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Frontline Systems Analytic Solver Platform with Simulation/Risk Analysis, Optimization Enhancements

Frontline Systems Analytic Solver Platform with Simulation/Risk Analysis, Optimization Enhancements

Frontline Systems, Version 2016-R2 of its Solvers for Excel, release includes wide-ranging enhancements in Monte Carlo simulation and optimization technology, plus the ability to convert Excel analytic models into free-standing web and mobile applications. “We’re fulfilling our promise of ‘no-compromise analytic power’ for Excel users,” said Daniel Fylstra, Frontline’s President and CEO. “Business analysts can build models with point-and-click ease of use, while taking advantage of the most advanced analytic algorithms and methods.” In just four months since release of Analytic Solver Platform V2016, Frontline has added functionality comparable to a years’ worth of many competitors’ offerings.

The new release features significant enhancements to Frontline’s popular Evolutionary Solver, focusing on models with integer variables – often yielding dramatically better solutions in a given amount of time, compared to Frontline’s previous releases and competitive products. These enhancements build on the SQP-GS (Sequential Quadratic Programming with Gradient Sampling) for local search, and Feasibility Pump algorithms introduced in Frontline Solvers V2016.

The new release also includes performance enhancements for multi-dimensional optimization and simul¬ation models that use the Dimensional Modeling features of Analytic Solver Platform, first introduced in Frontline Solvers V2014, in the frequently-occurring case where multi-dimensional data is ‘sparse’. Memory use and time computing multi-dimensional formulas is dramatically reduced for these models.

In Monte Carlo simulation, the V2016-R2 release supports correlation of uncertain variables with dissimilar distributions, using Gaussian, Student and Archimedean (Clayton, Frank and Gumbel) copulas – complementing existing support for rank-order correlation. Copulas for correlation are popular in quantitative finance models. All the methods are available are available via a simple point-and-click correlation dialog that allows the user to preview scatter plots showing how samples from uncertain variables will be generated and correlated.

The new release also supports ‘compound distributions’ – often used in actuarial and insurance models – where samples are generated and aggregated from multiple instances of a probability distribution – called the ‘severity’ distribution. The number of instances can be a constant or a sample from another, discrete probability distribution, called the ‘frequency’ distribution. Nearly all of Analytic Solver’s 50+ analytic distributions can be used, and for many common distributions, the software uses analytic methods in lieu of repeated sampling for speed.

Also enhanced in the new release is support for export of analytic model results to Tableau, the popular data visualization software, complementing support first introduced in Frontline Solvers V2015. The new release maintains support for Tableau Data Extract (TDE) files, but it can also generate model results in HTML/CSS/JavaScript form that can be consumed directly by the Web Data Connector introduced in Tableau 9.1.

Frontline Solvers V2016-R2 builds on the powerful ‘Create App’ feature introduced in Frontline’s V2016 release that opens up the world of web and mobile apps to Excel users. Business analysts can build a worksheet with Excel formulas, point and click to define elements of an optimization or simulation/risk analysis model; then a single click to ‘Create App’, and a second click to choose where the model will run, is enough to create a basic ‘app’ that embeds in JavaScript, contains the user’s model, and solves problems via REST API calls to Frontline’s Azure-based RASON server.

The ‘Create App’ feature translates Excel models into Frontline’s new RASON modeling language, introduced in 2015, which contains the entire Excel formula language – with nearly 500 built-in Excel functions – plus higher-level multi-dimensional extensions. Because a RASON model is embedded in JSON – JavaScript Object Notation – it is valid in JavaScript code on Web pages. Users can easily edit and modify RASON models, and run them on desktops or laptops, or in the cloud.

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