LMIs in Control/pages/LMI for Linear Programming

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LMI for Linear Programming

Linear programming has been known as a technique for the optimization of a linear objective function subject to linear equality or inequality constraints. The feasible region for this problem is a convex polytope. This region is defined as a set of the intersection of many finite half-spaces which are created by the inequality constraints. The solution for this problem is to find a point in the polytope of existing solutions where the objective function has its extremum (minimum or maximum) value.

The System[edit | edit source]

We define the objective function as:

and constraints of the problem as:




The Data[edit | edit source]

Suppose that , , and are given parameters where and . Moreover, is an vector of positive variables.

The Optimization Problem[edit | edit source]

The optimization problem is to minimize the objective function, when the aforementioned linear constraints are satisfied.

The LMI: LMI for linear programming[edit | edit source]

The mathematical description of the optimization problem can be readily written in the following LMI formulation:

Conclusion:[edit | edit source]

Solving this problem results in the values of variables which minimize the objective function. It is also worthwhile to note that if , the computational cost for solving this problem would be .

There does not exist an analytical formulation to solve a general linear programming problem. Nonetheless, there are some efficient algorithms, like the Simplex algorithm, for solving a linear programming problem.

Implementation[edit | edit source]

A link to Matlab codes for this problem in the Github repository:


Related LMIs[edit | edit source]

LMI for Feasibility Problem

External Links[edit | edit source]

  • [1] - LMI in Control Systems Analysis, Design and Applications

Return to Main Page[edit | edit source]

LMIs in Control/Tools