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.
v17.0.0.78 [Apr 4, 2017]
- Enhancements to the Simplex solvers boost performance on linear models. Large models solve an average of 20% faster using primal simplex and 15% faster for dual simplex.
- New symmetry detection capabilities dramatically reduce the time required to prove optimality on certain classes of models with integer variables.
- Stability and robustness of the Global solver has been improved through several enhancements to quadratic recognition and range reduction.
- Several new functions and constraint types are recognized, e.g., the @AllDiff constraint for general integer variables.
- New capability to pass arguments to user-defined procedures.
- Support of data frame style input in Data and Calc sections has been added.
- New functions for programmatically reading input data in Calc sections.
- New function for retrieving the next best solution to a binary integer programming model.
