YALMIP is a modelling language for advanced modeling and solution of convex and nonconvex optimization problems. It is implemented as a free (as in no charge) toolbox for MATLAB.

The main motivation for using YALMIP is rapid algorithm development. The language is consistent with standard MATLAB syntax, thus making it extremely simple to use for anyone familiar with MATLAB.

Another benefit of YALMIP is that it implements a large amount of modeling tricks, allowing the user to concentrate on the high-level model, while YALMIP takes care of the low-level modeling to obtain as efficient and numerically sound models as possible.

Problem classes

The modelling language supports a large number of optimization classes, such as linear, quadratic, second order cone, semidefinite, mixed integer conic, geometric, local and global polynomial, multiparametric, bilevel and robust programming.


One of the central ideas in YALMIP is to concentrate on the language and the higher level algorithms, while relying on external solvers for the actual computations. However, YALMIP also implements internal algorithms for global optimization, mixed integer programming, multiparametric programming, sum-of-squares programming and robust optimization. These algorithms are typically based on the low-level scripting language available in YALMIP, and solve sub-problems using the external solvers.


Johan Lfberg