max-div
A versatile, high-performance solver for Maximum Diversity Problems — select the k most
diverse of n items, under optional fairness constraints.
Highlights
-
⚡ obtains near-optimal results within seconds-to-one-minute for problems up to
n=200k -
⏱️ runs within an arbitrary solve budget — wall-clock time or iteration count
-
🚀 leverages numba JIT-compilation for maximum speed without relying on pre-compiled binaries
-
⚖️ natively supports flexible fairness constraints
-
uniquely supports constraints with overlapping groups & ranged counts
-
returns the least infeasible solution (with configurable weighted linear or quadratic penalties) when constraints conflict
-
provides proofs of (in)feasibility
-
-
📐 uniquely supports 5+ distance metrics (L1, L2, L∞, Minkowski, cosine — or precomputed distances) and 4 diversity metrics (minimum, mean & geomean separation + mean pairwise distance) in any combination
-
💾 computes item distances eagerly when memory allows (maximum speed), lazily when problem size requires (minimal memory usage)
-
🤝 leverages multi-core CPUs with parallel workers in independent, cooperative or dynamically grouped configurations, without duplicating core problem data
The benchmarks compare max-div in depth with 10 other freely available solvers.
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:
- Getting started — installation, distance and diversity metrics, solver presets
- Comparison with other tools — how max-div relates to exact solvers, greedy pickers, and samplers
- Benchmarks — the measured comparison against third-party tools
License
Licensed under the Apache License 2.0.
1 maintainer
Release files for max-div 0.17.0
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