Non-Smooth Optimization Algorithms, System Theory and Software Tools.

1995 ◽  
Author(s):  
Elijah Polak
2012 ◽  
Vol 45 (17) ◽  
pp. 291-298 ◽  
Author(s):  
Hans-Bernd Dürr ◽  
Christian Ebenbauer

1993 ◽  
Vol 115 (4) ◽  
pp. 978-987 ◽  
Author(s):  
K. Kurien Issac

This paper describes a nondifferentiable optimization (NDO) algorithm for solving constrained minimax linkage synthesis. Use of a proper characterization of minima makes the algorithm superior to the smooth optimization algorithms for minimax linkage synthesis and the concept of following the curved ravines of the objective function makes it very effective. The results obtained are superior to some of the reported solutions and demonstrate the algorithm’s ability to consistently arrive at actual minima from widely separated starting points. The results indicate that Chebyshev’s characterization is not a necessary condition for minimax linkages, while the characterization used in the algorithm is a proper necessary condition.


Author(s):  
Jose-Maria Carazo ◽  
I. Benavides ◽  
S. Marco ◽  
J.L. Carrascosa ◽  
E.L. Zapata

Obtaining the three-dimensional (3D) structure of negatively stained biological specimens at a resolution of, typically, 2 - 4 nm is becoming a relatively common practice in an increasing number of laboratories. A combination of new conceptual approaches, new software tools, and faster computers have made this situation possible. However, all these 3D reconstruction processes are quite computer intensive, and the middle term future is full of suggestions entailing an even greater need of computing power. Up to now all published 3D reconstructions in this field have been performed on conventional (sequential) computers, but it is a fact that new parallel computer architectures represent the potential of order-of-magnitude increases in computing power and should, therefore, be considered for their possible application in the most computing intensive tasks.We have studied both shared-memory-based computer architectures, like the BBN Butterfly, and local-memory-based architectures, mainly hypercubes implemented on transputers, where we have used the algorithmic mapping method proposed by Zapata el at. In this work we have developed the basic software tools needed to obtain a 3D reconstruction from non-crystalline specimens (“single particles”) using the so-called Random Conical Tilt Series Method. We start from a pair of images presenting the same field, first tilted (by ≃55°) and then untilted. It is then assumed that we can supply the system with the image of the particle we are looking for (ideally, a 2D average from a previous study) and with a matrix describing the geometrical relationships between the tilted and untilted fields (this step is now accomplished by interactively marking a few pairs of corresponding features in the two fields). From here on the 3D reconstruction process may be run automatically.


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