optimization
锥规划
YeeKal
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"#optimization"
Conic Programming
Here: - $c, x \in \mathbb{R}^n$ - $D:\mathbb{R}^n \rightarrow Y $ linear, $d\in Y$ for some Euclidean space $Y$ - $K\subseteq Y$ is a closed convex cone - write $x\preceq_K y$ for $y-x \in K$
锥规划的关注点是指约束条件为锥(相比于其它规划形式)
Second-order cone programming(SOCP)
Second-order cone:
SOCP:
where: $\mathcal{Q} = \mathcal{Q}{n1}\times \cdots \times \mathcal{Q}$
Observations:
- case $r = 1$ can be solved in closed-from
- $r\geq 2$ is not so easy
- $LP\subsetneq SOCP \subsetneq SDP$
Form transform
Second-order cone:
二阶是指二范数,比如对标准锥作仿射变换:
二次约束转化为锥约束: