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Table 1 Comparison between our proposed methods and some general optimization algorithms

From: Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs

Optimization algorithms Pros Cons
Genetic algorithm Population-based, easily implemented, commonly used on various problems Do not use gradient information, do not scale well with complexity, unable to deal with constraints effectively
Simulated annealing Suitable for optimization problems where search space is discrete, unlikely stuck with local optimum Do not use gradient information, slow convergence, limited to system size up to thousand
Greedy Fast and easily implemented, results are usually satisfying It may fail to find global optimal on certain problems
GSO Taking advantage of gradient information using relaxation techniques, with automatic differentiation technique, it is much more faster than classic algorithms There is still room for improvement of the solution, because it may stuck with local optimum sometimes
EvoGSO Both gradient and population-based algorithm, with evolution strategy, it is able to overcome local optimum to some extent and obtain better solutions Usually more time consumption than original GSO in exchange for better results