Simulated Annealing Algorithm for Graph Coloring
This is an incremental application of existing methods to a classic combinatorial optimization problem, with no clear practical impact stated.
The paper tackled the graph coloring problem by implementing a simulated annealing algorithm within a Markov Chain Monte Carlo framework, and presented results by varying parameters such as the number of colors and average node degree, though no concrete numerical improvements or comparisons were provided.
The goal of this Random Walks project is to code and experiment the Markov Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this report, we present the plots of cost function \(\mathbf{H}\) by varying the parameters like \(\mathbf{q}\) (Number of colors that can be used in coloring) and \(\mathbf{c}\) (Average node degree). The results are obtained by using simulated annealing scheme, where the temperature (inverse of \(\mathbfβ\)) parameter in the MCMC is lowered progressively.