Six months ago in Analytic Solver V2017, we introduced an array of new features: Analytic Solver Basic as a starting point for new users, always-available data mining, optimization and simulation, while you pay for only what you need to solve large models, Test Runs that give you an ongoing free trial of everything you haven't yet purchased, a new licensing system that lets you easily work on multiple computers with one license, and access to AnalyticSolver.com, our comprehensive cloud-based analytics platform that inter-operates with Excel.
That was pretty big -- but of course we haven't stopped. Where V2017 introduced an array of enhancements in data mining -- such as enhanced neural networks, ensembles of any of ten different classification and prediction methods, and PMML models for deployment, our new V2017-R2 release introduces major enhancements for optimization and simulation. You can read full details in our press release. But perhaps the most exciting new feature of V2017-R2 is the ability to deploy your working Excel model (using optimization and/or simulation) as a Power BI Custom Visual -- with just two mouse clicks!
Much Faster Setup of Large Optimization Models
Users with large linear or nonlinear optimization models (10,000 to 1,000,000 decision variables) have probably noticed that it often takes more time to go through "Setting Up Problem" -- when your model is extracted from tens or hundreds of thousands of Excel formulas -- than it takes to actually find the optimal solution, especially with our plug-in large-scale Solver Engines. In V2017-R2, this process is sometimes spectacularly faster: While "your model and mileage may vary," we've seen models go through "Setting Up Problem" 10x to 50x faster!
Also part of this release are new, higher performance versions of the Gurobi Solver Engine (based on Gurobi 7.5), the Xpress Solver Engine (based on Xpress 30.1), and the Artelys Knitro Solver Engine (based on Knitro 10.3).
New Distribution and Correlation Features in Simulation
V2017-R2 introduces a new family of probability distributions, called the metalog distributions, even more general than the Pearson distributions -- members of this family can be chosen based directly on historical data (even just a few observations), without a distribution fitting process.
And it's now possible to fit copula parameters to historical data – a complement to distribution fitting that is sometimes called “correlation fitting.”
Creating Power BI Custom Visuals
You simply select rows or columns of data to serve as changeable parameters, then choose Create App – Power BI, and save the file created by V2017-R2. You click the Load Custom Visual icon in Power BI, and select the file you just saved. What you get isn’t just a chart – it’s your full optimization or simulation model, ready to accept Power BI data, run on demand on the web, and display visual results in Power BI! You simply need to drag and drop appropriate Power BI datasets into the “well” of inputs to match your model parameters.