Solving Randomized Algorithm Problems: Exploring Math Behind it

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In summary, the conversation discusses a problem involving a bag of shapes, five containers, and a person collecting triangles. The problem is a randomized algorithm and can be approached using a Markov chain. The goal is to determine the probability of no longer being able to collect triangles after a certain number of steps. Additional research can be done on randomized algorithms and genetic algorithms to gain a better understanding of the problem.
  • #1
Fellowroot
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I find this problem pretty interesting and I'd like to know more about it and what topic of math it comes from.

Amy and Chris stand opposite from each other at a table. Amy has a bag filled with certain shapes and these shapes are squares, circles and triangles. On the table there are 5 separate containers which at max can only hold a single shaped object. At the start of this process all containers are empty. Amy then randomly selects objects from her bag and then places them into the containers. The entire process is measured with steps where each step represents an object being placed into a container. Chirs likes collecting triangles and when a triangle appears in a container he immediately grabs it and removes it. What is the probability that Chirs will no longer be able to collect any more triangles after X amount of steps have passed?

I asked my professor about this and he said that it was a randomized algorithm problem. Could anyone possibly tell me more about this problem and how to go about solving it?

Thanks.
 
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  • #3
This sounds to me like a stochastic process called a Markov chain. A Markov chain has a fixed state transition matrix whose elements are the probabilities of transitioning from anyone state to another. The question is when the game you describe will end with non-triangles in all 5 containers. The states are the shapes in the containers. The transition matrix elements are the probabilities that, starting in one state, the next draw by Amy will put a certain shape into a certain container and transition to another state. The transition matrix can be analysed to determine the probabilities of terminating in a state with all containers containing a non-triangle.
 

1. What is a randomized algorithm?

A randomized algorithm is a type of algorithm that uses a random number or process to make decisions or solve problems. It introduces an element of randomness into the computation, which can sometimes lead to more efficient or accurate solutions.

2. How are randomized algorithms used in problem solving?

Randomized algorithms are commonly used in problem solving when the input data is too large or complex to be solved using traditional deterministic algorithms. They can also be used to find approximate solutions to NP-hard problems.

3. What is the math behind randomized algorithms?

The math behind randomized algorithms involves probability theory and statistical analysis. These algorithms use randomness to reduce the time or space complexity of a problem, and the math helps to determine the probability of the algorithm producing the correct solution.

4. What are the benefits of using randomized algorithms?

There are several benefits to using randomized algorithms. They can often provide faster solutions to problems compared to deterministic algorithms, especially for large or complex data sets. They can also be used to find approximate solutions to difficult problems, and can sometimes be easier to implement and analyze.

5. Are there any drawbacks or limitations to using randomized algorithms?

While randomized algorithms can provide efficient solutions in many cases, they also have some limitations. The randomness in these algorithms can sometimes make it difficult to guarantee the correctness of the solution. They can also be more complex to analyze and implement compared to deterministic algorithms.

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