W.Murray, Sequential quadratic programming methods for large-scale problems. Computational issues in high performance software for nonlinear optimization , Comput. Optim. Appl. 7 (1997), 127--142.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....expensive solution of the nonlinear state equation in every step, but as indicated above allow use of the structure of optimal control problems. In addition, SQP methods have proven to be very successful for the solution of other nonlinear programming problems. See e.g. 5] 9] 23] 24] 40] [47], 48] 50] 56] As outlined before, we use SQP based methods for the solution of (1.1) i.e. the all at once approach. However, the reduced problem (1.2) is important to us for two reasons. Firstly, the relation between the full problem (1.1) and the reduced problem (1.2) gives important ....

W. Murray, Sequential quadratic programming methods for large problems, Computational Optimization and Applications, 7 (1997), pp. 127--142.

....next iterate (x k 1 ; k 1 ) Solving such a subproblem is itself an iterative procedure, with the minor iterations of an SQP method being the iterations of the QP method. For an overview of SQP methods, see, for example, Boggs and Tolle [6] Fletcher [30] Gill, Murray and Wright [47] Murray [55], and Powell [65] 2.3. The modi ed Lagrangian. Let x k and k be estimates of x and . For several reasons, our SQP algorithm is based on the modi ed Lagrangian associated with GNP, namely L(x; x k ; k ) f(x) T k d L (x; x k ) 2.2) which is de ned in terms of the constraint ....

W. Murray, Sequential quadratic programming methods for large-scale problems, J. Comput. Optim. Appl., 7 (1997), pp. 127-142. 28 P. E. GILL, W. MURRAY AND M. A. SAUNDERS

....direction towards the next iterate (x k 1 ; k 1 ) Solving such a subproblem is itself an iterative procedure, with the minor iterations of an SQP method being the iterations of the QP method. For an overview of SQP methods, see, for example, Fletcher [19] Gill, Murray and Wright [29] Murray [36], and Powell [46] 2.3. The modified Lagrangian. Let x k and k be estimates of x and . For several reasons, our SQP algorithm is based on the modified Lagrangian associated with GNP, namely L(x; x k ; k ) f(x) Gamma T k d L (x; x k ) 2.2) which is defined in terms of the ....

W. Murray, Sequential quadratic programming methods for large-scale problems, J. Comput. Optim. Appl., 7 (1997), pp. 127--142.

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W.Murray, Sequential quadratic programming methods for large-scale problems. Computational issues in high performance software for nonlinear optimization , Comput. Optim. Appl. 7 (1997), 127--142.

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