By Rick D. Craig
Concentrating on the prevention and early discovery of defects, the trying out technique defined during this textual content parallels the software program improvement lifecycle, and the authors define a versatile technique for making improvements to software program trying out functions
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Extra resources for Systematic Software Testing
3. 2 The updating phase In general, we can of course not expect that yt solves GVIP(F, u, X). The second step of a CA algorithm utilizes the subproblem solution yt to improve upon xt. In the basic algorithm, we utilize yt in the construction of a direction of search, df := yt - x t , in which a step is taken to reduce some measure of the distance from xt to the set SOL(F, u, X). We are interested in utilizing a measure which can be evaluated at any point of dom un X, has sufficient continuity properties, and is a merit function in the sense that its set of (local) minimizers (or, stationary points) coincide with SOL(F,u,X).
We also supply formulas for the calculation of directional derivatives and gradients, when they exist. An important observation made is that the possible iteration-dependency of the cost approximating function cp, which for the most part is immaterial to results established in Chapter 3 for nonlinear programs, here does affect some results, since the merit function 'Ij; is constructed based on the current iterate. The CA algorithm contains a large class of known algorithms for variational inequalities, some of which have been used in conjunction with the gap functions that are inherent in their construction.
We show that the 28 Nonlinear Programming and Variational Inequality Problems standard form of the CA algorithm is of the form of a splitting method as well as an inexact version of a regularization method, albeit it is much more general in scope, and we derive several algorithms, among which is Korpelevich's extra-gradient method, as inexact proximal point methods. 3 considers an optimization problem with explicit constraints, and shows how the framework of cost approximation covers primal-dual methods such as sequential quadratic programming, the Arrow-Hurwicz and Uzawa methods, and dual subgradient optimization, as well as a class of augmented Lagrangean methods.