By P. Ciarlini, M. G. Cox, E. Filipe, F. Pavese, D. Richter

ISBN-10: 9810244940

ISBN-13: 9789810244941

A set of the revised contributions from the 5th workshop on complex mathematical and computational instruments in metrology, held in Caparica, Portugal, in may perhaps of 2000. comprises papers from exact curiosity teams in metrology software program and information fusion. DLC: Mensuration--Congresses.

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O Closedness of M at every y 2 dom M is equivalent to the closedness of gph M in the product space Y X . Closedness and upper semicontinuity compete to be the counterpart concept of the lower semicontinuity. a; b/ (these systems are also called semi-infinite and appear in different fields). In this way we may use 30 2 Modeling Uncertain Linear Semi-infinite Optimization Problems D indistinctly F. / or F. /. , F . / is D the same thing that F . D / where DÁ WD f t2T t at D cg. Observe that F is closed for free.

Machine Learning [18, 184, 185, 193, 194, 213, 217]. Data envelopment analysis [91, 102]. Functional approximation [90, 102, 132, 163, 218]. Computational linear algebra [102]. Linear functional equations [90, 102]. Convex geometry [91, 149, 198]. Location problems [102]. Robust optimization [91, 178]. Semidefinite optimization [91, 164]. Geometric optimization [102]. Combinatorial optimization [164]. 4 (Two Open Problems in Deterministic LSIO). 1. Convergence theorems for simplex-like methods.

Ii) If Z is a convex body and ˛ 2 RTCC , then PR satisfies the density assumption. 11) The assumption of (ii) guarantees that PR can be solved via grid discretization. , whenever Z D B1 , with I D f1; : : : ; 2n g. 11) may provide a reformulation of PR as LSIO problem with an index set of the same dimension as T . 2. 1, with an approximation error not greater than a given ˛ 2 Œ0; 1. 4). t. ˛ sin t / x1 sin t / x1 i h ; 1; t 2 0; 2 i h ; 1; t 2 0; 2 46 3 Robust Linear Semi-infinite Optimization Fig.

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