A power penalty method for linear complementarity problems
We propose a power penalty approach to a linear complementarity problem (LCP) in R^n based on approximating the LCP by a nonlinear equation. We prove that the solution to this equation converges to that of the LCP at an exponential rate when the penalty parameter tends to infinity.
Summary form only given, as follows. A study is presented of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed-point arithmetic to implement the backpropagation algorithm. >