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Inexact SQP methods for equality constrained optimization
| Content Provider | CiteSeerX |
|---|---|
| Author | Byrd, Richard H. Curtis, Frank E. Nocedal, Jorge |
| Abstract | Abstract. We present an algorithm for large-scale equality constrained optimization. The method is based on a characterization of inexact sequential quadratic programming (SQP) steps that can ensure global convergence. Inexact SQP methods are needed for large-scale applications for which the iteration matrix cannot be explicitly formed or factored and the arising linear systems must be solved using iterative linear algebra techniques. We address how to determine when a given inexact step makes sufficient progress toward a solution of the nonlinear program, as measured by an exact penalty function. The method is globalized by a line search. An analysis of the global convergence properties of the algorithm and numerical results are presented. Key words. large-scale optimization, constrained optimization, sequential quadratic programming, inexact linear system solvers, Krylov subspace methods AMS subject classifications. 49M37, 65K05, 90C06, 90C30, 90C55 1. Introduction. In |
| File Format | |
| Journal | SIAM J. Opt |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Inexact Sqp Method Exact Penalty Function Sufficient Progress Large-scale Optimization Inexact Step Line Search Global Convergence Numerical Result Iteration Matrix Cannot Key Word Large-scale Application Inexact Sequential Quadratic Programming Large-scale Equality Iterative Linear Algebra Technique Sequential Quadratic Programming Linear System Inexact Linear System Solver Global Convergence Property Nonlinear Program |
| Content Type | Text |
| Resource Type | Article |
