This algorithm belongs to the sequential quadratic programming (SQP) type methods. At each iteration, the search direction d is obtained by solving one quadratic programming according to the K-T condition of the minimax problem. When d is equal to zero, then the corresponding iteration point x is a K-T point, otherwise, d is a descent direction. Unlike the SQP type algorithms for nonlinear programming, the direction d doesn`t induce any Maratos like effect. A particular linear search with above-mentioned direction assure global convergence as well as superlinear convergence. Numerical results to date suggest the resulting algorithm is effective.
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Zhibin Zhu, 2004. An SQP Method for Solving the Nonlinear Minimax Problem. Journal of Applied Sciences, 4: 498-507.