SAS® High-Performance Optimization
Model and solve optimization problems that are very large or whose other characteristics make them cumbersome to solve. With the ability to solve these kinds of optimization models quickly, you can perform more frequent modeling iterations and use sophisticated analytics to get answers to even the most complex questions.
Benefits
- Find and seize new opportunities, assess alternatives thoroughly and efficiently, and make the right choices.
- Use advanced modeling and solution techniques, and perform more model iterations, to get precise answers to your most difficult questions.
- Generate insights at breakthrough speeds for high-value and time-sensitive decision making.
- Take advantage of a highly scalable and reliable analytics infrastructure to test more ideas and investigate more scenarios.
Features
- High-performance optimization.
- Decomposition algorithm. Decomposes the overall problem into a set of component problems that can be solved quickly. Works well for certain classes of large, structured linear and mixed-integer optimization problems.
- Multistart capability. Increases the likelihood of identifying a globally optimal solution. Can be used to select and begin optimization from each point. The best solution found among all starting points is reported.
- Parallelized option tuner functionality in PROC OPTMILP. Helps identify the best option settings for submitted problems. The tuner searches among possible option combinations, runs multiple optimizations in parallel with different settings, and finds the best set of option values.
- Decomposition algorithm. Decomposes the overall problem into a set of component problems that can be solved quickly. Works well for certain classes of large, structured linear and mixed-integer optimization problems.
- High-performance local search optimization.
- Optimizes a user-defined objective subject to linear and nonlinear constraints.
- Allows continuous and integer variables.
- Uses genetic algorithms, as well as other global and local search techniques in parallel, to solve problems.
