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

We define the objective function as:

c1x1+c2x2+...cnxn

and constraints of the problem as:

a11x1+a12x2+...+a1nxn<b1

a21x1+a22x2+...+a2nxn<b2

.

.

.

am1x1+am2x2+...+amnxn<bm

The Data

Suppose that cjn, aijn, and bim are given parameters where i=1,2,...,m and j=1,2,...,n. Moreover, x=[x1x2...xn]T is an n×1 vector of positive variables.

The Optimization Problem

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

The LMI: LMI for linear programming

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

mincTxs.t.aiTxbix

Conclusion:

Solving this problem results in the values of variables x which minimize the objective function. It is also worthwhile to note that if mn, the computational cost for solving this problem would be mn2.

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

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

https://github.com/asalimil/LMI-for-Linear-Programming

LMI for Feasibility Problem

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


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