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 bound on the weight parameter. Numerical examples and computer simulations illustrate the results obtained.