Back to Optimization Under Uncertainty
Stochastic Programming is a framework for modeling optimization problems that involve uncertainty. Many of the fundamental concepts are discussed in the linear case, Stochastic Linear Programming.
- Stochastic Linear Programming Solvers on NEOS Server
- SAMPL - a translator for the modelling language for stochastic programming based on AMPL
- Test Problems
- A POrtable Stochastic programming Test Set (POSTS) by Derek Holmes and John Birge
- Test-Problem Collection for Stochastic Linear Programming by K. Ariyawansa and Andrew Felt
- Test Set by Jeff Linderoth, Alexander Shapiro and Stephen Wright