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max-div

Configurable solver for Maximum Diversity Problems with optional fairness constraints: given n items — vectors or precomputed distances — select the k most diverse, optionally subject to per-group minimum/maximum quotas.

max-div is an anytime heuristic: it returns a good selection quickly and keeps improving it for as long as you allow (wall-clock time or iteration count). Two things set it apart among freely available tools: it is the only dedicated diversity solver with native support for overlapping fairness constraints, and the only one offering a geometric-mean separation objective — one of four diversity metrics it provides (minimum, mean, and geometric-mean separation, plus mean pairwise distance).

Feature comparison of max-div against exact solvers and one-shot pickers: distance metrics, diversity objectives, constraint handling, time budgets and practical scale

It fills the gap between exact MIP/CP solvers, which prove optimal answers but do not scale, and single-shot pickers, which are fast but at best have very limited support for constraints. The benchmarks show where it leads and where it does not.

Installation

pip install max-div

Python 3.11+; free-threaded builds (3.14t) are supported and CI-tested (see the installation notes for the numba version they require).

Quick start

import numpy as np
from max_div import MaxDivProblem, MaxDivSolverBuilder, seconds

rng = np.random.default_rng(42)
vectors = rng.random((200, 5))               # 200 points in 5 dimensions

# select the 20 most diverse, improving for up to 5 seconds
problem = MaxDivProblem.new(vectors, k=20)
solution = MaxDivSolverBuilder(problem).with_preset(seconds(5)).build().solve()

print(solution.i_selected)                   # indices of the selected items

With fairness constraints

Require a minimum and/or maximum number of selected items from given subsets — useful for fair representation across groups. Groups may overlap, and infeasible constraints degrade gracefully to the least-infeasible selection rather than failing.

from max_div import Constraint

# require between 8 and 12 of the selected items from each half of the data
constraints = [
    Constraint(int_set=set(range(0, 100)),   min_count=8, max_count=12),
    Constraint(int_set=set(range(100, 200)), min_count=8, max_count=12),
]
problem = MaxDivProblem.new(vectors, k=20, constraints=constraints)

Documentation

Full documentation lives at max-div.readthedocs.io, including:

License

Licensed under the Apache License 2.0.

Project links

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  • Released:
Newer release available (0.15.0)

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Credits

Author: Bert Pluymers

GitHub Statistics

Data verified by PyPI on Jul 27, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
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Requires

Python >=3.11

Provides Extra

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