An optical processor for matrix-by-vector multiplication: an application to the distance geometry problem in 1D

2021 ◽  
Author(s):  
Simon Hengeveld ◽  
Nara Rubiano da Silva ◽  
Douglas S. Gonçalves ◽  
Paulo Henrique Souto Ribeiro ◽  
Antonio Mucherino

Abstract We present the architecture of a new optical processor specialized in matrix-by-vector multiplication via the manipulation of the light wavefront. This processor can reach up to 1.2 Giga MAC (multiply-accumulate) operations per second using commercially available devices. Moreover, this architecture is compatible with hardware upgrade with potential to achieve processing speed above Tera MAC per second. We initially present the optical processor, and then discuss the use of such a processor for tackling a special class of the one-dimensional Distance Geometry Problem (DGP), which is a well-known NP-hard problem.

2017 ◽  
Vol 59 (2) ◽  
pp. 271-279 ◽  
Author(s):  
D. S. MAIOLI ◽  
C. LAVOR ◽  
D. S. GONÇALVES

Finding the intersection of $n$-dimensional spheres in $\mathbb{R}^{n}$ is an interesting problem with applications in trilateration, global positioning systems, multidimensional scaling and distance geometry. In this paper, we generalize some known results on finding the intersection of spheres, based on QR decomposition. Our main result describes the intersection of any number of $n$-dimensional spheres without the assumption that the centres of the spheres are affinely independent. A possible application in the interval distance geometry problem is also briefly discussed.


2018 ◽  
Vol 14 (2) ◽  
pp. 483-492
Author(s):  
Luiz Leduino de Salles Neto ◽  
Carlile Lavor ◽  
Weldon Lodwick

2019 ◽  
Author(s):  
Oskar Taubert ◽  
Ines Reinartz ◽  
Henning Meyerhenke ◽  
Alexander Schug

Abstract Summary The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct; an adaptation of the generic MaxEnt-Stress graph drawing algorithm to the domain of biological macromolecules. diSTruct is fast, provides reliable structural models even from incomplete or noisy distance data and integrates access to graph analysis tools. Availability and implementation diSTruct is written in C++, Cython and Python 3. It is available from https://github.com/KIT-MBS/distruct.git or in the Python package index under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.


2015 ◽  
Vol 197 ◽  
pp. 3-19 ◽  
Author(s):  
Guilherme Dias da Fonseca ◽  
Vinícius Gusmão Pereira de Sá ◽  
Raphael Carlos Santos Machado ◽  
Celina Miraglia Herrera de Figueiredo

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