Nonlinearly constrained optimization via sequential regularized linear programming
This thesis proposes a new active-set method for large-scale nonlinearly con strained optimization. The method solves a sequence of linear programs to generate search directions. The typical approach for globalization is based on damping the search directions with a trust-region constraint; our p...
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| Language: | English |
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University of British Columbia
2010
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| Online Access: | http://hdl.handle.net/2429/29648 |