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The most recent version of metadata is available in the original repository.\r\n\r\n\r\n• The following gradient-based optimization algorithms are implemented (see Sections\r\n3.2.1 and 3.2.2, and Tables 3.2 and 3.3 in [1])\r\n\r\n– SD/SA: Steepest Descent/Ascent ,\r\n– CG-PR: Conjugate Gradient (Polak-Ribière formula)\r\n– CG-FR: Conjugate Gradient (Fletcher-Reeves formula)\r\n\r\n• The following line search methods are implemented and used together with the\r\ngradient algorithms (see Section 3.2.3, and Table 3.4 in [1]):\r\n– Armijo step method,\r\n– polynomial approximation-based method\r\n– DFT approximation-based method\r\n\r\nAll combinations of Riemannian gradient methods and line search methods proposed\r\nin [1] are tested. In practical applications, just one of these algorithms (SD/SA, CG-\r\nPR/CG-FR) together with one of the line search methods (Armijo, polynomial of DFT-\r\nbased) is sufficient to solve the problem at hand. The algorithm/method to be used may\r\nbe chosen based on experimental testing. The difference in performance may depend on\r\nthe cost function. In general, based on our experience, first we recommend CG-PR with\r\nthe polynomial-based line search method, and second, SD/SA with the DFT-based line\r\nsearch method.\r\n\r\nAs a test example, the Brockett cost function (see [1] for details).\r\n\r\nNOTE: This set of codes was designed such that the Riemannian optimization scripts\r\nare separated from the cost function specific scripts. Therefore, these codes can also be\r\neasily used to optimize other smooth cost functions, simply by changing the cost function-\r\nspecific parameters/scripts. 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