  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.
