Makes the solving of stochastic, linear, nonlinear, quadratic, etc. integer optimization models quicker and more effective. It embeds in-built solvers, an environment for building and editing problems as well as a language for optimization models.
Lingo is a powerful modeling program that integrates features which can be used in a wide range of fields such as: mathematics, physics, economy, automatic control, finances, etc. It allows you to create various model types and generates accurate solutions for them. Lingo comes with its own modeling language and a comprehensive help manual.
As an Automatic Control and Computer Engineering graduate, I found Lingo to be an efficient alternative to other popular modeling applications such as MatLab. It uses few system resources (CPU, memory, etc.) and generates professional solutions for any type of model that the user provides. Lingo also includes its own sample models for various purposes. They include comments that help users understand each function or algorithm that is being used.
I should mention that Lingo is recommended for users that have advanced modeling knowledge. It requires at least basic understanding of modeling principles. By opening the integrated help manual, users can access detailed information regarding the program features. Also, it proves to be useful when it comes to learning about various functions and commands.
Students as well as professionals from many fields can use Lingo to create models and generate solution for various problems.
v20.0 [Sep 30, 2022]
- 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.
