Algorithm 414: Chebyshev approximation of continuous functions by a Chebyshev system of functions
Gene Howard Golub, L. B. Smith
Abstract
Gene Howard Golub, L. B. Smith
Abstract
The second algorithm of Remez can be used to compute the minimax approximation to a function, ƒ( x ), by a linear combination of functions, { Q i ( x )} n 0 , which form a Chebyshev system. The only restriction on the function to be approximated is that it be continuous on a finite interval [ a , b ]. An Algol 60 procedure is given, which will accomplish the approximation. This implementation of the second algorithm of Remez is quite general in that the continuity of ƒ( x ) is all that is required whereas previous implementations have required differentiability, that the end points of the interval be “critical points,” and that the number of “critical points” be exactly n + 2. Discussion of the method used and of its numerical properties is given as well as some computational examples of the use of the algorithm. The use of orthogonal polynomials (which change at each iteration) as the Chebyshev system is also discussed.
OpenAlex reports 19 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The second algorithm of Remez can be used to compute the minimax approximation to a function, ƒ( x ), by a linear combination of functions, { Q i ( x )} n 0 , which form a Chebyshev system. The only restriction on the function to be approximated is that it be continuous on a finite interval [ a , b ]. An Algol 60 procedure is given, which will accomplish the approximation. This implementation of the second algorithm of Remez is quite general in that the continuity of ƒ( x ) is all that is required whereas previous implementations have required differentiability, that the end points of the interval be “critical points,” and that the number of “critical points” be exactly n + 2. Discussion of the method used and of its numerical properties is given as well as some computational examples of the use of the algorithm. The use of orthogonal polynomials (which change at each iteration) as the Chebyshev system is also discussed.
Key concepts: Chebyshev iteration, Mathematics, Minimax approximation algorithm, Chebyshev filter, Equioscillation theorem, Differentiable function, Chebyshev polynomials, Minimax