Features List

SAS Optimization Features List

SAS® Optimization

Algebraic, symbolic optimization modeling language

  • Flexible algebraic syntax for intuitive model formulation.
  • Support for the transparent use of SAS functions.
  • Direct invocation of linear, mixed integer linear, quadratic, nonlinear, conic, black-box, constraint programming, and network solvers.
  • Support for the rapid prototyping of customized optimization algorithms, including support for named problems and subproblems.
  • Use of industry-standard MPS/QPS format input data sets.
  • Aggressive presolvers to reduce effective problem size.
  • Solve independent problems concurrently on one machine or a computational grid.
  • Automated linearization and indicator constraints.

Powerful optimization solvers

  • Linear programming solution algorithms:
    • Primal and dual simplex.
    • Sifting.
    • Network simplex.
    • Interior point with crossover.
    • Concurrent solve capability.
  • Mixed integer linear programming solution algorithms:
    • Branch-and-bound with cutting planes.
    • Primal heuristics.
    • Conflict search.
    • Option tuning.
    • Root node (LP relaxation) algorithm options.
    • Report up to the K best integer feasible solutions or up to K optimal solutions.
  • Decomposition algorithm (automated Dantzig-Wolfe) for linear programming and mixed integer linear programming problems with user-specified or automatically detected block structure.
  • Quadratic programming solution algorithm: interior point with state-of-the-art solver tailored for large-scale optimization problems.
  • Nonlinear programming solution algorithms: active set, interior point. Concurrent solve capability. Multistart algorithm for nonconvex problems.

Network optimization

  • Diagnostic and optimization algorithms include:
    • Connected components and biconnected components (with articulation points).
    • Maximal clique enumeration.
    • Cycle enumeration.
    • Path enumeration.
    • Transitive closure.
    • Topological sort.
    • Maximum flow.
    • Minimum cut.
    • Minimum spanning tree.
    • Minimum-cost linear assignment.
    • Minimum-cost network flow.
    • Shortest path.
    • Traveling salesman problem.
    • Vehicle routing problem.
    • Summary statistics.
    • Multiple links between each pair of nodes can be input and processed.

Black-box optimization

  • Solves problems with nonlinear functions that can be non-smooth, discontinuous, not continuously differentiable, and so on.
  • Hybrid parallel algorithm, including generic algorithms, global GA-type heuristics and pattern search. Multiobjective optimization.

Constraint programming

  • Solves constraint satisfaction problems using finite-domain constraint programming, with domain reduction/constraint propagation and a choice of search strategies (look ahead and backtracking). Find one, several or all feasible solutions. Optionally specify an objective function and find an optimal solution (bisection search method).

Distributed, accessible & cloud-ready

  • Optimization solvers run on SAS Viya, a scalable and distributed in-memory analytics platform.
  • Distributes analysis and data tasks across multiple computing nodes.
  • Distributed computation features:
    • Multistart option for nonlinear (NLP) solver in PROC OPTMODEL.
    • Decomposition algorithm (MILP) in PROC OPTMODEL, PROC OPTMILP.
    • Solving independent optimization scenarios: COFOR loop in PROC OPTMODEL.
    • Concurrent mode for MILP solver (PROC OPTMODEL, PROC OPTMILP).
    • Branch-and-bound MILP solver algorithm (PROC OPTMODEL, PROC OPTMILP).
    • Black-box optimization in PROC OPTMODEL.
    • Path enumeration, shortest path, and connected components network algorithms in PROC OPTNETWORK.
    • BY-group processing in network algorithms in PROC OPTNETWORK.
    • Provides fast, concurrent, multiuser access to data in memory.
    • Includes fault tolerance for high availability.
    • Lets you add the power of SAS analytics to other applications by using SAS Viya REST APIs.