Hybridization of DIRECT with Local Optimization Techniques

The hybridization of DIRECT with IF or BFGS operates by first allowing DIRECT to globally explore the search space. As described in my previous post, a large number of function evaluations would be required in order for DIRECT to find the global optimum to our desired level of accuracy. However, DIRECT converges rapidly to locations that are in the neighbourhood of the global optimum, providing a good starting position to execute either implicit filter or BFGS.

Based on experimentation, a budget of 200 times the dimension of the problem, function evaluations enables DIRECT to efficiently determine a reasonable starting position to begin IF or BFGS. Starting from the termination position of DIRECT, we then allow either IF or BFGS to fine-tune DIRECT’s global search, in what is called the precision phase. Assuming that the global optimum is contained within the boundaries of the feasible region, IF should rapidly converge to a highly accurate global optimum.

It is, however, possible for the position of the global optimum to be located outside of the domain provided to IF. In this case, IF is strictly confined to this region and will not be able to converge to this position. In theory, this could have a negative effect on the model fitting process. We therefore provide a second hybridization option by replacing IF with the unbounded optimization algorithm, BFGS, which is able to explore outside of the bound constraints provided to IF.


I am a recent (2012) Memorial University B.Sc. graduate in Applied Mathematics and Physics. I am currently working in the Mathematics & Statistics Department at MUN, under the supervision of Dr. Ronald Haynes. My current research is in numerical optimization, as it pertains to oil well placement and optimal parameterization. In my spare time I enjoy exercising, spending time with family and friends and playing soft rock and blues guitar.

Posted in Optimization Algorithms

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