Inexact SQP Interior Point Methods and Large Scale Optimal Control Problems

Authors:
F. Leibfritz;E. W. Sachs
Affiliations:
-;-
Venue:
SIAM Journal on Control and Optimization
Year:
1999

Citing 0
Cited 4

Numerical solution of special linear and quadratic programs via a parallel interior-point method

Parallel Computing - Special issue: Parallel computing in numerical optimization
The convergence of an interior point method for an elliptic control problem with mixed control-state constraints

Computational Optimization and Applications
A Truncated SQP Method Based on Inexact Interior-Point Solutions of Subproblems

SIAM Journal on Optimization
Performance analysis of gradient neural network exploited for online time-varying quadratic minimization and equality-constrained quadratic programming

Neurocomputing

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Abstract

Optimal control problems with partial differential equations lead to large scale nonlinear optimization problems with constraints. An efficient solver which takes into account the structure and also the size of the problem is an inexact sequential quadratic programming method where the quadratic problems are solved iteratively. Based on a reformulation as a mixed nonlinear complementarity problem we give a measure of when to terminate the iterative quadratic program solver. For the latter we use an interior point algorithm. Under standard assumptions, local linear, superlinear, and quadratic convergence can be proved. The numerical application is an optimal control problem from nonlinear heat conduction.