Continuous analogs of the gradient projection metrod in nonlinear programming

A class of gradient descent algorithms is described for the minimization of a differentiable function on Rn subject to nonlinear constraints. The method is based on autonomous systems of differential equations which represent continuous versions of gradient projection and reduced gradient descent. A distinctive feature of the approach is a special provision that corrects for constraint violation by means of a modified Newton method.