Optimal quantization in the unit square
Also known asoptimal quantizationquantizer problemcentroidal Voronoi tessellation
Place n points in the unit square. Each location in the square is assigned to its nearest point; minimize the average squared distance over the square from a location to its assigned point.
Formal definition
- ContainerThe unit square [0, 1]², with lower-left corner at (0, 0) and upper-right corner at (1, 1)
- SubmissionExactly n distinct points, each coordinate written as a decimal string with at most nine decimal places
- AssignmentEvery location belongs to its nearest submitted point (the Voronoi partition); equidistant boundaries have measure zero and do not affect the integral
- ScoreE(P) is the integral over the unit square of the squared distance to the nearest submitted point (equal to the mean since the square has area 1); the verifier computes the exact rational integral and rounds upward at 10⁻¹⁸
- ObjectiveMinimize E(P) over all valid point sets P
Getting a feel for it
An analogy: respawn points
Think of the square as a map and the n points as respawn points. A player appears uniformly at random on the map and is sent to the nearest respawn point; the score is the mean squared length of that trip.
Why Lloyd's algorithm alone is not enough
Repeatedly moving each point to the centroid of its Voronoi cell is Lloyd's algorithm. The iteration converges to a centroidal Voronoi tessellation, but the quantization energy has many local minima with different cell topologies, so finding the global optimum requires searching across distinct basins.
Known results
Exact optima are proven for n ≤ 2, while published results for n = 3, 4, and 5 rely on an unproven symmetry assumption (Roychowdhury, arXiv:1608.03815), and all cases with n ≥ 6 remain open. Fejes Tóth's moment theorem provides the universal strict lower bound 5/(18√3·n), corresponding to regular hexagonal cells that cannot tile a square.
Current best solutions by n
Each parameter n is an independently scored sub-problem. Select any card to inspect the current record construction or submit a stronger solution.
Discussion (0) ↓Data and citation
All sub-problems in this family — authoritative scores, proof status, coordinates, and sources — are published at the stable URL below under CC BY 4.0. Because scores update as records fall, please cite the file's generatedAt timestamp.
GET https://minmaxarena.com/data/optimal-quantization.json
Cite the frozen 2026-10 edition: live records change, whereas a frozen edition stays fixed and reproducible.
GET https://minmaxarena.com/data/editions/2026-10/optimal-quantization.json
BibTeX (click to copy)
@misc{minmaxarena-optimal-quantization-2026-10,
title = {{Optimal quantization in the unit square} (P55)},
author = {{MinMax Arena}},
year = {2026},
note = {Machine-verified records, 2026-10 edition},
url = {https://minmaxarena.com/data/editions/2026-10/optimal-quantization.json},
license = {CC BY 4.0}
}Discussion
Discuss strategies, share construction methods, or ask questions. Any signed-in user can post under their public byline and unique #UID. New posts appear after moderation.
No threads yet. Start the first discussion.
Grouped by player and sub-problem, showing the latest note first. Earlier versions and replies are preserved; click any note to read or reply. Notes can be published alongside record-breaking submissions.
No solver notes yet.