Changes in version 1.0.0.9004                      

  - Improve ExactMaxMin() performance and support >46k points.

  - ExactMaxMin() reports an upper bound on the optimum, and supports
    continuation from a timed-out state.

  - PickPoint() supports the "peripheral" and "random_furthest"
    strategies, including where d is a function, returning nSeeds seeds.

  - FarFirst() supports strategy = "random_furthest" when d is a
    function.

  - MaxEntropy() is roughly 3-12x faster at large n.

                 Changes in version 1.0.0 (2026-09-17)                  

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.

Max-Min diversity (MMDP / discrete p-dispersion)

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.

Max-Mean dispersion

  - MaxMean(): reinforcement-learning-guided tabu search (Nijimbere et
    al. 2020), selecting a subset of unrestricted size that maximises
    the mean pairwise distance.

Discrete k-centre

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.

Max-Sum diversity and maximum entropy

  - 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.

Scoring and utilities

  - 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.
  - Each solver returns a classed object with print(), format() and
    (where informative) summary() methods giving a terse or detailed
    report of the selection, the achieved objective, and the search
    effort.
  - Solver behaviour is tunable through
    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 = ).