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A real square matrix
A
{\displaystyle A}
is monotone (in the sense of Collatz ) if for all real vectors
v
{\displaystyle v}
,
A
v
≥
0
{\displaystyle Av\geq 0}
implies
v
≥
0
{\displaystyle v\geq 0}
, where
≥
{\displaystyle \geq }
is the element-wise order on
R
n
{\displaystyle \mathbb {R} ^{n}}
.[1]
Properties [ edit ]
A monotone matrix is nonsingular.[1]
Proof : Let
A
{\displaystyle A}
be a monotone matrix and assume there exists
x
≠
0
{\displaystyle x\neq 0}
with
A
x
=
0
{\displaystyle Ax=0}
. Then, by monotonicity,
x
≥
0
{\displaystyle x\geq 0}
and
−
x
≥
0
{\displaystyle -x\geq 0}
, and hence
x
=
0
{\displaystyle x=0}
.
◻
{\displaystyle \square }
Let
A
{\displaystyle A}
be a real square matrix.
A
{\displaystyle A}
is monotone if and only if
A
−
1
≥
0
{\displaystyle A^{-1}\geq 0}
.[1]
Proof : Suppose
A
{\displaystyle A}
is monotone. Denote by
x
{\displaystyle x}
the
i
{\displaystyle i}
-th column of
A
−
1
{\displaystyle A^{-1}}
. Then,
A
x
{\displaystyle Ax}
is the
i
{\displaystyle i}
-th standard basis vector, and hence
x
≥
0
{\displaystyle x\geq 0}
by monotonicity. For the reverse direction, suppose
A
{\displaystyle A}
admits an inverse such that
A
−
1
≥
0
{\displaystyle A^{-1}\geq 0}
. Then, if
A
x
≥
0
{\displaystyle Ax\geq 0}
,
x
=
A
−
1
A
x
≥
A
−
1
0
=
0
{\displaystyle x=A^{-1}Ax\geq A^{-1}0=0}
, and hence
A
{\displaystyle A}
is monotone.
◻
{\displaystyle \square }
Examples [ edit ]
The matrix
(
1
−
2
0
1
)
{\displaystyle \left({\begin{smallmatrix}1&-2\\0&1\end{smallmatrix}}\right)}
is monotone, with inverse
(
1
2
0
1
)
{\displaystyle \left({\begin{smallmatrix}1&2\\0&1\end{smallmatrix}}\right)}
.
In fact, this matrix is an M-matrix (i.e., a monotone L-matrix ).
Note, however, that not all monotone matrices are M-matrices. An example is
(
−
1
3
2
−
4
)
{\displaystyle \left({\begin{smallmatrix}-1&3\\2&-4\end{smallmatrix}}\right)}
, whose inverse is
(
2
3
/
2
1
1
/
2
)
{\displaystyle \left({\begin{smallmatrix}2&3/2\\1&1/2\end{smallmatrix}}\right)}
.
See also [ edit ]
References [ edit ]