SQ2P, Sequential Quadratic Constrained Quadratic Programming

We follow the popular approach for unconstrained minimization, i.e. we develop a local quadratic model at a current approximate minimizer in conjunction with a trust region. We then minimize this local model in order to find the next approximate minimizer. Asymptotically, finding the local minimizer of the quadratic model is equivalent to applying Newton’s method to the stationarity condition.

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