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Coreset selects a representative subset of a fixed
candidate set under an arbitrary distance, solving four discrete
location objectives on a distance matrix, a coordinate matrix, or an
on-demand distance-column oracle.
Maximises the minimum pairwise distance within a subset of size
k.
FarFirst(): greedy farthest-first selection (Gonzalez
1985), with a choice of peripheral seeding strategies, distinct-seed
random restarts (nSeeds), and a robust ensemble
default.DropAdd(): DropAdd tabu search (Porumbel et al. 2011),
which can compute distances between pairs on the fly rather than needing
a complete matrix a priori.Grasp(): GRASP with path relinking (Resende et
al. 2010), attaining the highest T_k of the package’s
heuristics on small to medium instances.ExactMaxMin(): exact node-packing optimum (Sayyady
& Fathi 2016), decided by clique search.MaxMean(): reinforcement-learning-guided tabu search
(Nijimbere et al. 2020), selecting a subset of unrestricted size that
maximises the mean pairwise distance.Minimises the largest distance from any element to its nearest selected centre.
KCentre(): the CDSh covering heuristic (Garcia-Diaz et
al. 2017, 2019).ExactKCentre(): exact minimum-cover optimum.ExactMaxSum(): exact solver for the Max-Sum Diversity
Problem (requires ‘highs’).MaxEntropy(): maximum-entropy (maxdet) selection — the
mode of a determinantal point process — by greedy pivoted-Cholesky
selection, and by exact enumeration for small instances.MinDist(), MeanDist() and
KCentreRadius() score an arbitrary selection under the
max-min, max-mean and k-centre objectives respectively.PickPoint() exposes the peripheral seed indices
directly.DropAdd() and Grasp() accept a
maxCandidates composable-coreset cap, thinning the
candidate set with FarFirst() before the expensive search
and mapping the chosen indices back to the original numbering.print(),
format() and (where informative) summary()
methods giving a terse or detailed report of the selection, the achieved
objective, and the search effort.options(Coreset.symmetryTolerance = ), which sets how large
a rounding discrepancy between d[i, j] and
d[j, i] is repaired rather than refused, and
options(Coreset.progress = ).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.