Titlebook: Optimization on Low Rank Nonconvex Structures; Hiroshi Konno,Phan Thien Thach,Hoang Tuy Book 1997 Springer Science+Business Media Dordrech

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Low-Rank Nonconvex Structureskably easier when the values of the variables in the first group (which are called . are temporarily fixed. Fundamental partitioning methods for mixed integer linear programming problems were developed in the 60’s (Benders (1962), Rosen (1964), Ritter (1967)) and ever since extended to nonlinear pro

Expertise

Global Search Methods and Basic D.C. Optimization Algorithmsematical programming. Although specific problems can be efficiently handled only by methods which take advantage of their particular structure, there are some general principles guiding global search processes. Furthermore, a general approach to low rank nonconvex problems is to transform a given pr

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Monotonic Problemsf practical interest. A natural question arises as to whether this method is strictly specific to the multiplicative structure, or it can be extended to a wider class of problems. In fact, a variety of quite different problems which have anything to do with the multiplicative structure appear to be

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Definitive

Design Centering and Related Geometric Problemsal nonlinear programming methods. Fortunately, most of these problems exhibit a d.c. structure, by exploiting which it is possible in several cases of interest to devise reasonably practical algorithms for locating a global optimum to sufficient accuracy in acceptable computing time. A typical examp

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Formatting and Integrating Soft Data: Stochastic Imaging via the Markov-Bayes Algorithmbability distributions. Joint spatial uncertainty about the phenomenon can then be assessed through those alternative images..A case study using a topographic elevation derived-data set (the Walker Lake data set) is included. The case study shows that the Markov-Bayes algorithm succeeds in reproduci

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