By Alberto Sillitti, Xiaofeng Wang, Angela Martin
Read Online or Download Agile processes in software engineering and extreme programming : 11th International Conference, XP 2010, Trondheim, Norway, June 1-4, 2010. Proceedings PDF
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Additional info for Agile processes in software engineering and extreme programming : 11th International Conference, XP 2010, Trondheim, Norway, June 1-4, 2010. Proceedings
We show that the CA subproblem also in this case provides a merit (gap) function automatically, and investigate its continuity, directional differentiability and descent properties, under assumptions similar to those for the primal application. We then study the use of these merit functions in an SQP algorithm for VIP(F,X). We establish that descent properties can be achieved under mild requirements on the problem data, while also eliminating the need for a penalty function for the linearized constraints.
2 presents the column generation CA algorithm in its application 36 Nonlinear Progmmming and Variational Inequality Problems to GVIP(F, u, X). In simplicial decomposition, the columns generated are extreme points, while the solutions to the CA subproblems need not be; this introduces a possibility to define columns either as the subproblem solutions or as their extensions to the boundary of the set X. Further, we discuss the possibility of dropping columns that received a zero (or, a very low) weight in the solution to the restricted master problem defined by the current set of columns, and more general means to construct the new column and to redefine the inner representation.
We then study the use of these merit functions in an SQP algorithm for VIP(F,X). We establish that descent properties can be achieved under mild requirements on the problem data, while also eliminating the need for a penalty function for the linearized constraints. The results obtained extend and improve upon those in [TaF95, TaF96] for merit functions for primal-dual variational inequalities based on a quadratic function 'P. Chapter 5 Chapter 5 analyzes the convergence of the CA algorithm in applications to CNDP(f, u, X).