Refer to page 36 for solving a bang-bang control problem. Instructions: • Formulate the problem, identifying the control constraints. • • Apply Pontryagin's Maximum Principle to derive the switching conditions. Clearly illustrate the switching points in the control trajectory. Verify the solution satisfies the optimality criteria. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440 AZF/view?usp=sharing]

State the rules used for unconstrained (regular) optimization and for constrained optimization.

How can we interpret non-basic slack variables within an optimal solution? Select one or more: as the amount of uninvested money cannot be interpreted as possible increase in risk as exceeding the minimum requirement to invest in gold as the annual return on the total money invested as the difference between invested money in silver and in platinum

After performing a linear optimization you determine that one of the constraints has a slack value of 135 units. Assuming that 500 units were initially available: What is the shadow/dual price of the constraint? 0 Over what range of values would you expect this shadow price to remain constant? Allowable decrease ? Allowable increase?

Assume the maximum demand for Super is 600,000 barrels. What is the optimal maximum profit?   Hints:  There are six decision variables are as follows:  R1: # of barrels of ingredient 1 used in Regular fuel R2: # of barrels of ingredient 2 used in Regular fuel R3: # of barrels of ingredient 3 used in Regular fuel S1: # of barrels of ingredient 1 used in Super fuel S2: # of barrels of ingredient 2 used in Super fuel S3: # of barrels of ingredient 3 used in Super fuel   Some calculations you would need in the constraints: The total # of barrels for Regular would be R1+R2+R3. The total # of barrels for Super would be S1+S2+S3. The total # of barrels of Ingredient 1 would be R1+S1. The total # of barrels of Ingredient 2 would be R2+S2. The total # of barrels of Ingredient 3 would be R3+S3.

To specify that x1 must be at most 75% of the blend of x1, x2, and x3, what constraint should be included in the optimization model?

I asked this question before, please show the IMAGGE/PICTURE of the graph on demos. It’s 3 variables so it should be 3D shape. A picture of the demos graph and it’s constraint. A photo/IMAGE I asked.

Valencia Products makes automobile radar detectors and assembles two models: LaserStop and SpeedBuster. Both models use the same electronic components. After reviewing the components required and the profit for each model, the firm found the following linear optimization model for profit, where Lis the number of LaserStop models produced and S is the number of SpeedBuster models produced. Implement the linear optimization model on a spreadsheet and use Solver to find an optimal solution. Interpret the optimal solution, identify the binding constraints, and verify the values of the slack variables. Maximize Profit = 125 L + 135 S 17 L + 12 S≤ 4000 5 L +9 S≤ 3500 L≥0 and S≥0 (Availability of component A) (Availability of component B) Implement the linear optimization model and find an optimal solution. Interpret the optimal solution. The optimal solution is to produce LaserStop models and SpeedBuster models. This solution gives the (Type integers or decimals rounded to two decimal places…

How, if at all, do the maximum profit and optimal bottling policy change if the company has no minimum required production?

What is the exact calculator input on geogebra to get the graph? Please construct the graph and the constraints you substituted. Please don’t send the same exact answer as last time