SNOPT: An SQP algorithm for large-scale constrained optimization

作     者：Gill, PE Murray, W Saunders, MA 

作者机构：Univ Calif San Diego Dept Math La Jolla CA 92093 USA Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA 

出 版 物：《SIAM JOURNAL ON OPTIMIZATION》 (工业与应用数学会最优化杂志)

年 卷 期：2002年第12卷第4期

页      面：979-1006页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：large-scale optimization nonlinear programming nonlinear inequality constraints sequential quadratic programming quasi-Newton methods limited-memory methods 

摘      要：Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints ( linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems. SNOPT is a particular implementation that makes use of a semidefinite QP solver. It is based on a limited-memory quasi-Newton approximation to the Hessian of the Lagrangian and uses a reduced-Hessian algorithm (SQOPT) for solving the QP subproblems. It is designed for problems with many thousands of constraints and variables but a moderate number of degrees of freedom ( say, up to 2000). An important application is to trajectory optimization in the aerospace industry. Numerical results are given for most problems in the CUTE and COPS test collections ( about 900 examples).