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In this paper, a new iterative algorithm is proposed to design IIR digital filters in the minimax sense. Instead of directly minimizing the error limit of the approximation error, the proposed algorithm employs a bisection searching procedure to locate the minimum error limit. At each iteration, a feasibility problem with a given error limit is to be solved, which is constructed by applying the semidefinite programming (SDP) relaxation technique to transform the nonconvex approximation error into a convex form. In practice, however, the truly minimax solution cannot be always obtained by using this iterative procedure. Therefore, a regularization term needs to be incorporated in the objective of the feasibility problem at each iteration. Another bisection searching procedure is then deployed to find the minimum weight utilized in the regularized objective function of the feasibility problem. The stability of designed filters can be guaranteed by a monitoring strategy, which does not need to incorporate any other constraint to the formulation of the feasibility problem. The convergence of the proposed method can be guaranteed. The performances have been demonstrated by filter examples.