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LINGO is an integrated optimization modeling software for linear, nonlinear, integer, and stochastic problems. It offers a powerful modeling language, built-in solvers, and seamless data integration with spreadsheets and databases, enabling fast, efficient development and analysis of complex mathematical models.

Vendor

Vendor

LINDO Systems

Company Website

Company Website

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Product details

LINGO

LINGO is a powerful optimization modeling software designed to simplify and accelerate the development and solution of mathematical models. It supports a wide range of problem types including linear, nonlinear, integer, quadratic, stochastic, and global optimization. LINGO integrates a modeling language, development environment, and built-in solvers into a single package, making it ideal for analysts, researchers, and decision-makers across industries.

Features

  • Intuitive Modeling Language: Express models using summations and subscripted variables in a readable, paper-like format.
  • Integrated Solvers: Automatically selects and runs the appropriate solver for the model type, including simplex, barrier, nonlinear, global, and stochastic solvers.
  • Data Connectivity: Imports and exports data directly from spreadsheets and databases for seamless integration.
  • Interactive and Programmatic Use: Build and solve models interactively or call LINGO from external applications via DLL, OLE, or Excel macros.
  • Multi-Core Processing: Utilizes multiple CPU cores for faster model generation and solution.
  • Extensive Documentation: Comes with a comprehensive user manual and modeling textbook, plus dozens of sample models.

Capabilities

  • Linear and Integer Programming: Solve large-scale LP and MIP problems with advanced preprocessing and cut generation.
  • Nonlinear and Global Optimization: Use GRG, SLP, and branch-and-bound techniques to find local or global optima.
  • Quadratic and Conic Models: Efficiently solve models with quadratic objectives or constraints, including SOCPs.
  • Stochastic Programming: Model uncertainty with multistage stochastic optimization using Benders decomposition and deterministic equivalents.
  • Model Reduction and Linearization: Automatically simplify models and convert nonsmooth functions into linear equivalents for faster solving.

Benefits

  • Speed and Efficiency: Reduces development time and accelerates solution processes with built-in solvers and preprocessing.
  • Flexibility: Handles a wide variety of optimization problems in a single environment.
  • Ease of Use: Designed for both novice and expert users with intuitive syntax and extensive help resources.
  • Scalability: Suitable for small models and large enterprise-scale optimization tasks.
  • Integration: Easily connects with external applications and data sources for end-to-end decision support.