faer calculates the eigenvalues of a nontrivial adjacency matrix as `0` and `+/- inf`

[ariasanovsky]

Describe the bug
The Vec<c64> output of eigenvalues() for the adjacency matrix of the following $20$-vertex tree are incorrectly reported as all zeroes.

Note: the path graph on $3$ vertices produces the wrong eigenvalues. Here's a failing unit test.

To Reproduce

/* use the smaller unit test linked above
#[test]
fn this_other_tree_has_correct_maximum_eigenvalue() {
    let edges = [(3, 2), (6, 1), (7, 4), (7, 6), (8, 5), (9, 4), (11, 2), (12, 2), (13, 2), (15, 6), (16, 2), (16, 4), (17, 8), (18, 0), (18, 8), (18, 2), (19, 6), (19, 10), (19, 14)];
    let mut a = faer::Mat::zeros(20, 20);
    for (v, u) in edges.iter() {
        a[(*v, *u)] = 1.0;
        a[(*u, *v)] = 1.0;
    }
    let eigs_complex: Vec<faer::complex_native::c64> = a.eigenvalues();
    println!("{eigs_complex:?}");
    let eigs_real = eigs_complex
        .iter()
        .map(|e| e.re)
        .collect::<Vec<_>>();
    println!("{eigs_real:?}");
    let lambda_1 = *eigs_real
        .iter()
        .max_by(|a, b| a.partial_cmp(&b).unwrap())
        .unwrap();
    let correct_lamba_1 = 2.6148611139728866;
    assert!(
        (lambda_1 - correct_lamba_1).abs() < 1e-10,
        "lambda_1 = {lambda_1}, correct_lamba_1 = {correct_lamba_1}",
    );
}
*/

Expected behavior

name: graphenv
# channels:
#   - defaults
# dependencies:
#   - python
#   - networkx
#   - matplotlib
#   - imageio
#   - msgpack-python
#   - scipy

import networkx as nx
import numpy as np
import matplotlib.pyplot as plt

# Create the graph
edges = [[3, 2], [6, 1], [7, 4], [7, 6], [8, 5], [9, 4], [11, 2], [12, 2], [13, 2], [15, 6], [16, 2], [16, 4], [17, 8], [18, 0], [18, 8], [18, 2], [19, 6], [19, 10], [19, 14]]
graph = nx.Graph()
graph.add_edges_from(edges)

# Get the adjacency matrix eigenvalues
adjacency_matrix = nx.adjacency_matrix(graph).todense()
eigenvalues = np.linalg.eigvals(adjacency_matrix)

# Print the eigenvalues in decreasing order
print("Eigenvalues in decreasing order:")
for eigenvalue in sorted(eigenvalues, reverse=True):
    print(eigenvalue)

# Plot the graph
nx.draw(graph, with_labels=True)
plt.show()

[ariasanovsky]

Smaller bug witness ($N = 15$)

let edges = [[2, 0], [6, 4], [7, 0], [8, 4], [9, 8], [10, 0], [11, 0], [12, 4], [13, 5], [13, 11], [13, 3], [14, 1], [14, 3], [14, 4]]
// ...
let correct_lambda_1 = 2.2950716903444595;

[ariasanovsky]

Smaller bug witness ($N = 8$)

let edges = [[1, 0], [3, 0], [5, 2], [5, 0], [6, 0], [6, 4], [7, 6]]
// ...
let correct_lambda_1 = 2.2215832939527633;

[ariasanovsky]

Smaller bug witness ($N = 4$)

let edges = [[1, 0], [2, 0], [3, 0]];
// [0.0 + 0.0 * I, -inf + 0.0 * I, 0.0 + 0.0 * I, 0.0 + 0.0 * I]
// [0.0, -inf, 0.0, 0.0]

[ariasanovsky]

Smaller bug witness ($N = 3$)

let edges = [[1, 0], [0, 2]];
/*
[
[0.0, 1.0, 1.0],
[1.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
]
[0.0 + 0.0 * I, -inf + 0.0 * I, 0.0 + 0.0 * I]
[0.0, -inf, 0.0]
*/

However, after permutation, the values are correct

let edges = [[1, 0], [2, 1]];
/*
[
[0.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
]
[-1.414213562373094 + 0.0 * I, 5.160802341031001e-17 + 0.0 * I, 1.414213562373095 + 0.0 * I]
[-1.414213562373094, 5.160802341031001e-17, 1.414213562373095]
*/

[ariasanovsky]

Unit test

#[test]
fn path_on_three_vertices_has_correct_eigenvalues_regardless_of_permutation() {
    let all_edges = [
        [(1, 0), (2, 0)],
        [(2, 1), (1, 0)],
        [(2, 1), (2, 0)],
    ];
    let correct_eigenvalues = [2.0f64.sqrt(), 0., -2.0f64.sqrt()];
    for edges in all_edges {
        let mut a = faer::Mat::zeros(3, 3);
        for (v, u) in edges.iter() {
            a[(*v, *u)] = 1.0;
            a[(*u, *v)] = 1.0;
        }
        let eigs_complex: Vec<faer::complex_native::c64> = a.eigenvalues();
        let mut eigs_real = eigs_complex
            .iter()
            .map(|e| e.re)
            .collect::<Vec<_>>();
        eigs_real.sort_by(|a, b| b.partial_cmp(a).unwrap());
        let error = correct_eigenvalues.iter().zip(eigs_real.iter()).map(|(a, b)| (a - b).powi(2)).sum::<f64>();
        assert!(
            error < 1e-10,
            "error = {error}\nedges = {edges:?}\na = {a:?}\neigs = {eigs_complex:?}\neigs = {eigs_real:?}",
        );
    }
}

[ariasanovsky]

Patching with

const ZERO: f64 = 0.0000001;
let mut a = Mat::from_fn(N, N, |_, _| ZERO);

solve my problem.

If this is user error, feel free to close.

[sarah-quinones]

it's not a user error. i forgot i should be detecting zeros at the head/tail of the matrix during the eigenvalue computation xP

[sarah-quinones]

fixed in 0.15.0
thanks a bunch!