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Linear k-Monotonicity Preserving Algorithms and Their Approximation Properties

机译:线性k单调保真算法及其逼近性质

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This paper examines the problem of finding the linear algorithm (operator) of finite rank n (i.e. with a n-dimensional range) which gives the minimal error of approximation of identity operator on some set over all finite rank n linear operators preserving the cone of k-monotonicity functions. We introduce the notion of linear relative (shape-preserving) n-width and find asymptotic estimates of linear relative n-widths for linear operators preserving k-monotonicity in the space C~k[0,1]. The estimates show that if linear operator with finite rank n preserves k-monotonicity, the degree of simultaneous approximation of derivative of order 0 ≤ i ≤ k of continuous functions by derivatives of this operator cannot be better than n~(-2) even on the set of algebraic polynomials of degree k + 2 (as well as on bounded subsets of Sobolev space W_∞~((k+2)) [0,1]).
机译:本文探讨了寻找有限阶数n(即n维范围)的线性算法(算子)的问题,该算法给出了在所有有限阶数n线性算子上保持矩阵的锥度的恒等式逼近最小误差。 k单调函数。我们介绍了线性相对(形状保持)n宽度的概念,并找到了在空间C〜k [0,1]中保持k单调性的线性算子的线性相对n宽度的渐近估计。估计表明,如果具有有限秩n的线性算子保持k单调性,则该算子的导数对连续函数的阶数0≤i≤k的导数的同时逼近程度甚至不会比n〜(-2)好。阶数为k + 2的代数多项式集(以及Sobolev空间W_∞〜((k + 2))[0,1]的有界子集)。

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