to sign ambiguity) to the original network in the best case.

$\mathbb{F} \gets [\ ]$ \[quadInitPF\] Locate a non-coplanar $K_4$ $\{i,j,k,l\} \subset V$ \[seednodes\] Assign positions to $i,j,k,l$ \[assignpositions\] $Q_{\textit{localized}} \gets [i,j,k,l]$ \[quadInitQ\] \[quadQnotEmpty\] $u \gets \textit{dequeue}(Q_{\textit{localized}})$\[quadDequeue\] add $(u, u.\textit{pos})$ into $\mathbb{F}$ \[quadAddIntoF\] \[quadIterateOnNeighbors\] \[quadCountge4\] \[canBeLocalized\] $\textit{enqueue}(v,Q_{\textit{localized}})$ \[quadAddIntoQ\] $\mathbb{F}$ \[quadReturnBest\]

Basically, the algorithm takes the network graph $G = (V, E)$ as the input, and assigns positions to each vertex in $v \in V$, based on the pairwise distances given by $E$. We begin by initializing the point formation in Line \[quadInitPF\]. In Line \[seednodes\], we find a non-coplanar $K_4$ in $G$ to begin the localization. The vertices of this complete subgraph are called *the seeds*. Since every pairwise distance is available, we can assign positions to these vertices in Line \[assignpositions\]. In Line \[quadInitQ\], we initialize a queue, called $Q_{\textit{localized}}$, that contains the localized vertices. Initially, it contains only the seeds. In Line \[quadQnotEmpty\], we start a loop, and continue until $Q_{\textit{localized}}$ is empty. In the loop, we first dequeue the first node in Line \[quadDequeue\], and then store its position into the point formation $\mathbb{F}$ in Line \[quadAddIntoF\]. Now that we have a localized vertex $u$, we iterate 