Tricky Optimization Problem

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In summary, the conversation discusses finding the optimal height for a light to be suspended above the floor in order to maximize the illumination at a given point. This is achieved by using the Pythagorean theorem and the definition of cosine to derive a formula for illumination in terms of the height of the light. The formula is then differentiated and set equal to 0 to find the optimal height.
  • #1
maseratigt89
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can someone PLEASE help me wit this problem? i will be ETERNALLY GRATEFUL. THANK YOU!.

A light is suspended at a height "h" above the floor. The illumination at the point P is inversely proportional to the square of the distance from the point P to the light ("r") and directly proportional to the cosine of the angle theta. How far from the floor should the light be to maximize the illumination at the point P?

light
|\ o=theta
|o \
| \
h| \ r
| \
| \
|__10M__\
floor P
 
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  • #2
light
|\ o=theta
|o \
| \
h | \ r
| \
| \
|__10M__\
floor P
 
  • #3
thats supposed to be a triangle by the way. sry.
 
  • #4
I assume that theta is set so that the light is shining directly at point p.

Okay, let "h" be the height of the light- which is, after all, what you want to find. Use the Pythagorean theorem to determine r, the straight line distance from the light to P, in terms of h. Use that, together with the definition of cosine, to find cos(theta) in terms of h. Since " The illumination at the point P is inversely proportional to the square of the distance from the point P to the light ("r") and directly proportional to the cosine of the angle theta" you can now write a formula for illumination entirely in terms of h. Differentiate that with respect to h and set equal to 0.
 

1. What is the concept of a "Tricky Optimization Problem"?

A tricky optimization problem is a type of mathematical or computational problem that requires finding the best solution among a large number of possible solutions. These problems are often complex and require careful analysis and strategic thinking to find the optimal solution.

2. What makes a problem "tricky" in terms of optimization?

A problem is considered tricky in terms of optimization when it presents multiple constraints, variables, and objectives that need to be simultaneously optimized. These problems often have no clear solution and require creative thinking and advanced techniques to find the best possible outcome.

3. What are some common strategies for solving tricky optimization problems?

Some common strategies for solving tricky optimization problems include formulating the problem as a mathematical model, using algorithms and heuristics to search for the optimal solution, and breaking down the problem into smaller, more manageable sub-problems.

4. How does optimization play a role in scientific research?

Optimization is a fundamental tool in scientific research as it helps scientists find the best possible solution to a problem. It is used in various fields such as engineering, physics, biology, and economics to optimize processes, design experiments, and make predictions.

5. What are some real-world applications of tricky optimization problems?

Tricky optimization problems have a wide range of real-world applications, including airline scheduling, portfolio optimization, network design, and resource allocation in industries such as transportation, finance, and telecommunications. They are also used in machine learning and artificial intelligence to optimize algorithms and improve performance.

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