11.For a minimization problem, a point a global minimum if : 1825494
11.For a minimization problem, a point is a global minimum if there are no other feasible points with a smaller objective function value.
12.There are nonlinear applications in which there is a single local optimal solution that is also the global optimal solution.
13.Functions that are convex have a single local maximum that is also the global maximum.
14.The function f (X, Y) = X 2 + Y 2 has a single global minimum and is relatively easy to minimize.
15.The problem of maximizing a concave quadratic function over a linear constraint set is relatively difficult to solve.
16.Each point on the efficient frontier is the maximum possible risk, measured by portfolio variance, for the given return.
17.Any feasible solution to a blending problem with pooled components is feasible to the problem with no pooling.
18.Any feasible solution to a blending problem without pooled components is feasible to the problem with pooled components.
19.When components (or ingredients) in a blending problem must be pooled, the number of feasible solutions is reduced.
20.The value of the coefficient of imitation, q, in the Bass model for forecasting adoption of a new product cannot be negative.
21.The Markowitz mean-variance portfolio model presented in the text is a convex optimization problem.
22.Because most nonlinear optimization codes will terminate with a local optimum, the solution returned by the codes will be the best solution.
23.It is possible for the optimal solution to a nonlinear optimization problem to lie in the interior of the feasible region.