A trust region method based on interior point techniques for nonlinear programming

A trust region method based on interior point techniques for nonlinear programming

An algorithm for minimizing a nonlinear function subject to nonlinear equality and inequality constraints is described. It can be seen as an extension of primal interior point methods to non-convex optimization. The new algorithm applies sequential quadratic programming techniques to a sequence of b...

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Veröffentlicht in:Mathematical programming 2000-11, Vol.89 (1), p.149-185
Hauptverfasser: BYRD, Richard H, GILBERT, Jean Charles, NOCEDAL, Jorge
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container_title Mathematical programming
container_volume 89
creator BYRD, Richard H
GILBERT, Jean Charles
NOCEDAL, Jorge
description An algorithm for minimizing a nonlinear function subject to nonlinear equality and inequality constraints is described. It can be seen as an extension of primal interior point methods to non-convex optimization. The new algorithm applies sequential quadratic programming techniques to a sequence of barrier problems, and uses trust regions to ensure the robustness of the iteration and to allow the direct use of second order derivatives. An analysis of the convergence properties of the new method is presented.
doi_str_mv 10.1007/pl00011391
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subjects Applied sciences
Computer Science
Exact sciences and technology
Mathematical programming
Operational research and scientific management
Operational research. Management science
Other
title A trust region method based on interior point techniques for nonlinear programming
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