LocalSolver is a new-generation, hybrid mathematical programming solver. LocalSolver combines the best of all optimization technologies to solve your problem at hand. Using LocalSolver, you can now tackle ultra-large real-life nonlinear problems in a model-and-run fashion without any tuning. Because efficiency is nothing without reliability and robustness, we ensure our clients the best quality of products and services thanks to a drastic continuous integration methodology coupled with a responsive and dedicated support.
New-generation, hybrid solver
LocalSolver combines the best of all optimization techniques: local search, constraint propagation and inference, linear and mixed-integer programming, as well as nonlinear programming techniques.
Contrarily to other math programming software, LocalSolver is not based on a single optimization technique. LocalSolver hybridizes different optimization techniques dynamically, during the resolution, thanks to a unique hybrid neighborhood search approach. LocalSolver combines local search techniques, constraint propagation and inference techniques, linear and mixed-integer programming techniques, as well as nonlinear programming techniques, to solve your problem at best.
Moreover, LocalSolver is the first math programming solver integrating pure and direct local search techniques for combinatorial and continuous optimization. In this way, LocalSolver is able to solve models involving millions of variables which are out of scope of classical solvers, in particular mixed-integer linear programming (MIP) solvers.
Scalability of OR solution technologies
Innovative math modeling language
LocalSolver comes with a powerful modeling language. It enables you to quickly prototype combinatorial optimization applications.
The LocalSolver Programming language (LSP) offers an efficient programming style: dynamic but strongly typing, implicit variable declaration, compact looping syntax, etc. Many functions can be used both for mathematical modeling or for programming, making the language easy to learn.
Our goal is to reduce your programming effort as much as possible (efficiency), while framing your prototyping work (reliability). You will see that the resulting LSP models are less verbose and more readable than the ones written with existing modeling languages.
A piece of LSP model
Lightweight object-oriented APIs
To fully integrate LocalSolver in your business applications, we provide easy-to-use object-oriented programming interfaces for Python, C++, Java, .NET.
LocalSolver's APIs are lightweight, exposing only a few classes. The corresponding callable libraries (Python, C++, Java, .NET) are provided common operating systems (Windows, Linux, Mac OS) and common architectures (x86, x64). LocalSolver's x64 binaries offer full 64-bit capabilities: some clients tackle models involving 50 GB of RAM, with about 50 million variables.
Passing from LSP to APIs is easy: you have to concentrate on your math optimization model only. You do not have to decompose your problem, you do not have to tune the solver, nor even to write additional specific codes, to solve large-scale real-world optimization problems in minutes.
LocalSolver's API object model