Abstract
In this paper a new class of self-scaling VM-algorithms for nonlinear optimization are investigated. Some theoretical results are given on the scaling strategies that guarantee the global and super linear convergence of the new proposed algorithms. Numerical evidence on thirty two well-known nonlinear test functions is generally encouraging.
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