mcGlobaloptim: Global optimization using Monte Carlo and Quasi Monte Carlo simulation

For the purpose of Market Consistent actuarial valuation, we need to calibrate our models to market data. Traditional optimization methods based only on gradient (Newton, quasi-Newton…) fail, or provide volatile results. Indeed, they search for a solution in a local neighborhood, while objective functions might exhibit multiple local minima. The R package mcGlobaloptim achieves global optimization, combining Monte Carlo and Quasi-Monte Carlo simulation with local searches. The local searches can be speeded-up by the use of your computer’s (multiple) cores.

Currently, the function multiStartoptim generates pseudo-random and quasi-random numbers within the search domain specified by its bounds. Local searches are then performed by a user-selected method, either on the whole set of numbers generated as starting points, or only on the points lying under the objective function’s median. When (and only when) a high number of local searches are to be performed, parallel computation can be used to speed-up the search

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October 28, 2013 · 6:34 pm

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