Sequential Quadratic Programming Methods for Nonlinear Programming
作者： Philip E. Gill , Walter Murray , Michael A. Saunders , Margaret H. Wright
DOI: 10.1007/978-3-642-52465-3_23
关键词: Augmented Lagrangian method 、 Mathematical optimization 、 Active set method 、 Second-order cone programming 、 Nonlinear programming 、 Fractional programming 、 Sequential quadratic programming 、 Quadratically constrained quadratic program 、 Quadratic programming 、 Computer science
摘要: Sequential quadratic programming (SQP) methods are among the most effective techniques known today for solving nonlinearly constrained optimization problems. This paper presents an overview of SQP based on a quasi-Newton approximation to Hessian Lagrangian function (or augmented function). We briefly describe some issues in formulation methods, including form subproblem and choice merit function. conclude with list available software.
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