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How do I make non-linear portfolio optimization faster? I want to optimize a portfolio of 50 assets on the following measure: (mean(portfolio returns_t-1)/sd(portfolio returns_t-1)-mean(portfolio returns_t-2)/sd(portfolio returns_t-2). So I want the measure being as high as possible by choosing according portfolio weights

Currently I use the function DEoptim on R, however, with 50 assets the running time is quite long and I don’t know whether the solution is globally optimal or only locally. Is there a solution to make portfolio optimization on such difficult measures faster? Better algorithms? Switching to different programming languages? Thanks in advance!

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  • Please check how to ask and provide a reproducible example Commented Dec 5, 2017 at 12:17
  • I don't understand your objective function but in general for smooth NLP (nonlinear programming) problems a real NLP solver is more appropriate than a DE heuristic (which is basically "trial-and-error"). Commented Dec 5, 2017 at 12:32
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