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This operate is just like the find_min_bobyqa routine other than that it negates the target functionality right before undertaking optimization. Consequently this purpose will make an effort to discover the maximizer of the target instead of the minimizer.
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A worldwide design that higher bounds our goal perform. That is a non-parametric piecewise linear product derived from all functionality evaluations at any time viewed from the global_function_search object.
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This function finds the submatrix in just a user provided matrix which has the largest sum. It then zeros out that submatrix and repeats the process until eventually no more maximal submatrices can be found.
The values of the variables are constrained by upper and reduced bounds. The following paper, revealed in 2009 by Powell, describes the comprehensive working from the BOBYQA algorithm. The BOBYQA algorithm for certain constrained optimization with out derivatives by M.J.D. Powell
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