A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem

DOI10.1007/s11750-013-0305-9zbMath1336.90065OpenAlexW2021111715MaRDI QIDQ287624

Maria Elena Bruni, Patrizia Beraldi

Publication date: 23 May 2016

Published in: Top (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11750-013-0305-9

zbMATH Keywords

stochastic programmingportfolio optimizationclustering algorithmsscenario tree reduction

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Sensitivity, stability, parametric optimization (90C31) Stochastic programming (90C15) Portfolio theory (91G10)

Related Items (7)

Medium range optimization of copper extraction planning under uncertainty in future copper prices ⋮ A New Scenario Reduction Method Based on Higher-Order Moments ⋮ Evaluation of scenario reduction algorithms with nested distance ⋮ Practical arbitrage‐free scenario tree reduction methods and their applications in financial optimization ⋮ Multistage stochastic demand-side management for price-making major consumers of electricity in a co-optimized energy and reserve market ⋮ Clustering and portfolio selection problems: a unified framework ⋮ A parallelized variable fixing process for solving multistage stochastic programs with progressive hedging

Uses Software

• clusfind

Cites Work

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