Discrete Applied Mathematics的目的是汇集算法和应用离散数学不同领域的研究论文,以及组合数学在信息学和科学技术各个领域的应用。发表在期刊上的文章可以是研究论文、简短笔记、调查报告,也可以是研究问题。“传播”部分将致力于尽可能快地出版最近的研究成果,这些成果由编辑委员会的一名成员检查和推荐出版。《华尔街日报》还将出版数量有限的图书公告和会议记录。这些程序将得到充分的裁决,并遵守《华尔街日报》的正常标准。官网链接:https://www.sciencedirect.com/journal/discrete-applied-mathematics/about/aims-and-scope

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A general framework for solving nonlinear least squares problems without the employment of derivatives is proposed in the present paper together with a new general global convergence theory. With the aim to cope with the case in which the number of variables is big (for the standards of derivative-free optimization), two dimension-reduction procedures are introduced. One of them is based on iterative subspace minimization and the other one is based on spline interpolation with variable nodes. Each iteration based on those procedures is followed by an acceleration step inspired in the Sequential Secant Method. The practical motivation for this work is the estimation of parameters in Hydraulic models applied to dam breaking problems. Numerical examples of the application of the new method to those problems are given.

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