Construct the Efficient Frontier

B. Ralph Armentrout’s optimal stocking quantity is less than Anna Sheen’s in Problem #2 due to

(b) The mean is the arithmetic mean or average of scores for each category (Governors and CEOs). For example, to calculate the mean desk size for governors, I would sum 44, 36, 52, and 40, then divide by the total number of desks measured, thus giving 43. This is the average desk size of governors. The standard deviation represents a

20-12.Refer to the following table for a different consumer, and assume that each week this consumer buys only hot dogs and tickets to baseball games. The price of a hot dog is $2, and the price of a baseball game is $60. If the consumer’s income is $128 per week, what quantity of each item will he purchase each week at a consumer optimum?

3. How does the base case optimal-portfolio Sharpe ratio change, if shorting is not allowed

The __weight__ of a portfolio is simply the percent of an investment portfolio that is held by a single asset. Sounds simple enough right, let 's think of the weight of a portfolio in terms of pizza slices. If you have 8 slices of pizza and the total value of the pizza is 20 dollars. Each slice of pizza will hold a weight of 40% (8/24=.40 or 40%). That calculation is an example of a portfolio weight by value. There are several ways the weight of a portfolio can be determined. The value calculation is the most basic and used method. Other methods include: cost, unit, sectors, and types of securities.

The optimal investment weights for our portfolio are 21.20% in VPMCX, 11.00% in HASCX, 11.40% in VTCIX, 1.80% in RSYEX, and 53.60% in PQNCX.

We have built a template, available upon request, to perform the calculations. We find the value of x that maximizes this function within the interval to be x= 6.733241. So how do we find the y coordinate? We substitute x= 6.733241 into the equation y = -1/2 x + 10 and find y =6.63338. Our Nash arbitration

It is clear that demand is higher than supply. Since demand is greater than supply, the demand constraints will be less than or equal to in the equation. Using solver in Excel, we can complete the spreadsheet. We find that the optimal solution is:

- Constraints: x1 + 3x1x2 + 2x2, ≤ 10 restrictions on the values to decision variables

Plugging in the following percentage, 25%, 30%, 33% and 40% respectively into the formula which is Max profit margin= FX%+C. The x presents the profit margin. Then the results come out by solver.

K distinct contracts are randomly chosen from the portfolio as an initial centroid, which is μ ⃑_1^((0)),μ ⃑_2^((0)),μ ⃑_3^((0)),…,μ ⃑_k^((0)).

Step 6: If cost shows initially low and after that high, (concavity fails for K ) go to step 28 Step 7: i = 0 to 20 do: Rw  2  i /10

Finding out the value for each matched case with respect to the given input requirements

Since there are only two variables , we can solve this problem using the Graphical Method by graphing the set of points in the plane that satisfies the constraint set and then finding which point of this set maximizes the value of the objective function . Each inequality constraint is satisfied by a half-plane of

Economic dispatch(ED) is basically an optimization problem for economic scheduling of power generating units to meet the forecasted load demand while satisfying all operational constraints [1]. As practical ED is a complex constrained optimization problem, its solution requires robust optimization methods. An extensive study on has been carried out by researchers on small /medium/ large dimension problems related with single area till date [2]-[4]. The ED problem aims to determine the optimum powers for the generating units so that the generating cost for the entire system is minimum, when the power balance conditions and the generating units restrictions are met.