LINDO API includes features to allow users to incorporate uncertainty into their optimization models.
Stochastic Programming Interface
Modeling and optimization with uncertain elements through multistage linear, nonlinear and integer stochastic programming (SP).
Extensive set of API functions to setup and solve SP models.
Benders decomposition for solving linear SP models.
Deterministic equivalent method for solving nonlinear and integer SP models.
Supports most (20+) parametric (continuous or discrete) distributions.
User-defined distribution functions to be used through callbacks.
Customized sampling scenarios through the statistical sampling API.
Statistical Sampling API
Extensive API functions to sample directly from various statistical distributions,
Variance reduction with Latin-Hyper-Cube and Anti-thetic variates sampling,
Generation of correlated samples via Pearson, Spearman, or Kendall correlation measures.
Pseudo random number generation via a choice of three different generators.
Simplex Solver Improvements
Large linear models solve an average of 20% faster with improved primal and dual solvers.
MIP Solver Improvements
Substantial improvements in all heuristics for finding close to optimal solutions quickly.
Significant improvements in cuts for certain types of special model structures.
Global Solver Improvements
Significant improvement in the handling of nonlinear models with quadratic terms, especially non-convex quadratic expressions.