Optimizing with derivatives I think

  • Thread starter michaeldw
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In summary, when trying to minimize cost for retrieving a bit on the moon, the optimal launch angle is a few degrees below 70 degrees. This is because it is expensive to adjust the launch angle, and the roover must also return to the base. The first derivative of the equation with the absolute value function must be taken and set equal to zero in order to find the optimal angle, taking into account the conversion from degrees to radians. The most efficient launch angle will be below 70 degrees.
  • #1
michaeldw
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Optimizing with derivatives Question

Homework Statement


You're on the moon and you need a bit from the base 900 feet away so you can continue your experiment. The base has a roover/launcher used for retrieving the bit. The roover can move a max dist of 600 feet. The launcher is controlled by angle. At this time the angle is inclined at 70 degrees. It cost $375 per foot to move the roover, and $20,000 per degree to change the launch angle. What are the optimal settings (what angle should I launch from where) to minimize cost?

acceleration due to grav on moon = 5.31 ft/s^2
initial velocity of bit leaving launcher = 75 ft/s

Homework Equations



R = V^2(sin(2x))/g ; where R = total distance, V= Velocity, x= theta (some angle) and g= gravitational acceleration.

The Attempt at a Solution


I made an equation for constraint and pne for the optimaization.

Constraint:
900 = [[(75^2)*sin(2x)]/5.31]+ y ; where y = distance roover moves and x = theta

optimization
minCost = [20000*|70-x|]+2y(375) ; |...| = absolute value, 2y because the roover has to return to base.

I remember doing problems like this in HS but I need help with this one. I solved for y in the constraint equation, then substiituted it in for y in the optimization equation. Here's what I got:

minCost= [-7.945*(10^5)*sin(2x)+675000+20000*|x-70|]

Now if I am correct I get the 1st derivative of this and I will get the extrema or something like that which will be the optimal angle. Then I plug that into the first equation and get y...

But how do I find the first derivative. This might not even be the right method so please help thatnks.
 
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  • #2
anyone?
 
  • #3
You are probably having a hard time getting answers because the problem is nasty and not terribly interesting. I think I agree with your minCost function. Unfortunately, life is not so easy as to just take a derivative and set equal to zero. For one thing you have the absolute value function which makes the form different for x>70 and x<70. Write down these two forms, take derivative and set equal to zero (be careful since you are working in degrees - if x is in degrees, sin(2x) should be sin(2*(pi/180)*x). For the record I get an answer just a few degrees below 70 degrees (as you might expect, since it is so expensive to adjust the angle).
 
  • #4
i understand what you are saying. how do I set up the two forms though? my math skills are not that great. and is d/dx sin(2x) = to cos(2x)?
 
  • #5
d/dx sin(k*x)=k*cos(k*x) (that's the chain rule). But that assumes k*x is in radians. You are working in degrees, don't forget the conversion. And for the two forms |x-70| is (x-70) if x>70 and (70-x) if x<70.
 
  • #6
is this the correct derivative:
(if x > 70)
d/dx= 20000+7.945^5(cos(x(pi/90)))(pi/90)
 
  • #7
Yes, I'll go along with that. You'll find x>70 is not very interesting though. Above 70 y increases AND (70-x) increases. x<70 is the place to be.
 
  • #8
yAY completed and turned in. Thanks for All your help! this is an awesme forum.
 

What is meant by "optimizing with derivatives"?

"Optimizing with derivatives" refers to using mathematical concepts known as derivatives to find the maximum or minimum value of a function. This is often used in scientific and engineering fields to optimize the performance of a system or process.

Why is optimizing with derivatives important in scientific research?

Optimizing with derivatives allows scientists to find the most efficient or effective solution to a problem. By finding the maximum or minimum value of a function, scientists can improve the performance of a system or process and achieve their research goals more effectively.

What are some common applications of optimizing with derivatives in science?

Optimizing with derivatives is commonly used in fields such as physics, engineering, economics, and computer science. Examples of applications include finding the most efficient route for a delivery truck, optimizing the design of a bridge, or maximizing the profit of a company.

What types of derivatives are commonly used in optimization?

The two most commonly used types of derivatives in optimization are the derivative of a function with respect to a single variable (known as the first derivative) and the derivative of a function with respect to multiple variables (known as the gradient). These derivatives can be used to find the maximum or minimum value of a function.

Are there any limitations to using derivatives for optimization?

While optimizing with derivatives can be a powerful tool, there are some limitations to consider. Derivatives may not always exist for a given function, and even if they do, they may not always provide a global maximum or minimum. It is important for scientists to carefully consider their problem and choose the appropriate optimization method.

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