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On the entropic regularization method for solving min-max problems with applications 总被引:4,自引:0,他引:4
Consider a min-max problem in the form of min
xX
max1im
{f
i
(x)}. It is well-known that the non-differentiability of the max functionF(x) max1im
{f
i
(x)} presents difficulty in finding an optimal solution. An entropic regularization procedure provides a smooth approximationF
p(x) that uniformly converges toF(x) overX with a difference bounded by ln(m)/p, forp > 0. In this way, withp being sufficiently large, minimizing the smooth functionF
p(x) overX provides a very accurate solution to the min-max problem. The same procedure can be applied to solve systems of inequalities, linear programming problems, and constrained min-max problems.This research work was supported in part by the 1995 NCSC-Cray Research Grant and the National Textile Center Research Grant S95-2. 相似文献
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本文提出一种以最大熵方法为基础的光滑技术,用来求解和“极大值”函数有关的一类不可微优化问题,解决问题的基本思路,是用一个称之为“凝聚”函数的光滑函数直接代替不可微的极大值函数,文中给出了该函数的推导和证明了它的一些有用性质,使用这一光滑技术,可把无约束和有约束极大极小两种问题均转化为光滑函数的无约束优化问题,因此可以直接利用现有的无约束优化算法软件解这类不可微优化问题,本文方法特别易于计算机实现,而且收敛速度快、数值稳定性好。 相似文献
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