Find optimal solutions to complex business and planning problems – fast

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SAS Optimization

Run powerful optimization and network optimization solvers in parallel in the cloud to find solutions to even the most challenging business problems, including optimal resource allocation, routing and planning.



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Key features

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.

Data access, preparation & quality

Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.

Data visualization

Visually explore data, and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data​.

Robust, intuitive algebraic optimization modeling language

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

A unified modeling language

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

Powerful optimization solvers & presolvers

A suite of optimization solvers are streamlined for simplicity and tuned for performance. Aggressive presolvers reduce effective problem size so you can tackle large problems and solve them faster.

Network flow optimization

Investigate the characteristics of networks and find the best answers to network-oriented problems using network algorithms accessible from both PROC OPTMODEL and PROC OPTNETWORK.

Multistart algorithm for nonconvex nonlinear optimization

Increase the 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)

Decompose overall problem into a set of component problems, each with an exclusive set of decision variables solved in parallel. Parallel subproblem solving is coordinated with the overall solution process, which saves significant time.

Black-box optimization

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

Constraint programming

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

Cloud native

SAS Viya's architecture is compact, cloud-native and fast. Whether you prefer to use a public or private cloud provider, you'll be able to make the most of your cloud investment.



SAS Viya is cloud-native and cloud-agnostic

Consume SAS how you want – SAS managed or self-managed. And where you want.

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Recommended resources for SAS Optimization

BLOG

What is optimization? And why it matters for your decisions

Blog

Using SAS Optimization with Python and containers

Community

Mathematical Optimization, Discrete-Event Simulation & OR Community


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