of a new iteration; thus, we use a function that causes the algorithm to recompute the entropy more often at lower iteration numbers. Lipp [*et al.*]{} [@lipp2015] use the same function for reducing the running time of their WSPD algorithm. This “lazy” computation of the entropy term significantly reduces the running time while the quality does not deteriorate much.

##### Solving the Laplacian linear systems. {#solving-the-laplacian-linear-systems. .unnumbered}

Recall that Gansner [*et al.*]{}derived an iteration of subsequent Laplacian linear systems for optimizing maxent-stress. They use the conjugate gradient method (CG) as a Laplacian solver in their implementation. The conjugate gradient method has superlinear time complexity. That is why we use lean algebraic multigrid (LAMG) instead, a fast solver proposed by Livne and Brandt [@livne2012] with linear empirical running time. We use our C++ implementation of LAMG; it is available in NetworKit and has been used for other Laplacian graph problems before [@DBLP:conf/siamcsc/BergaminiWLM16]. An alternative multilevel approach for solving the linear systems exists [@meyerhenke2015], but is harder to adapt to the present scenario.

##### Optimization Workflow. {#optimization-workflow. .unnumbered}

We combine our two optimization algorithms into one workflow: the solution found by the SA algorithm can often be further improved by a subsequent 