Research on trust-region algorithms for nonlinear programming

Abstract

This report discusses research on the following topics: interior- point methods for linear programming; trust-region SQP newton's method for general nonlinear programming problems; trust-region SQP newton's method for large sparse nonlinear programming problems with applications to oil reservoir management; a unified approach to global convergence of trust-region methods for nonsmooth optimization; and SQP augmented lagrangian BRGS algorithm for constrained optimization. (LSP).

Related Papers

• → A Feasible Trust-Region Sequential Quadratic Programming Algorithm(2004)57 cited
• → GLOBAL CONVERGENCE OF A TRUST REGION SEQUENTIAL QUADRATIC PROGRAMMING METHOD(2005)2 cited
• → A Trust-Region Algorithm Based on Global SQP for Reactive Power Optimization(2005)4 cited
• → A Modified Filter Trust Region Method for Nonlinear Programming(2006)4 cited
• Different Second Order Approximationsin a model-based SQP Trust-Region DFO Method(2014)