By Arieh Iserles
Acta Numerica has confirmed itself because the leading discussion board for the presentation of definitive studies of numerical research themes. Highlights of this year's factor contain articles on sequential quadratic programming, mesh adaption, loose boundary difficulties, and particle tools in continuum computations. The invited papers will enable researchers and graduate scholars alike to fast seize the present tendencies and advancements during this box.
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Extra resources for Acta Numerica 1995 (Volume 4)
Fukushima (1986), 'A successive quadratic programming algorithm with global and superlinear convergence properties', Mathematical Programming 35, 253264. D. Gabay (1982), 'Reduced quasi-Newton methods with feasibility improvement for nonlinearly constrained optimization', Mathematical Programming Study 16, 18-44. U. M. Garcia-Palomares and O. L. Mangasarian (1976), 'Superlinearly convergent quasi-Newton methods for nonlinearly constrained optimization problems', Mathematical Programming 11, 1-13.
Tolle (1989), 'A strategy for global convergence in a sequential quadratic programming algorithm', SIAM Journal on Numerical Analysis 21, 600-623. P. T. Boggs and J. W. Tolle (1994), 'Convergence properties of a class of rank-two updates', SIAM Journal on Optimization 4, 262-287. 48 BOGGS AND TOLLE P. T. Boggs, J. W. Tolle and A. J. Kearsley (1991), 'A merit function for inequality constrained nonlinear programming problems', Internal Report 4702, National Institute of Standards and Technology.
In this section we mention two of these, namely, problem structure and the solution of large scale quadratic programs. Problems are considered large if, to solve them efficiently, either their structure must be exploited or the storage and manipulation of the matrices involved must be handled in a special manner. The most obvious structure, and the one most commonly considered, is sparsity of the matrices V/i, Vg and HC. Typically in large scale problems most constraints depend on only a few of the variables and the objective function is 'partially separable' (a partially separable function is, for example, made up of a sum of functions, each of which depends on only a few of the variables).
Acta Numerica 1995 (Volume 4) by Arieh Iserles