lpsolve vs Linear Program Solver

Struggling to choose between lpsolve and Linear Program Solver? Both products offer unique advantages, making it a tough decision.

lpsolve is a Development solution with tags like linear-programming, solver, optimization, modeling-language.

It boasts features such as Formulate linear programming models, Solve models with revised simplex method, Solve models with primal-dual interior point method, Handle integer variables, Import/export models in MPS format, Scriptable using Python and other languages and pros including Open source and free to use, Available on many platforms, Fast and robust performance, Supports integer programming, Can handle large models.

On the other hand, Linear Program Solver is a Office & Productivity product tagged with linear-programming, optimization, operations-research, simplex-method.

Its standout features include Solves linear programming problems, Supports defining an objective function to optimize, Allows specifying linear equality and inequality constraints, Provides sensitivity analysis tools, Offers APIs for integration with other applications, and it shines with pros like Efficient at solving large-scale linear programs, Can handle thousands of variables and constraints, Finds provably optimal solutions, Widely used and well-tested algorithms, Flexible modeling capabilities.

Feature	lpsolve	Linear Program Solver
Overall Score	N/A	N/A
Primary Category	Development	Office & Productivity

Product Overview

lpsolve

Description: lpsolve is an open-source linear programming solver. It allows formulating linear optimization problems in a simple modeling language and solving them with either the revised simplex method or the primal-dual interior point method.

Linear Program Solver

Description: A linear program solver is a software tool used to solve optimization problems with linear constraints and objectives. It takes a linear program formulated with variables, constraints, and an objective, then uses algorithms like the simplex method to find the optimal solution.

Key Features Comparison

lpsolve Features

• Formulate linear programming models
• Solve models with revised simplex method
• Solve models with primal-dual interior point method
• Handle integer variables
• Import/export models in MPS format
• Scriptable using Python and other languages

Linear Program Solver Features

• Solves linear programming problems
• Supports defining an objective function to optimize
• Allows specifying linear equality and inequality constraints
• Provides sensitivity analysis tools
• Offers APIs for integration with other applications

Pros & Cons Analysis

lpsolve

Pros

• Open source and free to use
• Available on many platforms
• Fast and robust performance
• Supports integer programming
• Can handle large models

Cons

• Limited documentation and support
• User interface is not very intuitive
• Does not support more advanced modeling features
• Not as full-featured as commercial solvers

Linear Program Solver

Pros

• Efficient at solving large-scale linear programs
• Can handle thousands of variables and constraints
• Finds provably optimal solutions
• Widely used and well-tested algorithms
• Flexible modeling capabilities

Cons

• Limited to linear models only
• Requires problem formulation expertise
• Not as intuitive for non-technical users
• Advanced features may require programming skills
• Solutions can be hard to interpret

Pricing Comparison

lpsolve

• Open Source

Linear Program Solver

• Free
• Freemium
• One-time Purchase
• Subscription-Based