Greetings, I'm following a potential idea for GSoC 2018 titled "Particle swarm optimization". I have read a few documents and familiarized myself with the algorithm. It's listed in the idea description: "So this project is divided into two parts: First implement one or two unconstrained methods and afterwards takes a look at one --contained-- (constrained [?]) method". I apologize if I misunderstood what constrained problems are, but can't we apply constraints to the methods already present in "src/mlpack/methods/*" directory? Or, are these unrelated? In the latter case, are there some specialized methods for constrained problems that need to be implemented for this project?
Regarding the test cases structuring, I've found that in some cases a test_function.cpp or <method_name>_test_function.cpp file is present in the main method directory, such as here (<https://github.com/mlpack/mlpack/blob/3d3d733ba3c41c4f51764f44185767384ab6d9c7/src/mlpack/core/optimizers/gradient_descent/test_function.cpp>https://github.com/mlpack/mlpack/blob/master/src/mlpack/core/optimizers/gradient_descent/test_function.cpp). Later, an object of this class is created in the main tests directory ("src/mlpack/tests/*"), in this case, here (<https://github.com/mlpack/mlpack/blob/97fd47de5e0a51adf3c01957f6646eb5cc3651d5/src/mlpack/tests/gradient_descent_test.cpp>https://github.com/mlpack/mlpack/blob/master/src/mlpack/tests/gradient_descent_test.cpp). So, my question is this, what is the preferred structure for writing test cases? In this case, I think this could have been directly tested without the need of a separate GDTestFunction class, however, this might not have been a neat alternative. Thank you, Adeel
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