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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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.
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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.
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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.
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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