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2

I hesitate to suggest it, since gradient methods tend to be faster than non-gradient methods, but the Nelder-Mead "simplex" algorithm is easy to code and might be worth a try.


2

When you have only box constraints I don't think Frank Wofle is very efficient. Frank Wolfe can handle more complex constraints. You should try a quasi newton algorithm like l-bfgs-b or a truncated newton conjugate gradient algorithm with projection.


3

From the looks of it (simple feasible set, convex objective, gradient available), Frank-Wolfe indeed makes a lot of sense here. I can point out that there exist many variants of the algorithm, and that several are implemented in this recent Julia package. This can give you a quick way to experiments various algorithmic configurations. If you do decide to ...


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