Abstract
If a fractional program does not have a unique solution or the feasible set is unbounded, numerical difficulties can occur. By using a prox-regularization method that generates a sequence of auxiliary problems with unique solutions, these difficulties are avoided. Two regularization methods are introduced here. They are based on Dinkelbach-type algorithms for generalized fractional programming, but use a regularized parametric auxiliary problem. Convergence results and numerical examples are presented.
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Gugat, M. Prox-Regularization Methods for Generalized Fractional Programming. Journal of Optimization Theory and Applications 99, 691–722 (1998). https://doi.org/10.1023/A:1021759318653
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DOI: https://doi.org/10.1023/A:1021759318653