The $L_q$-weighted dual programming of the linear Chebyshev approximation and an interior-point method

TL;DR

This paper introduces an $L_q$-weighted dual programming approach for linear Chebyshev approximation, providing a new interior-point method that converges faster than traditional Lawson's iteration, supported by numerical experiments.

Contribution

It develops an $L_q$-weighted dual programming framework and proposes a Newton-type interior-point method for improved convergence in Chebyshev approximation.

Findings

The interior-point method converges faster than Lawson's iteration.

Numerical experiments demonstrate the method's efficiency and accuracy.

The approach effectively finds reference points for minimax approximation.

Abstract

Given samples of a real or complex-valued function on a set of distinct nodes, the traditional linear Chebyshev approximation is to compute the best minimax approximation on a prescribed linear functional space. Lawson's iteration is a classical and well-known method for that task. However, Lawson's iteration converges linearly and in many cases, the convergence is very slow. In this paper, by the duality theory of linear programming, we first provide an elementary and self-contained proof for the well-known Alternation Theorem in the real case. Also, relying upon the Lagrange duality, we further establish an $L_q$-weighted dual programming for the linear Chebyshev approximation. In this framework, we revisit the convergence of Lawson's iteration, and moreover, propose a Newton type iteration, the interior-point method, to solve the $L_2$-weighted dual programming. Numerical experiments are reported to demonstrate its fast convergence and its capability in finding the reference points that characterize the unique minimax approximation.