Optimization algorithms - Simulated annealing and neural network processing

Details Optimization algorithms - Simulated annealing and neural network processing Optimization algorithms - Simulated annealing and neural network processing

Nov 1986

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1986apj...310..473j&link_type=abstract

Astrophysical Journal, Part 1 (ISSN 0004- 637X), vol. 310, Nov. 1, 1986, p. 473-481.

Mathematics

21

Algorithms, Neural Nets, Optimization, Simulated Annealing, Superconducting Devices, Systems Simulation, Annealing, Extremum Values, Fredholm Equations, Ill-Posed Problems (Mathematics), Remote Sensing

Scientific paper

Two algorithms which have been previously used for discrete optimization, simulated annealing and neural network processing, are developed and compared. It is demonstrated how these algorithms can be used to find global extrema of functions, while avoiding trapping in local extrema. In the standard treatment of neural network processors, only quadratic and linear terms in the function variables are included in the objective function. This traditional approach is extended to show how constraints not expressible in quadratic and linear terms (e.g., entropy) can be incorporated into the function to be minimized. The efficiency of the implementation of neural net processing is also demonstrated, and it is shown how its speed advantage over more traditional optimization techniques (even when implemented on serial processors) is related to its convergence properties. An important application of the results is in the interpretation of remote sensing data, since typical indirect sensing problems can be readily cast into the language of optimization theory; the methods presented here have the particular ability to solve severely ill posed inversion problems. The algorithms described here have been implemented on a serial processor but are cast in a form which is ideally suited for parallel processing.

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