• Announcing What'sBest 18 beta, LINGO 20 beta and LINDO® API 14 beta releases with faster solvers and new features.

    Announcing What'sBest 18 beta, LINGO 20 beta and LINDO® API 14 beta releases with faster solvers and new features.

  • What'sBest! 17.0 - Excel Add-In for Linear, Nonlinear, and Integer Modeling and Optimization

    What'sBest! 17.0 - Excel Add-In for Linear, Nonlinear, and Integer Modeling and Optimization

  • LINGO 19.0 - Optimization Modeling Software for Linear, Nonlinear, and Integer Programming

    LINGO 19.0 - Optimization Modeling Software for Linear, Nonlinear, and Integer Programming

  • LINDO® API 13.0 - Powerful Library of Optimization Solvers and Mathematical Programming Tools

    LINDO® API 13.0 - Powerful Library of Optimization Solvers and Mathematical Programming Tools

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  • What'sBest!

    What'sBest! lets you build linear, nonlinear, and integer models in Excel. Models are easy to build and understand using standard spreadsheet equations.


  • LINGO

    LINGO is a comprehensive tool designed to help you build and solve linear, optimization models quickly, easily, and efficiently.



  • LINDO® API

    LINDO® API creates optimization applications. It allows you to plug the power of the LINDO® solver right into customized applications that you have written.

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Announcing LINGO 20 : New Features 

 

      + Improved API  interface. It is now much easier to incorporate your LINGO

         model(s) into your own custom designed system. 

         The standard LINGO distribution has examples to illustrate how to do this.

 

      + LINGO in Excel.  It is now easy to integrate a LINGO model into an Excel 

           spreadsheet.  The end user sees it as just a smart Excel spreadsheet that can solve say, 

           a cutting stock problem for metal fabrication, or vehicle routing and delivery problems, or 

           a supply chain shipping assignment problem, and more.

 

      + New REST API  distributed computing interface. If you want to have an

         optimization based application accessible on the web from smart phones and

         other devices, this is now easier to do.

 

      + Improved capability to create a  Docker Image.  If you like to be able to move an 

         application from one server to another with minimum hassle, Docker has proven to

         be a popular way of achieving such portability. 

 

      + Improved interface to R. The R statistical package is a popular way of doing 

         statistical analysis.  It is now much easier to establish an interface between your 

         data in R and your optimization model in LINGO.

 

      + Improved interface to Python.  Python is one of the most popular languages for  

         doing general computations. It is now much easier to establish an interface

         between your Python application and your optimization model in LINGO.

 

      + Improved support for implied set names such as J01..J99,  Jan..Oct, etc.

 

+ Improved ODBC connection to databases allowing import of sets (in addition to 

   attributes) within Calc sections.

 

+ Ability to generate alternate optimal solutions to linear programming models.

   This may be done interactively, or programmatically within Calc sections using the 

   @NEXTALTOPT() function.

 

Performance Improvements 

+  Linear and Integer Solver Improvements.

Improved heuristics for general integer programs.

Average performance improvement of 2-3% on our standard test set.

 

+  Nonlinear and Global Solver Improvements

Faster (order of magnitude) solution of linear fractional programs (ratio 

     objectives)

 

+  Support for additional useful but “problematic” functions: 

    Power utility function (x^g-1)/g  and

    exponential ratio function (exp(g) – 1)/g, are important in some 

    situations modeling consumer behavior. The solver can now robustly avoid the 

    numerical problems that would otherwise occur when g approaches 0.

 

 + Linearization Improvements

More expressions can be automatically linearized, so you can now use a fast

     linear solver where otherwise a much slower (30x?) nonlinear solver might be

     required. Improved linearization of certain IF expressions. 

 

 

 

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