A massively parallel algorithm for nonlinear stochastic network problems
Operations Research
Solving multistage stochastic network programs on massively parallel computers
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
Computational Optimization and Applications
High-performance computing in finance: the last 10 years and the next
Parallel Computing - Special Anniversary issue
High-performance computing in finance: the last 10 years and the next
Parallel Computing - Special Anniversary issue
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
High-Performance Computing for Asset-Liability Management
Operations Research
Introduction to Stochastic Programming
Introduction to Stochastic Programming
Nonlinear optimization and parallel computing
Parallel Computing - Special issue: Parallel computing in numerical optimization
A distributed computing architecture for simulation and optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
Recourse-based stochastic nonlinear programming: properties and Benders-SQP algorithms
Computational Optimization and Applications
| Hi-index | 0.00 |
In [Euro. J. Operat. Res. 143 (2002) 452; Opt. Meth. Software 17 (2002) 383] a Riccati-based primal interior point method for multistage stochastic programmes was developed. This algorithm has several interesting features. It can solve problems with a nonlinear node-separable convex objective, local linear constraints and global linear constraints. This paper demonstrates that the algorithm can be efficiently parallelized. The solution procedure in the algorithm allows for a simple but efficient method to distribute the computations. The parallel algorithm has been implemented on a low-budget parallel computer, where we experience almost perfect linear speedup and very good scalability of the algorithm.