RASON® -- Restful Analytic Solver® Object Notation -- is a modeling language embedded in JSON and a REST API that makes it easy to create, test and deploy analytic models using optimization, simulation, and data mining, in web and mobile applications. It's supported by Excel Solver developer Frontline Systems.

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Analytics Professionals

If you have used another modeling language for optimization or simulation, you’ll find the RASON language simple but powerful and expressive – and that integrating RASON models into a larger application, especially a web or mobile app, is much easier than with other modeling systems.

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Excel Solver Users

If you have used Excel for optimization or simulation, you’ll find that it’s easy to translate Excel models into RASON models, that your knowledge of Excel formulas and functions is immediately usable, but that RASON models can be more flexibly “bound” to data from a variety of sources.

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Web App Developers

If you are using JavaScript, and you’re familiar with AJAX and use of REST APIs, you’ll find it’s exceptionally easy to embed RASON models as JSON in your code, and to solve them using Frontline’s RASON server, which exposes a simple REST API that’s scalable to handle very large, compute-intensive analytic models.

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You can use RASON either as an online service (register here for a free trial account) or as a software tool on your desktop or server (register on Solver.com to download a free trial).

Problems you can solve with RASON include linear programming and mixed-integer programming problems, quadratic programming and second-order cone problems, nonlinear and global optimization problems, problems requiring genetic algorithm and tabu search methods -- from small to very large (LP/MIP models with millions of variables).

You can also solve Monte Carlo simulation / risk analysis problems, and create and solve models with uncertainty, using simulation optimization, robust optimization, and stochastic programming methods. With built-in stochastic decomposition methods, you can solve stochastic linear programming problems scaled-up to the state of the art.