Optimal solutions to complex business and planning problems. Fast.

A powerful array of optimization, simulation and project scheduling techniques for identifying actions that will get the best results, while operating within resource limitations and other relevant restrictions.

Robust, intuitive algebraic optimization modeling language

Enables you to produce a range of models, including linear, mixed integer linear, nonlinear, quadratic and network optimization, as well as solve constraint satisfaction problems.

A unified modeling language

Supports a wide range of optimization models with a single modeling and solution framework. You only need to learn one set of statements and commands to build a range of optimization and constraint satisfaction models.

Powerful optimization solvers and presolvers

Provides a suite of optimization solvers – all streamlined for simplicity and tuned for performance. Aggressive presolvers reduce effective problem size so you can tackle large problems and solve them more quickly.

Network flow optimization

Provides network algorithms, accessible from both PROC OPTMODEL and PROC OPTNETWORK, for investigating the characteristics of networks and finding the best answers to network-oriented problems.

Multistart algorithm for nonconvex nonlinear optimization

Increases chance of finding a globally optimal solution among many locally optimal solutions. Selects multiple starting points, begins optimization in parallel from each,  then reports the best solution from all starting points.

Decomposition algorithm (automated Dantzig-Wolfe)

Decomposes the overall problem into a set of component problems, each with an exclusive set of decision variables solved in parallel. Parallel solution of the subproblems is coordinated with the overall solution process, significantly reducing time to solution.

Black-box optimization

The black-box solver can be used with (generally nonlinear) optimization problems that don’t adhere to the assumptions that conventional optimization solvers make. Functions might be discontinuous, nonsmooth, computationally expensive to evaluate, based on black-box simulations, etc.

Constraint programming

Solves constraint satisfaction problems using domain reduction/constraint propagation and a choice of search strategies, such as look ahead and backtracking.

Accessible, cloud-enabled, in-memory engine

Uses the SAS Viya engine, which enhances the SAS Platform, providing high availability, fast in-memory processing, the ability to code from open source languages and native cloud support.

Consider more alternative actions and scenarios, and determine the best allocation of resources and plans for accomplishing goals.

Quickly solve complex optimization problems.

Find optimal solutions to difficult problems faster than ever. SAS Optimization takes advantage of the SAS® Viya® distributed, in-memory engine to deliver optimization modeling results at breakthrough speeds. In-memory data persistence eliminates the need to load data multiple times during iterative analyses. 

Drive better decision making.

Identify and apply the best responses to complex, real-world problems. State-of-the-art methods for mathematical optimization are integrated with a full suite of data preparation, exploration, analytics and reporting capabilities – all in one unified environment.

Empower users with their preferred programming language.

Python, Java, R and Lua programmers can take advantage of the wide range of solvers in SAS Optimization without having to learn SAS code. They can access powerful, trusted and tested SAS algorithms from the programming language they are most comfortable with.


This solution runs on SAS® Viya®, which has the breadth and depth to conquer any analytics challenge, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.

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