Hudson [Hudson, D. J. Least-squares fitting of a polynomial constrained to be either non-negative, non-decreasing or convex. J. R. Statist. Soc. B. 31 113-118.] has described a complicated algorithm for least-squares fitting of polynomials constrained to be either nonnegative, nondecreasing, or convex. Alternate quadratic programming formulations which approximate general functions (not necessarily polynomials) by polygonal segmentation are presented here. The technique is simpler, more general and of wider applicability than that proposed of Hudson.
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机译:Hudson [Hudson,D. J.多项式的最小二乘拟合被约束为非负,非减或凸。 J. R. Statist。 Soc。 [B.31 113-118。]描述了一种复杂的算法,用于对多项式的最小二乘拟合进行约束,该多项式必须是非负的,非递减的或凸的。此处介绍了通过多边形分段近似通用函数(不一定是多项式)的替代二次编程公式。该技术比Hudson提出的技术更简单,更通用并且具有更广泛的适用性。
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