JOHN ROWSE
Nonlinear programming (NLP) offers many advantages for formulating and solving discrete-time nonlinear natural resource models. Not all natural resource analysts are aware of these advantages, however, and in this paper NLP approaches are discussed for a class of renewable resource models recently addressed in the literature using a current-period decision rule approximation method, a class of nonrenewable resource models recently solved by first identifying the terminal extraction period, and related nonrenewable resource models. NLP can solve dynamic models ranging in size from small pedagogically-oriented theoretical models to large-scale policy evaluation models exhibiting many real-world complications.