esperanza 0.0.1

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

Esperanza: Approximate Independent Set Solver

This work builds upon The Esperanza Algorithm.

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Maximum Independent Set (MIS) Problem: Overview

Definition

The Maximum Independent Set (MIS) problem is a fundamental NP-hard problem in graph theory. Given an undirected graph $G = (V, E)$, an independent set is a subset of vertices $S \subseteq V$ where no two vertices in $S$ are adjacent. The MIS problem seeks the largest such subset $S$.

Key Properties

• NP-Hardness: MIS is computationally intractable (no known polynomial-time solution unless $P = NP$).
• Equivalent Problems:
  • MIS is equivalent to finding the largest clique in the complement graph $\overline{G}$.
  • It is also related to the Minimum Vertex Cover problem: $S$ is an MIS iff $V \setminus S$ is a vertex cover.

Applications

1. Scheduling: Assigning non-conflicting tasks (e.g., scheduling exams with no shared students).
2. Network Design: Selecting non-adjacent nodes for efficient resource allocation.
3. Bioinformatics: Modeling protein-protein interaction networks.

Algorithms

Approach	Description	Complexity
Brute-Force	Checks all possible subsets of vertices.	$O(2^n)$
Greedy Heuristics	Selects vertices with minimal degree iteratively.	$O(n + m)$ (approx)
Dynamic Programming	Used for trees or graphs with bounded treewidth.	$O(3^{tw})$
Approximation	No PTAS exists; best-known approximation ratio is $O(n / (\log n)^2)$.	NP-Hard

Example

For a graph with vertices $\{A, B, C\}$ and edges $\{(A,B), (B,C)\}$:

• Independent Sets: $\{A, C\}$, $\{A\}$, $\{B\}$, $\{C\}$.
• MIS: $\{A, C\}$ (size 2).

Open Challenges

• Finding a constant-factor approximation for general graphs.
• Efficient quantum or parallel algorithms for large-scale graphs.

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Problem Statement

Input: A Boolean Adjacency Matrix $M$.

Answer: Find a Maximum Independent Set.

Example Instance: 5 x 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 graph is typically provided in DIMACS format. In this way, the previous adjacency matrix is represented in a text file using the following string representation:

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

This represents a 5x5 matrix in DIMACS format such that each edge $(v,w)$ appears exactly once in the input file and is not repeated as $(w,v)$. In this format, every edge appears in the form of

e W V

where the fields W and V specify the endpoints of the edge while the lower-case character e signifies that this is an edge descriptor line.

Example Solution:

Independent Set Found 4, 5: Nodes 4, and 5 constitute an optimal solution.

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Compile and Environment

Prerequisites

• Python ≥ 3.12

Installation

pip install esperanza

Execution

1. Clone the repository:

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

2. Run the script:

   hope -i ./benchmarks/testMatrix1
   

   utilizing the hope command provided by Esperanza's Library to execute the Boolean adjacency matrix esperanza\benchmarks\testMatrix1. The file testMatrix1 represents the example described herein. We also support .xz, .lzma, .bz2, and .bzip2 compressed text files.

   Example Output:

   testMatrix1: Independent Set Found 4, 5
   

   This indicates nodes 4, 5 form a Independent Set.

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Independent Set Size

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

hope -i ./benchmarks/testMatrix2 -c

Output:

testMatrix2: Independent Set Size 5

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Command Options

Display help and options:

hope -h

Output:

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

Compute the Approximate Independent 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 factor of at most 2
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Independent Set
  -v, --verbose         anable verbose output
  -l, --log             enable file logging
  --version             show program's version number and exit

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Batch Execution

Batch execution allows you to solve multiple graphs within a directory consecutively.

To view available command-line options for the batch_hope command, use the following in your terminal or command prompt:

batch_hope -h

This will display the following help information:

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

Compute the Approximate Independent 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 factor of at most 2
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Independent Set
  -v, --verbose         anable verbose output
  -l, --log             enable file logging
  --version             show program's version number and exit

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Testing Application

A command-line utility named test_hope is provided for evaluating the Algorithm using randomly generated, large sparse matrices. It supports the following options:

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

The Esperanza 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 factor of at most 2
  -b, --bruteForce      enable comparison with the exponential-time brute-force approach
  -c, --count           calculate the size of the Independent Set
  -w, --write           write the generated random matrix to a file in the current directory
  -v, --verbose         anable verbose output
  -l, --log             enable file logging
  --version             show program's version number and exit

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Code

• Python implementation by Frank Vega.

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Complexity

+ This algorithm finds near-optimal solutions for MIS in polynomial time, with an approximation ratio below 2. This breakthrough challenges the computational boundaries of P vs. NP providing strong evidence that P = NP.

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License

• MIT License.