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We use a penalty function approach and the gradient method to solve minimum norm problems. A class of penalty functions is introduced that allows one to transform constrained optimization minimum norm problems to unconstrained optimization problems. The sharp bound on the weight parameter is given for which constrained and unconstrained problems are equivalent. We also give a computationally efficient...
This paper is concerned with utilizng analog circuits to solve various linear and nonlinear programming problems. The dynamics of these circuits are analyzed. A new nonlinear programming network and its circuit implementation is introduced which utilizes the nonlinearities to eliminate the problems encountered in previous circuit implementations.
This paper is concerned with utilizing analog circuits to solve Various linear and nonlinear programming problems. The dynamics of these circuits are analyzed. A new nonlinear programming network and its circuit implementation is introduced which utilizes the nonlinearities to eliminate the problems encountered in previous circuit implementations.
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