理论
Lemma
1.
(
S-
Procedure[
34]
)
:
Define
the
quadratic
func-
\textbf{Lemma 1. ( S- Procedure[ 34] ) : Define the quadratic func- }
Lemma 1. ( S- Procedure[ 34] ) : Define the quadratic func-
tions w.r.t.
x
∈
C
M
×
1
\mathbf{x}\in\mathbb{C}^M\times1
x∈CM×1 as
f
m
(
x
)
=
x
H
A
m
x
+
2
R
e
{
b
m
H
x
}
+
c
m
,
m
=
1
,
2
,
f_m\left(\mathbf{x}\right)=\mathbf{x}^H\mathbf{A}_m\mathbf{x}+2Re\left\{\mathbf{b}_m^H\mathbf{x}\right\}+c_m,m=1,2,
fm(x)=xHAmx+2Re{bmHx}+cm,m=1,2,
where
A
m
∈
C
M
×
M
,
b
m
∈
C
M
×
1
,
a
n
d
\mathbf{A}_m\in\mathbb{C}^{M\times M},\mathbf{b}_m\in\mathbb{C}^{M\times1},and
Am∈CM×M,bm∈CM×1,and
c
m
∈
R
.
The
c_m\in \mathbb{R} . \textit{The}
cm∈R.The
c o n d i t i o n f 1 ≤ 0 ⇒ f 2 ≤ 0 h o l d s i f a n d o n l y i f t h e r e e x i s t s condition~f_1\leq0\Rightarrow f_2\leq0~holds~if~and~only~if~there~exists condition f1≤0⇒f2≤0 holds if and only if there exists
a variable ω ≥ 0 such that a\textit{ variable }\omega \geq 0\textit{ such that} a variable ω≥0 such that
(19)
ω
[
A
1
b
1
b
1
H
c
1
]
−
[
A
2
b
2
b
2
H
c
2
]
⪰
0
M
+
1
.
\omega\begin{bmatrix}\mathbf{A}_1&\mathbf{b}_1\\\mathbf{b}_1^H&c_1\end{bmatrix}-\begin{bmatrix}\mathbf{A}_2&\mathbf{b}_2\\\mathbf{b}_2^H&c_2\end{bmatrix}\succeq\mathbf{0}_{M+1}.
ω[A1b1Hb1c1]−[A2b2Hb2c2]⪰0M+1.
理论重述
Let
f
(
x
)
f(x)
f(x) and
g
(
x
)
g(x)
g(x) be two quadratic forms defined as:
f
(
x
)
=
x
H
A
x
+
2
ℜ
(
b
H
x
)
+
c
f(x) = x^H A x + 2 \Re(b^H x) + c
f(x)=xHAx+2ℜ(bHx)+c
and
g
(
x
)
=
x
H
D
x
+
2
ℜ
(
e
H
x
)
+
f
g(x) = x^H D x + 2 \Re(e^H x) + f
g(x)=xHDx+2ℜ(eHx)+f
where
A
,
D
∈
C
n
×
n
A, D \in \mathbb{C}^{n \times n}
A,D∈Cn×n are Hermitian matrices,
b
,
e
∈
C
n
b, e \in \mathbb{C}^n
b,e∈Cn are complex vectors, and
c
,
f
∈
R
c, f \in \mathbb{R}
c,f∈R are real constants. The superscript
H
H
H denotes the Hermitian (conjugate transpose) of the matrix or vector.
The implication
f
(
x
)
≤
0
⟹
g
(
x
)
≤
0
f(x) \leq 0 \implies g(x) \leq 0
f(x)≤0⟹g(x)≤0
holds if and only if there exists a scalar
λ
≥
0
\lambda \geq 0
λ≥0 such that:
f
(
x
)
+
λ
g
(
x
)
≤
0
f(x) + \lambda g(x) \leq 0
f(x)+λg(x)≤0
or equivalently:
x
H
(
A
+
λ
D
)
x
+
2
ℜ
(
(
b
+
λ
e
)
H
x
)
+
(
c
+
λ
f
)
≤
0
for all
x
.
x^H (A + \lambda D) x + 2 \Re \left( (b + \lambda e)^H x \right) + (c + \lambda f) \leq 0 \quad \text{for all } x.
xH(A+λD)x+2ℜ((b+λe)Hx)+(c+λf)≤0for all x.
This condition can be rewritten as the following matrix inequality:
(
A
+
λ
D
b
+
λ
e
(
b
+
λ
e
)
H
c
+
λ
f
)
⪰
0
\begin{pmatrix} A + \lambda D & b + \lambda e \\ (b + \lambda e)^H & c + \lambda f \end{pmatrix} \succeq 0
(A+λD(b+λe)Hb+λec+λf)⪰0
where
⪰
0
\succeq 0
⪰0 denotes that the matrix is positive semidefinite (PSD).
Thus, the S-Procedure states that if such a non-negative λ \lambda λ exists, then the implication f ( x ) ≤ 0 ⟹ g ( x ) ≤ 0 f(x) \leq 0 \implies g(x) \leq 0 f(x)≤0⟹g(x)≤0 holds.
实际案例
已知
Δ
h
H
Δ
h
≤
a
\Delta\mathbf{h}^H \Delta\mathbf{h} \leq a
ΔhHΔh≤a
如何根据S-Procedure 理论把下列形式转化成线性矩阵不等式呢
g
(
Δ
h
)
=
Δ
h
H
D
Δ
h
+
2
ℜ
(
h
H
D
Δ
h
)
+
h
H
D
h
−
z
≥
0
g(\Delta\mathbf{h}) = \Delta\mathbf{h}^H \mathbf{D} \Delta\mathbf{h} + 2 \Re(\mathbf{h}^H \mathbf{D} \Delta\mathbf{h}) + \mathbf{h}^H \mathbf{D} \mathbf{h} - z \geq 0
g(Δh)=ΔhHDΔh+2ℜ(hHDΔh)+hHDh−z≥0
实际案例详细说明
\section*{S-Procedure 推导}
\textbf{已知条件}
-
不等式 f 1 ( Δ h ) f_1(\Delta \mathbf{h}) f1(Δh):
f 1 ( Δ h ) = Δ h H Δ h − a ≤ 0 f_1(\Delta \mathbf{h}) = \Delta \mathbf{h}^H \Delta \mathbf{h} - a \leq 0 f1(Δh)=ΔhHΔh−a≤0
这表示:
Δ h H Δ h ≤ a \Delta \mathbf{h}^H \Delta \mathbf{h} \leq a ΔhHΔh≤a -
需要证明的不等式 f 2 ( Δ h ) f_2(\Delta \mathbf{h}) f2(Δh):
f 2 ( Δ h ) = − ( Δ h H D Δ h + 2 ℜ ( h H D Δ h ) + h H D h − z ) ≤ 0 f_2(\Delta \mathbf{h}) = - \left( \Delta \mathbf{h}^H \mathbf{D} \Delta \mathbf{h} + 2 \Re (\mathbf{h}^H \mathbf{D} \Delta \mathbf{h}) + \mathbf{h}^H \mathbf{D} \mathbf{h} - z \right) \leq 0 f2(Δh)=−(ΔhHDΔh+2ℜ(hHDΔh)+hHDh−z)≤0
等价于:
Δ h H D Δ h + 2 ℜ ( h H D Δ h ) + h H D h − z ≥ 0 \Delta \mathbf{h}^H \mathbf{D} \Delta \mathbf{h} + 2 \Re (\mathbf{h}^H \mathbf{D} \Delta \mathbf{h}) + \mathbf{h}^H \mathbf{D} \mathbf{h} - z \geq 0 ΔhHDΔh+2ℜ(hHDΔh)+hHDh−z≥0
\textbf{应用 S-Procedure}
为了应用 S-Procedure,我们需要构造两个二次型 f 1 f_1 f1 和 f 2 f_2 f2 的矩阵形式,并构造相应的线性矩阵不等式 (LMI)。
-
构造 f 1 ( Δ h ) f_1(\Delta \mathbf{h}) f1(Δh) 的矩阵形式:
f 1 ( Δ h ) = Δ h H Δ h − a f_1(\Delta \mathbf{h}) = \Delta \mathbf{h}^H \Delta \mathbf{h} - a f1(Δh)=ΔhHΔh−a
其矩阵形式为:
[ I 0 0 − a ] \begin{bmatrix} \mathbf{I} & \mathbf{0} \\ \mathbf{0} & -a \end{bmatrix} [I00−a] -
构造 f 2 ( Δ h ) f_2(\Delta \mathbf{h}) f2(Δh) 的矩阵形式:
f 2 ( Δ h ) = − ( Δ h H D Δ h + 2 ℜ ( h H D Δ h ) + h H D h − z ) f_2(\Delta \mathbf{h}) = - \left( \Delta \mathbf{h}^H \mathbf{D} \Delta \mathbf{h} + 2 \Re (\mathbf{h}^H \mathbf{D} \Delta \mathbf{h}) + \mathbf{h}^H \mathbf{D} \mathbf{h} - z \right) f2(Δh)=−(ΔhHDΔh+2ℜ(hHDΔh)+hHDh−z)
可以简化为:
f 2 ( Δ h ) = − Δ h H D Δ h − 2 ℜ ( h H D Δ h ) − ( h H D h − z ) f_2(\Delta \mathbf{h}) = - \Delta \mathbf{h}^H \mathbf{D} \Delta \mathbf{h} - 2 \Re (\mathbf{h}^H \mathbf{D} \Delta \mathbf{h}) - (\mathbf{h}^H \mathbf{D} \mathbf{h} - z) f2(Δh)=−ΔhHDΔh−2ℜ(hHDΔh)−(hHDh−z)
其矩阵形式为:
[ − D − D h − h H D − ( h H D h − z ) ] \begin{bmatrix} -\mathbf{D} & -\mathbf{D} \mathbf{h} \\ -\mathbf{h}^H \mathbf{D} & -(\mathbf{h}^H \mathbf{D} \mathbf{h} - z) \end{bmatrix} [−D−hHD−Dh−(hHDh−z)] -
构造 S-Procedure 矩阵:
根据 S-Procedure,存在
μ
≥
0
\mu \geq 0
μ≥0 使得:
μ
[
I
0
0
−
a
]
−
[
−
D
−
D
h
−
h
H
D
−
(
h
H
D
h
−
z
)
]
⪰
0
\mu \begin{bmatrix} \mathbf{I} & \mathbf{0} \\ \mathbf{0} & -a \end{bmatrix} - \begin{bmatrix} -\mathbf{D} & -\mathbf{D} \mathbf{h} \\ -\mathbf{h}^H \mathbf{D} & -(\mathbf{h}^H \mathbf{D} \mathbf{h} - z) \end{bmatrix} \succeq \mathbf{0}
μ[I00−a]−[−D−hHD−Dh−(hHDh−z)]⪰0
进一步简化为:
[
μ
I
+
D
D
h
h
H
D
−
μ
a
+
(
h
H
D
h
−
z
)
]
⪰
0
\begin{bmatrix} \mu \mathbf{I} + \mathbf{D} & \mathbf{D} \mathbf{h} \\ \mathbf{h}^H \mathbf{D} & -\mu a + (\mathbf{h}^H \mathbf{D} \mathbf{h} - z) \end{bmatrix} \succeq \mathbf{0}
[μI+DhHDDh−μa+(hHDh−z)]⪰0