Nonlinear constrained optimizer and parallel processing for golden block line search

Due T. Nguyen; Wilson H. Tang; Yeou K. Tung; Hakizumwami B. Runesha

Due T. Nguyen Multidisciplinary Parallel- Vector Computation Center
Wilson H. Tang Hong Kong University of Science and Technology
Yeou K. Tung Hong Kong University of Science and Technology
Hakizumwami B. Runesha Multidisciplinary Parallel- Vector Computation Center

Abstract

Generalized exponential penalty functions are constructed for the multiplier methods in solving nonlinear programming problems. The non-smooth extreme constraint Gext is replaced by a single smooth constraint Gs by using the generalized exponential function (base a > 1). The well-known K.S. function is found to be a special case of our proposed formulation . Parallel processing for Golden block line search algorithm is then summarized, which can also be integrated into our formulation. Both small and large-scale nonlinear programming problems (up to 2000 variables and 2000 nonlinear constraints) have been solved to validate the proposed algorithms.

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References

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