Algorithm models for nondifferentiable optimization

It is shown that a number of seemingly unrelated nondifferentiable optimization algorithms are special cases of two simple algorithm models: one for constrained and one for unconstrained optimization. In both of these models, the direction finding procedures use parametrized families of maps which are locally uniformly u.s.c, with respect to the generalized gradients of the functions defining the problem. The selection of the parameter is determined by a rule which is analogous to the one used in methods of feasible directions.