Chapter 7 – Nonlinear Optimization Models
1. Which of the following is not one of the reasons an optimization model can become nonlinear?
Nonconstant returns to scale.
The model objective is to minimize the sum of squared differences.
The model objective is a function of a variable times multiplied by a function of that same variable
The model objective is to minimize the sum of absolute errors.
2. Which of the following is not one of the common types of nonlinear models?
Advertising and response selection models.
Portfolio optimization models.
3. A function is concave if:
its slope is always nondecreasing
a line drawn connecting two points on the curve never lies below the curve
its slope is always nonincreasing
4. If a convex function is multiplied by a negative constant, the result:
could be either convex or concave, depending on the value of the constant
5. Which of the following is not one of the conditions for maximization in a nonlinear problem?
The objective function is concave
The logarithm of the objective function is concave
The starting values are bounded
The constraints are linear
6. If you try different starting values for the changing cells and obtain different solutions:
you can be confident there is no solution
there must be a mistake in your objective function
there must be a mistake in one of your constraints
you can keep the best solution you have found and hope that it is indeed optimal