Linear Matrix Inequalities
Linear Matrix Inequalities (LMIs) and LMI techniques have emerged as powerful design tools in areas ranging from control engineering to system identification and structural design. Three factors make LMI techniques appealing:
- A variety of design specifications and constraints can be expressed as LMIs. 
- Once formulated in terms of LMIs, a problem can be solved exactly by efficient convex optimization algorithms (see LMI Solvers). 
- While most problems with multiple constraints or objectives lack analytical solutions in terms of matrix equations, they often remain tractable in the LMI framework. This makes LMI-based design a valuable alternative to classical “analytical” methods. 
Tools
| LMI Editor | Specify or display systems of LMIs as MATLAB expressions | 
Functions
Topics
- Linear Matrix InequalitiesLinear Matrix Inequalities (LMIs) and LMI techniques are powerful design tools in areas ranging from control engineering to system identification and structural design. 
- LMI ApplicationsApplications of LMIs include robust stability, optimal LQG control, estimation, and many others. 
- Tools for Specifying and Solving LMIsThe LMI Lab blends tools for the specification and manipulation of LMIs with powerful LMI solvers for three generic LMI problems. 
- Specifying a System of LMIs
 To specify a system of LMIs, declare the dimensions and structure of each matrix variable, and then describe the terms of each LMI. 
- LMI SolversThere is a solver for each of the three generic optimization problems. 
- Minimize Linear Objectives Under LMI ConstraintsSolve an optimization problem using the mincxsolver.
- Conversion Between Decision and Matrix VariablesLMI solvers optimize a vector of the free scalar entries of the matrix variables. These entries are called the decision variables. 
- Validating ResultsUse evallmiandshowlmito analyze and validate the results of an LMI optimization.
- Advanced LMI TechniquesLMI Lab supports structured matrix variables, complex-valued LMIs, custom objectives.