由以下代码片段生成:
Secondly, instead of simultaneously minimizing the mean and standard deviation of the objective(s),
another type of formulation of robust optimization considers the standard deviation as the constraint:
\begin{flalign}\label{eq:robust-optimization-2}
\text{Type II:}
\begin{cases}
\text{find} &\vect{x} \\
\text{minimizing} &\mu\left\{f_{i}(\vect{x}, \vect{p})\right\} (i = 1, ...\, , n_{\text{objectives}}) \\
\text{subject to} &\sigma\left\{f_{i}(\vect{x}, \vect{p})\right\} \leq \sigma_{i}^{\text{crit}} (i = 1, ...\, , n_{\text{objectives}}) \\
&L_{j}(\vect{x}, \vect{p}) \leq 0 (j = 1, ...\, , n_{\text{constraints}}) \\
&\vect{x}^{-} \leq \vect{x} \leq \vect{x}^{+}
\end{cases}
\end{flalign}
Thirdly, as previously discussed, in the robust design optimization problem as formulated by
答案1
只需使用equation
环境并嵌套aligned
案例:
\documentclass{article}
\usepackage{empheq}
\let\vect = \mathbf
\begin{document}
Secondly, instead of simultaneously minimizing the mean and standard deviation of the objective(s),
another type of formulation of robust optimization considers the standard deviation as the constraint:
\begin{equation}\label{eq:robust-optimization-2}
\text{Type II:}
\begin{cases}\begin{aligned}
& \text{find} & &\vect{x} \\
& \text{minimizing} & &\mu\left\{f_{i}(\vect{x}, \vect{p})\right\} & & (i = 1, ...\, , n_{\text{objectives}}) \\
& \text{subject to} & & \sigma\left\{f_{i}(\vect{x}, \vect{p})\right\} \leq \sigma_{i}^{\text{crit}} & & (i = 1, ...\, , n_{\text{objectives}}) \\
& & &L_{j}(\vect{x}, \vect{p}) \leq 0 & & (j = 1, ...\, , n_{\text{constraints}}) \\
& & &\vect{x}^{-} \leq \vect{x} \leq \vect{x}^{+}
\end{aligned}\end{cases}
\end{equation}
Thirdly, as previously discussed, in the robust design optimization problem as formulated by
\end{document}
答案2
在包中定义fleqn
环境nccmath
:
\documentclass{article}
\usepackage{bm}
\usepackage{nccmath}
\begin{document}
\begin{fleqn}
\begin{align}\label{eq:robust-optimization-2}
\text{Type II:}
\left\{\begin{aligned}
\text{find} &&& \bm{x} \\
\text{minimizing} &&& \mu\left\{f_{i}(\bm{x}, \bm{p})\right\}
&& (i = 1, ...\, , n_{\text{objectives}}) \\
\text{subject to} &&& \sigma\left\{f_{i}(\bm{x}, \bm{p})\right\} \leq \sigma_{i}^{\text{crit}}
&& (i = 1, ...\, , n_{\text{objectives}}) \\
&&& L_{j}(\bm{x}, \bm{p}) \leq 0
&& (j = 1, ...\, , n_{\text{constraints}}) \\
&&&\bm{x}^{-} \leq \bm{x} \leq \bm{x}^{+}
&&
\end{aligned}\right.
\end{align}
\end{fleqn}
\end{document}
(红线表示文本区域边框)