furones 0.3.7

Compute the Approximate Minimum Dominating Set for undirected graph encoded in DIMACS format.

Furones: Approximate Dominating Set Solver

This work builds upon The Furones Algorithm.

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Overview of the Minimum Dominating Set (MDS)

Definition

A dominating set in a graph $G = (V, E)$ is a subset $D \subseteq V$ such that every vertex not in $D$ is adjacent to at least one vertex in $D$. The minimum dominating set (MDS) is the smallest possible dominating set in terms of the number of vertices.

Computational Complexity

• NP-Hard: Finding the minimum dominating set is NP-hard; no polynomial-time exact algorithm is known for general graphs.
• Approximation: The greedy Set Cover heuristic gives a ratio $H(\Delta+1) \le 1 + \ln(\Delta+1) = \mathcal{O}(\log \Delta)$, where $\Delta$ is the maximum degree. This is tight and matches the $o(\ln n)$-in-$n$ inapproximability threshold (Dinur–Steurer).

Applications

• Network monitoring and wireless sensor coverage
• Influence maximisation in social networks
• Facility placement and logistics
• Protein–protein interaction modelling

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The Furones Algorithm

Furones v0.3.7 is a linear-time candidate-comparison solver for MDS. It builds a portfolio of dominating-set candidates on the input graph and returns the smallest validated one. The pipeline consists of:

1. Preprocessing: self-loop removal, isolated-vertex separation.
2. TSCC-style pendant cascade (ReduceToTSCCForDS): iteratively commits pendant supports and isolated vertices to a forced set $F$, producing a reduced residual.
3. Forest projection (if the residual is non-planar): projects onto a spanning forest before solving.
4. Baker-style planar PTAS on the residual: solves MDS on $k$-outerplanar components by tree-decomposition DP.
5. Lifted candidate $C_L = F \cup \ell(D_R)$: lifts the reduced solution back to original labels.
6. Original-graph linear candidates (all computed in $\mathcal{O}(n+m)$):
   • $C_G$ — greedy maximum-coverage (certifies the $H(\Delta+1)$ ratio)
   • $C_D$ — closed-degree coverage sweep
   • $C_W$, $C_M$ — low- and medium-degree witness sweeps
   • $C_O$ — order-ownership candidates (early and late)
   • $C_S$ — seed-and-complete
   • $C_B$ — Salvador-style bipartite auxiliary
   • $C_R$ — reverse-delete (multiple deterministic orders)
7. Pruning (PruneRedundantDominating): each candidate is made inclusion-minimal.
8. Portfolio minimum: the smallest valid (dominates the original graph) pruned candidate is returned.

Unconditional guarantees

• Feasibility (Theorem): every normal return is a dominating set of the input graph.
• Approximation ratio (Theorem): because the portfolio contains $C_G$, every returned set $D$ satisfies $|D| \le H(\Delta+1),\gamma(G) \le (1+\ln(\Delta+1)),\gamma(G) = \mathcal{O}(\log \Delta)$.
• Constant factor on bounded-degree graphs (Corollary): for any fixed $\Delta$, the ratio is at most $1 + \ln(\Delta+1)$.

Near-threshold ratio hypothesis

Furones is conjectured to satisfy $|D| \le \max\{4,,\frac{1}{2}\ln n\}\cdot\gamma(G)$ on every graph. Proving this for all graphs would imply P = NP (via Dinur–Steurer). The hypothesis is already proved unconditionally for all graphs of maximum degree $\Delta \le \sqrt{n}/e$.

Runtime

$\mathcal{O}(n+m)$ for fixed parameters (large hidden constant; grows as $2^{\mathcal{O}(1/\varepsilon)}$ on the Baker branch).

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CAR Benchmarks

The car/ folder contains three reproducible exact-optimum studies. Every study imports the installed Furones package and compares it against the exact domination number $\gamma(G)$ computed by exhaustive search.

CAR-001: Core Exact Benchmark (run_integrity_measurements.py)

• 1 000 instances, at most 14 vertices, seven graph families (Erdős–Rényi sparse/medium/dense, random bipartite, random trees, perturbed paths/cycles, small structured).
• Exact $\gamma(G)$ by exhaustive search for every instance.
• Result: Furones is optimal on all 1 000 instances (100.0 %); observed ratio $\hat{\rho} = 1.000$.

Family	Instances	$n$	$\gamma$	Optimal	Mean ratio	Max ratio
Erdős–Rényi sparse	200	6–14	2–10	100.0 %	1.000	1.000
Erdős–Rényi medium	200	6–14	2–5	100.0 %	1.000	1.000
Erdős–Rényi dense	150	6–14	1–4	100.0 %	1.000	1.000
Random bipartite	150	6–14	2–9	100.0 %	1.000	1.000
Random trees	100	6–14	2–6	100.0 %	1.000	1.000
Perturbed paths/cycles	100	6–14	2–5	100.0 %	1.000	1.000
Small structured	100	6–14	1–4	100.0 %	1.000	1.000
Overall	1000	6–14	1–10	100.0 %	1.000	1.000

CAR-002: Per-Strategy Ablation (run_integrity_measurements.py)

Each exposed strategy is measured independently on the same 1 000-instance benchmark.

Strategy	Valid	Optimal	Mean ratio	Max ratio
Seed-and-complete	100.0 %	99.8 %	1.000	1.250
Greedy max-coverage ($C_G$)	100.0 %	92.7 %	1.023	1.500
Closed-degree coverage	100.0 %	89.0 %	1.048	2.500
Reverse delete, low-degree	100.0 %	88.7 %	1.049	2.500
Medium-degree witnesses	100.0 %	88.3 %	1.054	2.500
TSCC/Baker/lift	100.0 %	87.8 %	1.052	2.000
Salvador auxiliary	100.0 %	85.4 %	1.107	5.000
Low-degree witnesses	100.0 %	84.8 %	1.080	3.000
Order ownership, late	79.2 %	65.8 %	1.060	2.000
Order ownership, early	79.2 %	64.5 %	1.065	2.000
Reverse delete, reverse	100.0 %	61.2 %	1.176	3.500
Reverse delete, input order	100.0 %	57.9 %	1.241	5.000
Reverse delete, high-degree	100.0 %	27.9 %	1.428	5.000

No single strategy is universally optimal; the portfolio minimum combines them to achieve 100 % optimal on every instance.

CAR-003: High-Degree / Dense Regime (run_high_degree_experiment.py)

Probes the regime $\Delta > \sqrt{n}/e$ left open by the low-degree proposition. Base seed 12 345, 1 000 instances from seven dense families (complete, cocktail-party, dense circulant, random regular, dense Erdős–Rényi, perturbed hypercube, perturbed Paley), including perfect-code stress cases ($\tau(G) = 1$).

Family	Inst.	$n$	$\Delta$	$\tau$	Mean ratio	Max ratio	≤ ρ(n)
Complete	143	6–16	5–15	1.00	1.000	1.000	100 %
Cocktail party	143	6–16	4–14	1.67–1.88	1.000	1.000	100 %
Dense circulant	143	8–16	4–15	1.00–1.88	1.000	1.000	100 %
Random regular	143	8–16	2–8	1.00–1.69	1.000	1.000	100 %
Dense Erdős–Rényi	143	8–16	4–15	1.00–2.44	1.000	1.000	100 %
Hypercube (perturbed)	143	8–16	3–6	1.00–1.75	1.000	1.000	100 %
Paley (perturbed)	142	5–13	2–7	1.00–1.85	1.000	1.000	100 %
Overall	1000	5–16	2–15	1.00–2.44	1.000	1.000	100 %

Furones is optimal on all 1 000 high-degree instances, including every $\tau(G) = 1$ perfect-code case. Zero violations of $\rho(n) = \max\{4, \frac{1}{2}\ln n\}$.

CAR-004: Adversarial Stress Test (run_adversarial_experiment.py)

Actively searches for worst-case instances. Base seed 1 000, 10 000 instances from seven adversarial families: greedy traps, geometric set-cover gadgets, perturbed perfect-code graphs, near-efficient circulants, cocktail-party/multipartite graphs, dense random-regular, and dense Erdős–Rényi. Exact $\gamma(G)$ by exhaustive search on every instance.

Adversarial family	Inst.	$n$	$\gamma$	Mean ratio	Max ratio	Optimal	≤ ρ(n)
Greedy trap	1 429	6–16	2–4	1.000	1.000	100.0 %	100 %
Geometric set-cover	1 429	10–16	3–5	1.000	1.000	100.0 %	100 %
Perfect-code perturbed	1 429	7–16	1–4	1.000	1.000	100.0 %	100 %
Efficient circulant	1 429	10–16	2–4	1.000	1.000	100.0 %	100 %
Cocktail / multipartite	1 428	4–16	2	1.000	1.000	100.0 %	100 %
Random dense	1 428	8–16	1–5	1.000	1.000	100.0 %	100 %
Random regular	1 428	8–16	2–5	1.001	1.333	99.7 %	100 %
Overall	10 000	4–16	1–5	1.000	1.333	99.95 %	100 %

Zero violations of $\rho(n)$ across all 10 000 adversarial instances. The worst observed ratio was 1.333 (a dense random-regular graph, $n = 16$, $\gamma = 3$, returned size 4), well below $\rho = 4$.

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Problem Statement

Input: A Boolean Adjacency Matrix $M$.

Answer: Find a Minimum Dominating Set.

Example Instance: 5 × 5 matrix

	c1	c2	c3	c4	c5
r1	0	0	1	0	1
r2	0	0	0	1	0
r3	1	0	0	0	1
r4	0	1	0	0	0
r5	1	0	1	0	0

The input for undirected graphs is provided in DIMACS format:

p edge 5 4
e 1 3
e 1 5
e 2 4
e 3 5

Example Solution:

Dominating Set Found 1, 2: nodes 1 and 2 constitute an optimal solution.

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Compile and Environment

Prerequisites

• Python ≥ 3.12

Installation

pip install furones

Execution

1. Clone the repository:

   git clone https://github.com/frankvegadelgado/furones.git
   cd furones
   

2. Run the script:

   asia -i ./benchmarks/testMatrix1
   

   Example Output:

   testMatrix1: Dominating Set Found 1, 2
   

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Dominating Set Size

Use the -c flag to count the nodes in the Dominating Set:

asia -i ./benchmarks/testMatrix2 -c

Output:

testMatrix2: Dominating Set Size 4

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Command Options

asia -h

Output:

usage: asia [-h] -i INPUTFILE [-a] [-b] [-c] [-v] [-l] [--consistency] [--version]

Solve the Approximate Minimum Dominating Set for undirected graph encoded in DIMACS format.

options:
  -h, --help            show this help message and exit
  -i INPUTFILE, --inputFile INPUTFILE
                        input file path
  -a, --approximation   enable comparison with a polynomial-time approximation approach within a logarithmic factor
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Dominating Set
  -v, --verbose         enable verbose output
  -l, --log             enable file logging
  --consistency         require a linear-time certificate for the Furones approximation bound
  --version             show program's version number and exit

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Batch Execution

batch_asia -h

Output:

usage: batch_asia [-h] -i INPUTDIRECTORY [-a] [-b] [-c] [-v] [-l] [--consistency] [--version]

Solve the Approximate Minimum Dominating Set for all undirected graphs encoded in DIMACS format and stored in a directory.

options:
  -h, --help            show this help message and exit
  -i INPUTDIRECTORY, --inputDirectory INPUTDIRECTORY
                        Input directory path
  -a, --approximation   enable comparison with a polynomial-time approximation approach within a logarithmic factor
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Dominating Set
  -v, --verbose         enable verbose output
  -l, --log             enable file logging
  --consistency         require a linear-time certificate for the Furones approximation bound
  --version             show program's version number and exit

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Testing Application

usage: test_asia [-h] -d DIMENSION [-n NUM_TESTS] [-s SPARSITY] [-a] [-b] [-c] [-w] [-v] [-l] [--consistency] [--version]

The Furones Testing Application using randomly generated, large sparse matrices.

options:
  -h, --help            show this help message and exit
  -d DIMENSION, --dimension DIMENSION
                        an integer specifying the dimensions of the square matrices
  -n NUM_TESTS, --num_tests NUM_TESTS
                        an integer specifying the number of tests to run
  -s SPARSITY, --sparsity SPARSITY
                        sparsity of the matrices (0.0 for dense, close to 1.0 for very sparse)
  -a, --approximation   enable comparison with a polynomial-time approximation approach within a logarithmic factor
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Dominating Set
  -w, --write           write the generated random matrix to a file in the current directory
  -v, --verbose         enable verbose output
  -l, --log             enable file logging
  --consistency         require a linear-time certificate for the Furones approximation bound
  --version             show program's version number and exit

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Code

• Python implementation by Frank Vega.

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Complexity

+ We present a linear-time portfolio algorithm for MDS with an unconditional H(Δ+1) approximation guarantee and exact performance on all 11 000 CAR benchmark instances.

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License

• MIT License.