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*A* linear matrix inequality (LMI) is any
constraint of the form

A(x) := A_{0} + x_{1}A_{1} + ... + x < 0_{N}A_{N} | (3-1) |

where

*x*= (*x*_{1}, . . . ,*x*) is a vector of unknown scalars (the_{N}*decision*or*optimization*variables)*A*_{0}, . . . ,*A*are given_{N}*symmetric*matrices< 0 stands for "negative definite," i.e., the largest eigenvalue of

*A*(*x*) is negative

Note that the constraints *A*(*x*)
> 0 and *A*(*x*) < *B*(*x*)
are special cases of Equation 3-1 since they can be rewritten as –*A*(*x*)
< 0 and *A*(*x*)* –
B*(*x*) < 0, respectively.

The LMI of Equation 3-1 is
a convex constraint on *x* since *A*(*y*)
< 0 and *A*(*z*) < 0 imply
that $$A\left(\frac{y+z}{2}\right)<0$$. As a result,

Its solution set, called the

*feasible set*, is a convex subset of*R*^{N}Finding a solution

*x*to Equation 3-1, if any, is a convex optimization problem.

Convexity has an important consequence: even though Equation 3-1 has no analytical solution in general, it can be solved numerically with guarantees of finding a solution when one exists. Note that a system of LMI constraints can be regarded as a single LMI since

$$\{\begin{array}{c}{A}_{1}\left(x\right)<0\\ \vdots \\ {A}_{K}\left(x\right)<0\end{array}$$

is equivalent to

$$A\left(x\right):=\text{diag}\left({\text{A}}_{\text{1}}\left(x\right),\dots ,{\text{A}}_{\text{K}}\left(x\right)\right)<0$$

where diag (*A*_{1}(*x*),
. . . , *A*_{K}(*x*))
denotes the block-diagonal matrix with *A*_{1}(*x*),
. . . , *A*_{K}(*x*)
on its diagonal. Hence multiple LMI constraints can be imposed on
the vector of decision variables *x* without destroying
convexity.

In most control applications, LMIs do not naturally arise in the canonical form of Equation 3-1 , but rather in the form

*L*(*X*_{1},
. . . , *X*_{n}) < *R*(*X*_{1},
. . . , *X*_{n})

where *L*(.) and *R*(.)
are affine functions of some structured *matrix* variables *X*_{1},
. . . , *X*_{n}. *A* simple
example is the Lyapunov inequality

A + ^{T}XXA < 0 | (3-2) |

where the unknown *X* is a symmetric matrix.
Defining *x*_{1}, . . . , *x*_{N} as
the independent scalar entries of *X*, this LMI
could be rewritten in the form of Equation 3-1. Yet it is more convenient and efficient
to describe it in its natural form Equation 3-2, which is the approach taken in the
LMI Lab.

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