Some properties of generalized convexity for sets and for functions are identified in case of the reliability polynomials of two dual minimal networks. A method of approximating the reliability polynomials of two dual minimal network is developed based on their mutual complementarity properties. The approximating objects are from the class of quadratic spline functions, constructed based both on interpolation conditions and on shape knowledge. It is proved that the approximant objects preserve the shape properties of the exact reliability polynomials. Numerical examples and simulations show the performance of the algorithm, both in terms of low complexity, small error and shape preserving. Possibilities of increasing the accuracy of approximation are discussed.

翻译：在两个双最小网络具有可靠性的情况下,可以确定组群和功能具有普遍共融性的某些特性。根据两个双最小网络的相互补充性,可以开发出一种近似于其可靠性的双最小网络的多元性的方法。相近的物体来自基于内推条件和形状知识而建的四面形样功能类别。事实证明,相近物体保留了精确可靠性多多边网络的形状性能。数字实例和模拟显示了算法的性能,从低复杂性、小误差和形状保护角度来说都是如此。讨论了提高近似准确性的可能性。