SAS® High-Performance Optimization
While data and models continue to grow in scope, detail and complexity, decision makers need answers faster than ever. With SAS High-Performance Optimization, you can model and solve optimization problems that are very large or whose other characteristics make them cumbersome to solve.
Develop and solve sophisticated, challenging models more efficiently and effectively to produce better, more accurate and timelier decision guidance. SAS High-Performance Optimization is available for execution in a highly scalable, distributed processing architecture.
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 questions you never thought of. Or had time to ask. This solution is available for Greenplum and Teradata appliances, as well as on commodity hardware using Hadoop (Cloudera and Hortonworks distributions).
- Quickly and confidently 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 better answers to your 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.
- Provides a decomposition algorithm that works well for certain classes of large, structured linear and mixed-integer optimization problems. The algorithm decomposes the overall problem into a set of component problems that can be solved quickly.
- Provides a multistart capability that increases the likelihood of identifying a globally optimal solution. It 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.
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.