Exam 8: Nonlinear Optimization Models

Functions that are convex have a single local maximum that is also the global maximum.

Nonlinear programming algorithms are more complex than linear programming algorithms.

The interpretation of the dual price for nonlinear models is different than the interpretation of the dual price for linear models.

The measure of risk most often associated with the Markowitz portfolio model is the

In the case of functions with multiple local optima, most nonlinear optimization software methods can get stuck and terminate at a local optimum.

For a minimization problem, a point is a global minimum if there are no other feasible points with a smaller objective function value.

There are nonlinear applications in which there is a single local optimal solution that is also the global optimal solution.

The value of the coefficient of imitation, q, in the Bass model for forecasting adoption of a new product cannot be negative.

The Markowitz mean-variance portfolio model presented in the text is a convex optimization problem.

Each point on the efficient frontier is the maximum possible risk, measured by portfolio variance, for the given return.

A feasible solution is a global optimum if there are no other feasible points with a better objective function value in the feasible region.

For a typical nonlinear problem, duals price are relatively insensitive to small changes in right-hand side values.

In the Bass model for forecasting the adoption of a new product, the objective function