Constrained optimization problems are almost everywhere in engineering research. A mathematical description of those problems with a single objective is to minimize or maximize an objective function over a set of decision variables under a set of constraints. There are different ways to format optimization problems; personally, I follow the format used in the book “Convex Optimization” by Stephen Boyd and Lieven Vandenberghe. For example, a general optimization problem has the form
This is generated by the following 
code:
\begin{equation*}
\begin{aligned}
& \underset{x}{\text{minimize}}
& & f_0(x) \\
& \text{subject to}
& & f_i(x) \leq b_i, \; i = 1, \ldots, m.
\end{aligned}
\end{equation*}
As seen in the code, the formatting is done by the aligned environment, which is defined in the amsmath package, so you need to include the following line in the preamble:
\usepackage{amsmath}
Unlike the tabular environment, in which you can specify the alignment of each column, in the aligned environment, each column (separated by &) has a default alignment, which alternates between right and left-aligned. Therefore, all the odd columns are right-aligned and all the even columns are left-aligned.
We conclude with a real example:
The above problem is formulated for completing low-rank positive semidefinite matrices. It is convex, or more precisely, it is a semidefinite program. The corresponding code is
\begin{equation*}
\begin{aligned}
& \underset{X}{\text{minimize}}
& & \mathrm{trace}(X) \\
& \text{subject to}
& & X_{ij} = M_{ij}, \; (i,j) \in \Omega, \\
&&& X \succeq 0.
\end{aligned}
\end{equation*}
