme (FPTAS) for convex stochastic dynamic programs using the technique of K-approximation sets and functions introduced by Halman et al. This paper deals with the convex case only, and it has the following contributions: First, we improve on the worst-case running time given by Halman et al. Second, we design an FPTAS with excellent computational performance, and show that it is faster than an exact algorithm even for small problem instances and small approximation factors, becoming orders of magnitude faster as the problem size increases. Third, we show that with careful algorithm design, the errors introduced by floating point computations can be bounded, so that we can provide a guarantee on the approximation factor over an exact infinite-precision solution. Our computational evaluation is based on randomly generated problem instances coming from applications in supply chain management and finance.
AB - We propose a computationally efficient Fully Polynomial-Time Approximation Scheme (FPTAS) for convex stochastic dynamic programs using the technique of K-approximation sets and functions introduced by Halman et al. This paper deals with the convex case only, and it has the following contributions: First, we improve on the worst-case running time given by Halman et al. Second, we design an FPTAS with excellent computational performance, and show that it is faster than an exact algorithm even for small problem instances and small approximation factors, becoming orders of magnitude faster as the problem size increases. Third, we show that with careful algorithm design, the errors introduced by floating point computations can be bounded, so that we can provide a guarantee on the approximation factor over an exact infinite-precision solution. Our computational evaluation is based on randomly generated problem instances coming from applications in supply chain management and finance.
UR - http://www.scopus.com/inward/record.url?scp=84884332244&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40450-4_49
DO - 10.1007/978-3-642-40450-4_49
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AN - SCOPUS:84884332244
SN - 9783642404498
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 577
EP - 588
BT - Algorithms, ESA 2013 - 21st Annual European Symposium, Proceedings
T2 - 21st Annual European Symposium on Algorithms, ESA 2013
Y2 - 2 September 2013 through 4 September 2013
ER -