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Neural network for non‐linear programming with linear equality constraints
Author(s) -
Osowski Stanisłsaw
Publication year - 1992
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.4490200108
Subject(s) - linear programming , artificial neural network , integrator , preprocessor , contrast (vision) , mathematical optimization , lagrangian , computer science , linear fractional programming , mathematics , algorithm , control theory (sociology) , artificial intelligence , computer network , control (management) , bandwidth (computing)
This paper presents a simplified approach to neural optimization in the presence of linear equality constraints. In contrast to the standard Lagrangian approach, the constraints simplify the final neural circuit instead of complicating it. the number of elements used is also significantly reduced. Instead of n + t integrators we need only n – t . There is also a similar saving in the number of preprocessing non‐linear devices. Elimination of the constraints allows a large speed‐up of the solution.