
A sequential quadratic programming method with line search is analyzed and studied for finding the local solution of a nonlinear semidefinite programming problem resulting from the discrete-time output feedback problem. The method requires an initial feasible point with respect to two positive definite constraints. By parameterizing the optimization problem we ease that requirement. The method is tested numerically on several test problems chosen from the benchmark collection (Leibfritz, 2004).
Quadratic Programming, Quadratically constrained quadratic program, Geometry, Quadratic programming, Quantum mechanics, Engineering, Numerical mathematical programming methods, Point (geometry), Nonlinear programming, QA1-939, FOS: Mathematics, Semidefinite programming, Numerical Analysis, Nonlinear Programming, Numerical Optimization Techniques, Semidefinite Programming, Geography, Physics, Mathematical optimization, Sequential quadratic programming, Quadratic equation, System Identification Techniques, Computer science, Model Predictive Control in Industrial Processes, Control and Systems Engineering, Physical Sciences, Nonlinear system, Benchmark (surveying), Mathematics, Nonlinear Systems, Geodesy, Mixed-Integer Nonlinear Programs
Quadratic Programming, Quadratically constrained quadratic program, Geometry, Quadratic programming, Quantum mechanics, Engineering, Numerical mathematical programming methods, Point (geometry), Nonlinear programming, QA1-939, FOS: Mathematics, Semidefinite programming, Numerical Analysis, Nonlinear Programming, Numerical Optimization Techniques, Semidefinite Programming, Geography, Physics, Mathematical optimization, Sequential quadratic programming, Quadratic equation, System Identification Techniques, Computer science, Model Predictive Control in Industrial Processes, Control and Systems Engineering, Physical Sciences, Nonlinear system, Benchmark (surveying), Mathematics, Nonlinear Systems, Geodesy, Mixed-Integer Nonlinear Programs
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