latticepts 0.1.4

A software package for enumerating lattice points in convex polyhedra

latticepts

Nate MacFadden, Liam McAllister Group, Cornell

Fast lattice point enumeration for convex polyhedra. A C/Cython implementation of Kannan's algorithm, significantly outperforming Normaliz and OR-Tools CP-SAT in speed for certain problems. As one performance example: latticepts generates ~107M lattice points in the strict interior of an example 7D cone ('Manwe') in ~23s. See the benchmarks for benchmarking plots.

Citation

If you use latticepts in your research, please cite it:

@software{latticepts,
  author  = {MacFadden, Nate},
  title   = {latticepts},
  doi     = {10.5281/zenodo.19406651},
  url     = {https://github.com/natemacfadden/latticepts},
  orcid   = {0000-0002-8481-3724},
}

Description

More explicitly, latticepts enumerates lattice points

$$ \{x\in\mathbb{Z}^{\text{dim}}: Hx\geq\text{rhs}\} $$

for $H\in\mathbb{Z}^{N_\text{hyps}\times\text{dim}}$ and $\text{rhs}\in\mathbb{Z}^{N_\text{hyps}}$. Here each row of $H$ is an inward-facing facet normal and the corresponding entry of $\text{rhs}$ is its offset. Cones correspond to $\text{rhs}=0$; 'stretched cones' (e.g., for finding strict interior points) correspond to $\text{rhs} > 0$; polyhedra to general $\text{rhs}$.

Limitations

• Maximum dimension: 256. For convex cones, latticepts excels at low-dimensions. It can become sluggish in comparison to alternatives at higher-dimensions (well before 256)
• Windows is not supported: the C kernel uses C99 variable-length arrays, which MSVC does not support

Installation

pip install -e .

Requires a C compiler and Cython. NumPy must be installed first.

Algorithm Notes

This repo contains a Cython wrapper of a C implementation of Kannan's algorithm. See this webpage for some other relevant work (not by me). The specific implementation in this repo is for lattice point enumeration in square boxes $|x_i|\leq B$ for $B\geq 1$. I.e.,

$$ \{x\in\mathbb{Z}^{\text{dim}}: Hx\geq\text{rhs} \text{ and } |x|_\infty \leq B\}. $$

It is a short algorithm, only $\leq 350$ lines - I encourage you to read it. If Python is easier to follow, reference/kannan_reference.py is a pedagogical numba.njit port of the same algorithm. The Python port is efficient but has less options than box_enum (scalar rhs only, so cones and stretched cones but not general polyhedra) and is not used at runtime.

A helper method to box_enum is provided in case the user wants $N$ points but doesn't care about box size. One such task here is for enumerating some lattice points in convex cones. In this case, boxes of increasing sizes $B$ are studied until $\geq N$ lattice points are found.

Benchmarks

Convex cones: runtime vs requested number of interior lattice points in a cone (i.e., not on the boundary). The cone studied is the 7D 'Manwe' from https://arxiv.org/abs/2406.13751:

Polytopes: runtime to enumerate all contained lattice points for various 4D reflexive polytopes. Size of the polytope is measured by h11 with one polytope per h11 value, h11 = 6..491:

More polytopes: runtime vs dimension of length-2 hypercubes $[0,2]^{dim}$ for dim = 2..14:

Usage

There are two primary interfaces. For unbounded polyhedra (e.g., cones), the focus is on efficiently generating a finite collection of lattice points. This can be done via enum_lattice_points which enumerates all lattice points with components bounded by $|x_i|\leq B$ in the polyhedron. The algorithm increases the size of $B$ until a user-requested number of points is found. See the following example of how to use this to get lattice points in the strict (since $rhs=1$) interior of a convex cone:

import numpy as np
from latticepts import enum_lattice_points

# Find at least 1000 lattice points in {x : H @ x >= rhs}
H   = np.array([[1, 2], [3, -1]], dtype=np.int32)
rhs = 1
pts = enum_lattice_points(H=H, rhs=rhs, min_N_pts=1000)

# Optionally restrict to primitive vectors (GCD = 1)
pts = enum_lattice_points(H=H, rhs=rhs, min_N_pts=1000, primitive=True)

box_enum allows direct control over the box size $B$ instead of the number of lattice points. I.e., to enumerate all lattice points in $\{x: Hx \geq \text{rhs},\ |x|_\infty \leq B\}$:

from latticepts import box_enum

pts, status, N_nodes = box_enum(B=5, H=H, rhs=rhs, max_N_out=10_000)
# status: 0 = success, -1 = dim>256, -2 = hit max_N_out, -3 = hit max_N_nodes
# (statuses are also explained in the docstring)

box_enum is well suited to lattice point enumeration in polytopes (assuming an H-representation is known). For example, if one knows a bounding box of the polytope (if you have a V-representation, this is trivial: $B = \max|v_i|$ over all vertices), then the lattice point enumeration can be done as follows. Here's an example of the $h^{1,1}=491$ 4D reflexive polytope:

import numpy as np
from latticepts.box_enum import box_enum

H   = np.array([[ 1,   0,   0,   0],
                [-15,  8,   6,   1],
                [-15,  8,   6,  -1],
                [ -1,  1,  -1,   0],
                [  0, -1,   0,   0]], dtype=np.int32)
rhs = np.array([-1, -1, -1, -1, -1], dtype=np.int32)
# has bounding box B = max(|vertices|) = 42 (basis-dependent)
B   = 42

# one can then get the lattice points via:
pts, status, N_nodes = box_enum(B=B, H=H, rhs=rhs, max_N_out=10_000)

Organization

latticepts/
├── latticepts/
│   ├── box_enum.h                       # STB-style library for the Kannan enumeration
|   ├── box_enum.pyx                     # Cython wrapper
|   └── latticepts.py                    # the enum_lattice_points wrapper for box_enum
├── tests/
│   ├── conftest.py                      # shared test helpers (pytest)
│   ├── test_box_enum.py                 # tests of box_enum
│   ├── test_manwe.py                    # tests relating to 'Manwe' (arXiv:2406.13751)
│   ├── test_enum_lattice_points.py      # tests of enum_lattice_points
|   └── c/                               # simple C-kernel tests (no Python interface)
├── benchmarks/                          # perf benchmarks; double as usage examples + make the README figures
│   ├── benchmark_box_enum.py            # runtime vs B for the Manwe geometry (h11=491, 7D)
│   ├── benchmark_cytools.py             # runtime vs CYTools' point enumeration
│   ├── benchmark_enum_lattice_points.py # runtime vs requested N for the Manwe cone (h11=491, 7D)
│   ├── benchmark_polytopes.py           # runtime vs h11 for 4D reflexive polytopes; runtime vs dimension for hypercubes
│   └── benchmark_narrowness.py          # runtime vs narrowness for a 4D convex cone
├── reference/
│   └── kannan_reference.py              # readable pure-Python (numba.njit) port of the algorithm; not used at runtime
├── docs/                                # README figures (benchmark_*.png)
├── pyproject.toml
└── setup.py