Non-smooth optimization problems

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Reference no: EM132256272

1. In the EOQ model, the decision variable is:

a. The holding cost fraction, i.

b. The setup cost, S.

c. The order quantity, Q.

d. The demand, D.

2. Non-smooth optimization problems:

a. Often contain EXCEL functions like: IF( ), MAX( ), MIN( ), CHOOSE( ), or LOOKUP( ).

b. Have derivatives that are not continuous.

c. Produce the solutions are “good” but they are not guaranteed to be globally or even locally optimal.

d. All of the above.

3. Typical financial spreadsheet calculations are:

a. Too complex to comfortably apply optimization techniques.

b. Inherently nonlinear See “financial spreadsheet models” in chapter 8 for more information.

c. Easy, so no optimization techniques are needed to make sound financial decisions.

d. Not structured properly to apply optimization techniques.

4. One of famous applications of NLP in finance involves:

a. Maintaining a constant investment risk level.

b. Maximizing the level of return.

c. Determining the optimal mix of investments to hold in a portfolio in order to minimize the risk of the portfolio while achieving a desired level of return.

d. Minimizing the amount of money invested.

5. The term ‘efficient investment’ in a financial portfolio theory means that:

a. No portfolio exists that produces a higher expected return with less risk than the portfolio currently held.

b. There exist portfolios with a higher risk compared to the portfolio currently held.

c. There exist portfolios with a higher expected return compared to the portfolio currently held.

d. There exist portfolios with a lower risk compared to the portfolio currently held.

6. A graph of an efficient frontier:

a. Identifies all local minima

b. Plots the minimal portfolio risk associated with each possible level of return.

c. Identifies only global minimum

d. Enables automating investment portfolio decisions

7. The two conflicting objectives in investment portfolio management involve:

a. Minimizing risk and maximizing expected return.

b. Minimizing amount invested and maximizing expected return.

c. Maximizing amount invested and minimizing risk.

d. Maximizing risk and disregarding expected return.

8. In nonlinear optimization the Lagrange multipliers can be used to:

a. Accurately calculate the impact on the objective function of changing a constraint’s RHS value.

b. Estimate the approximate impact on the objective function of changing a constraint’s RHS value by small amounts.

c. Estimate the impact of changing the objective function coefficient by a small amount on the constraint RHS.

d. Predict the impact on the objective function of changing a constraint’s RHS value by large amounts.

Reference no: EM132256272

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