20 citations · 28 across the 6 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
math.OC2019★ 2 cited
Dynamic cut aggregation in L-shaped algorithms
Martin Biel, Mikael Johansson
We present a novel framework for dynamic cut aggregation in L-shaped algorithms. The aim is to improve the parallel performance of distributed L-shaped algorithms through reduced c…
math.OC2019★ 5 cited
Optimal day-ahead orders using stochastic programming and noise-driven RNNs
Martin Biel
This paper presents a methodology for strategic day-ahead planning that uses a combination of deep learning and optimization. A noise-driven recurrent neural network structure is p…
math.OC2019★ 20 cited
Efficient Stochastic Programming in Julia
Martin Biel, Mikael Johansson
We present StochasticPrograms.jl, a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling…