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LMI for Generalized Eigenvalue Problem

Technically, the generalized eigenvalue problem considers two matrices, like and , to find the generalized eigenvector, , and eigenvalues, , that satisfies . If the matrix is an identity matrix with the proper dimension, the generalized eigenvalue problem is reduced to the eigenvalue problem.

The System

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Assume that we have three matrice functions which are functions of variables as follows:

where are , , and () are the coefficient matrices.

The Data

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The , , and are matrix functions of appropriate dimensions which are all linear in the variable and , , are given matrix coefficients.

The Optimization Problem

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The problem is to find such that:

, , and are satisfied and is a scalar variable.

The LMI: LMI for Schur stabilization

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A mathematical description of the LMI formulation for the generalized eigenvalue problem can be written as follows:

Conclusion:

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The solution for this LMI problem is the values of variables such that the scalar parameter, , is minimized. In practical applications, many problems involving LMIs can be expressed in the aforementioned form. In those cases, the objective is to minimize a scalar parameter that is involved in the constraints of the problem.

Implementation

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

https://github.com/asalimil/LMI-for-Schur-Stability

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LMI for Generalized Eigenvalue Problem

LMI for Matrix Norm Minimization

LMI for Maximum Singular Value of a Complex Matrix

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


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