SDDP.jl: A Julia Package for Stochastic Dual Dynamic Programming
O Dowson, L Kapelevich - INFORMS Journal on Computing, 2021 - pubsonline.informs.org
We present SDDP. jl, an open-source library for solving multistage stochastic programming
problems using the stochastic dual dynamic programming algorithm. SDDP. jl is built on …
problems using the stochastic dual dynamic programming algorithm. SDDP. jl is built on …
[HTML][HTML] Mature offshore oil field development: Solving a real options problem using stochastic dual dynamic integer programming
Oil and gas companies are facing low output prices and are forced to focus on the
development of mature fields. Relevant investment decisions for operators include lifetime …
development of mature fields. Relevant investment decisions for operators include lifetime …
Distributionally robust stochastic dual dynamic programming
We consider a multistage stochastic linear program that lends itself to solution by stochastic
dual dynamic programming (SDDP). In this context, we consider a distributionally robust …
dual dynamic programming (SDDP). In this context, we consider a distributionally robust …
Duality and sensitivity analysis of multistage linear stochastic programs
In this paper we investigate the dual of a Multistage Stochastic Linear Program (MSLP). By
writing Dynamic Programming equations for the dual, we can employ an SDDP type method …
writing Dynamic Programming equations for the dual, we can employ an SDDP type method …
[HTML][HTML] Pricing electricity in constrained networks dominated by stochastic renewable generation and electric energy storage
This paper studies the electricity price formation in a competitive market when introducing
generation from variable renewable energy technologies with zero marginal cost and …
generation from variable renewable energy technologies with zero marginal cost and …
Bi-objective multistage stochastic linear programming
We propose an algorithm for solving a class of bi-objective multistage stochastic linear
programs. We show that the cost-to-go functions are saddle functions, and we exploit this …
programs. We show that the cost-to-go functions are saddle functions, and we exploit this …
Midas: A mixed integer dynamic approximation scheme
AB Philpott, F Wahid, JF Bonnans - Mathematical Programming, 2020 - Springer
Mixed integer dynamic approximation scheme (MIDAS) is a new sampling-based algorithm
for solving finite-horizon stochastic dynamic programs with monotonic Bellman functions …
for solving finite-horizon stochastic dynamic programs with monotonic Bellman functions …
Deep reinforcement learning for unmanned aerial vehicle-assisted vehicular networks
Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication
infrastructure in future smart cities. Hot spots easily appear in road intersections, where …
infrastructure in future smart cities. Hot spots easily appear in road intersections, where …
[PDF][PDF] Stochastic dual dynamic programming and its variant-a review
C Füllner, S Rebennack - Optimization Online, 2023 - optimization-online.org
We provide a tutorial-type review on stochastic dual dynamic programming (SDDP), 4 as
one of the state-of-the-art solution methods for large-scale multistage stochastic programs …
one of the state-of-the-art solution methods for large-scale multistage stochastic programs …
Optimal microgrid operation considering battery degradation using stochastic dual dynamic programming
Intermittent energy sources demand temporal storages to balance generation and load, and
batteries stand out as an alternative. However, the lifetime is limited, and cycling depth …
batteries stand out as an alternative. However, the lifetime is limited, and cycling depth …