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dcyu请看过来!

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发表于 2003-8-15 20:28:59 | 显示全部楼层 |阅读模式
SUMT算法(序列无约束最优化方法)求助!!!
我们在建模过程中要用到这种算法,
看了很多书还是不太明白,
罚函数的选取准则也搞不懂。
请指点!!
有编程的例子最好了
谢谢
发表于 2003-8-15 22:26:09 | 显示全部楼层
一本书你都看不明白。别人怎么能几句说清楚
 楼主| 发表于 2003-8-16 10:02:44 | 显示全部楼层
抛砖引玉,
你难道没有听过!


发表于 2003-8-16 10:40:11 | 显示全部楼层
哈哈,dcyu才能来呀,我只好路过了

发表于 2003-8-16 23:12:51 | 显示全部楼层
我琢磨了一下
真的不是三两句话能说清楚的
这种介绍性的东西还不如针对某个具体问题的讨论来的方便
不过对于看书
不比看太多  有一些比较经典的最优化教材
把这些琢磨好就可以了
看的越多有时候反而越糊涂
发表于 2003-8-17 03:22:26 | 显示全部楼层
偶也没有用过这种方法,你用google搜一下,其中偶搜的一个解释,这个算法介绍的也不多,而且有局限性,你看看下面的说明好了。

Interior-point solvers for nonlinear programming are the subjects of intense current investigation. An algorithm of this class, known as the sequential unconstrained minimization technique (SUMT) was actually proposed in the 1960s, in the book of Fiacco and McCormick (1968). The idea at that time was to define a barrier-penalty function for the NLP as follows:
where   is a small positive parameter. Given some value of  , the algorithm finds an approximation to the minimizer   of  . It then decreases   and repeats the minimization process. Under certain assumptions, one can show that   as   so the sequence of iterates generated by SUMT should approach the solution of the nonlinear program provided that   is decreased to zero. The difficulties with this approach are that all iterates must remain strictly feasible with respect to the inequality constraints (otherwise the log functions are not defined), and the subproblem of minimizing   becomes increasingly difficult to solve as   becomes small, as the Hessian of this function becomes highly ill conditioned and the radius of convergence becomes tiny. Many  implementations of this approach were attempted, including some with enhancements such as extrapolation to obtain good starting points for each value of  . However, the approach does not survive in the present generation of software, except through its profound influence on the interior-point research  of the past 15 years.
发表于 2003-8-17 16:34:45 | 显示全部楼层
我觉得还是看书比较好
让老师推荐一本这方面的书
静下心来好好读一读
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