argminmatlab代码-A3DMM:NeurIPS2019论文“TrajectoryofAlternatingDirectionMeth
argmin
matlab代码A3DMM
-
ADMM
的自适应加速
复现论文结果的matlab代码
,
,
2019
ADMM
考虑以下约束和复合优化问题
$$
\min_{x
\in
\mathbb{R}^n
,
y\in
\mathbb{R}^m
}
~
R(x)
+
J(y)
\quad
\textrm{这样~那}
\quad
Ax
+
By
=
b
,
$$
其中强加以下基本假设
$R,
J$
是适当的凸函数和下半连续函数。
$A
:
\mathbb{R}^n
\to
\mathbb{R}^p
$
和
$B
:
\mathbb{R}^m
\to
\mathbb{R}^p$
是{内射}线性运算符。
$\mathrm{ri}
(
\mathrm{dom}(R)
\cap
\mathrm{dom}(J)
)
\neq
\emptyset$，并且最小化集合非空。
与
(1)
相关联的增广拉格朗日数为
$$
\mathcal{L}(x,y;
\psi)
=
R(x)
+
J(y)
+
\langle{\psi,
Ax+By-b}\rangle
+
\
tfrac{\gamma}{2}|
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