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Discrete least-norm approximation by nonnegative (trigonometric) polynomials and rational functions

机译:非负(三角)多项式和有理函数的离散最小范数逼近

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Polynomials, trigonometric polynomials, and rational functions are widely used for the discrete approximation of functions or simulation models. Often, it is known beforehand that the underlying unknown function has certain properties, e.g., nonnegative or increasing on a certain region. However, the approximation may not inherit these properties automatically. We present some methodology (using semidefinite programming and results from real algebraic geometry) for least-norm approximation by polynomials, trigonometric polynomials, and rational functions that preserve nonnegativity.
机译:多项式,三角多项式和有理函数广泛用于函数或仿真模型的离散逼近。通常,预先知道潜在的未知函数具有某些性质,例如在某些区域上是非负的或增加的。但是,近似值可能不会自动继承这些属性。我们为多项式,三角多项式和保留非负有理函数的最小范数逼近提供了一些方法(使用半定规划和实数代数几何的结果)。

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